Numerical Modeling of Floating Oil Boom Motions in Wave-Current Coupling Conditions

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英语翻译

英语翻译

金属聚氨酯,聚氨酯-脲和聚氨酯-醚综述摘要由于聚氨酯具有优良的耐磨性,弹性和塑性,它在工程材料中变得越来越重要。

随着聚氨酯科学与技术的发展,促进了具有更多的令人满意的性能的新材料的发展。

这种材料包括含金属的聚氨酯类,聚氨酯-脲和聚氨酯-醚,它们有异氰酸酯的结构单元,并且增强了热稳定性,阻燃性,柔软性和溶解性。

合成含金属的聚氨酯最初的原料用含金属离子的二醇盐,此时金属牢固的融入了聚合物的主链中。

把金属引入聚氨酯中使得其具有广泛的应用,比如在水性增稠剂、浸渍剂、纺织施胶剂、粘合剂、添加剂、树脂和催化剂上的应用。

本文主要介绍了制备包含金属的聚氨酯各种合成方法,以及其共聚物性能。

1.引言聚氨酯代表了热塑性和热固性的一类重要聚合物,是因为通过不同的多元醇与多异氰酸酯反应调整其机械性能、热的和化学性能。

聚氨酯含有大量的氨基甲酸酯(-HN-COO-),不论其他部分是什么。

通常,通过多异氰酸酯与含有至少两个羟基的反应物如聚醚、蓖麻油和简单的二醇,两者组合来获得PU。

其他活性基像氨基和羧基也可能存在。

因此,这种典型的聚氨酯除了含有氨基甲酸酯基团外,还有酯、醚、酰胺和脲基。

从二醇(HO-R-OH)和二异氰酸酯(OCN-R’-NCO)的结构可以得出线性聚氨酯的一般结构如下:PU的性能根据其需求在许多方面都不同。

羟基化合物和异氰酸酯基的功能基增加到三个或者更多,以此来形成支链或者交联聚合物。

还可以通过R(聚醚、聚酯和乙二醇)的性质做其他结构的改变,也可以通过改变R'的分子量和类型改变R'。

由于这些原因,PU的交联性和链柔软性和其分子间力可以广泛而且独立的变化。

一般来讲,由于P中U通过聚多元醇的软链段和氨基甲酸酯的极性官能团增强的弹性性能,使PU呈现出良好的粘合性能。

这些材料被广泛的应用于涂料、泡沫材料、和不同的塑料和弹性体。

金属聚氨酯代表了把无机盐连接到聚合物链上的一个广泛聚合物的分类。

一般,当他们有充足的离子电荷在水中溶解时,我们把它称为“聚合物电解质”,如果含有离子基的浓度太低以至于不能水解时,我们称之为“离子聚合物”。

细胞生物学细胞膜结构及膜骨架(ppt)

细胞生物学细胞膜结构及膜骨架(ppt)
脂质
胆固醇和中性脂质
➢ 主要存在真核细胞膜上,含量一般 不超过膜脂的1/3,植物细胞膜中 含量较少。
➢ 胆固醇主要提高双脂层的力学稳定 性,调节双脂层流动性,降低水溶 性物质的通透性。
脂质
脂质体(liposome) 是一种人工膜。 在水中,搅动后磷脂形成双层脂分子的球
形脂质体,直径25~1000nm不等。
脂质
糖脂
➢含糖而不含磷酸的脂类,含量约占脂总量 的5%以下,在神经细胞膜上糖脂含量较高, 约占5-10%。 ➢糖脂也是两性分子,其结构与SM很相似。
糖脂决定血细胞表面抗原
Glu: Glucose Gal:Galactose GlcNAc:Nacetyglucosamine GalNAc:Nacetygalactosamine Fuc:Fucose
特点
膜流动的意义
质膜的流动性是保证其正常功能的必要条件。 例如跨膜物质运输、细胞信息传递、细胞识 别、细胞免疫、细胞分化以及激素的作用等 等都与膜的流动性密切相关。当膜的流动性 低于一定的阈值时,许多酶的活动和跨膜运 输将停止,反之如果流动性过高,又会造成 膜的溶解。
如何证明是自发的热运动?
同等条件下,不同的膜蛋白 (如在脂质体中)恢复呈不同 的趋势,为什么?
影响膜蛋白流动的因素的实验研究 单分子追踪(single-particle tracking,SPT)
膜蛋白流动的影响因素
➢ 细胞质膜下的骨架结构与膜整合蛋白结合限 制膜蛋白移动 ➢ 细胞外基质中的某些分子与膜整合蛋白结合 限制了膜蛋白的移动 ➢ 膜蛋白与另一细胞的膜蛋白作用限制了自身 的移动 ➢ 膜中其他不动蛋白限制了膜蛋白的移动
特点
膜脂分子的运动
侧向扩散:同一平面上相邻脂分子交换位置。 旋转:围绕与膜平面垂直的轴进行快速旋转。 摆动:围绕与膜平面垂直的轴进行左右摆动。 伸缩震荡:脂肪酸链进行伸缩震荡运动。 翻转:膜脂分子从脂双层一层翻转到另一层。 旋转异构化:脂肪酸链围绕C-C键旋转。

Mathematical modeling of drying of pretreated

Mathematical modeling of drying of pretreated

Mathematical modeling of drying of pretreated and untreated pumpkinT.Y.Tunde-Akintunde &G.O.OgunlakinRevised:13February 2011/Accepted:26April 2011#Association of Food Scientists &Technologists (India)2011Abstract In this study,drying characteristics of pretreated and untreated pumpkin were examined in a hot-air dryer at air temperatures within a range of 40–80°C and a constant air velocity of 1.5m/s.The drying was observed to be in the falling-rate drying period and thus liquid diffusion is the main mechanism of moisture movement from the internal regions to the product surface.The experimental drying data for the pumpkin fruits were used to fit Exponential,General exponential,Logarithmic,Page,Midilli-Kucuk and Parabolic model and the statistical validity of models tested were determined by non-linear regression analysis.The Parabolic model had the highest R 2and lowest χ2and RMSE values.This indicates that the Parabolic model is appropriate to describe the dehydration behavior for the pumpkin.Keywords Pumpkin fruits .Hot air drying .Effective diffusivity .Mathematical modellingNomenclature a Drying constant b Drying constant c Drying constant DR Drying rate (g water/g dry matter*h)k Drying constant,1/min M e Equilibrium moisture content(kg water/kg dry matter)M i Initial moisture content (kg water/kg dry matter)M R Dimensionless moisture ratioMR exp,i Experimental dimensionless moisture ratio MR pre,i Predicted dimensionless moisture ratio M t Moisture content at any time of drying (kg water/kg dry matter)M t +dtMoisture content at t +dt (kg water/kg dry matter)N Number of observationsn Drying constant,positive integer R 2Coefficient of determination t Time (min)W Amount of evaporated water (g)W 0Initial weight of sample (g)W 1Sample dry matter mass (g)z Number of constants χ2Reduced chi-squareIntroductionPumpkin (cucurbita mixta )is a fruit rich in Vitamin A,potassium,fiber and carbohydrates.It is a versatile fruit that can be used for either animal feed or for human consumption as a snack,or made into soups,pies and bread.The high moisture content of the fruit makes it susceptible to deterioration after harvest.The most common form of preservation being done locally is drying.It is a means of improving storability by increasing shelf-life of the food product.Dried products can be stored for months or even years without appreciable loss of nutrients.Drying also assist in reducing post harvest losses of fruits and vegetables especially which can be as high as 70%(Tunde-Akintunde and Akintunde 1996).Sun drying is the common method of drying in the tropical region.However the process is weather depen-T.Y .Tunde-Akintunde (*):G.O.Ogunlakin Department of Food Science and Engineering,Ladoke Akintola University of Technology,PMB 4000,Ogbomoso,Oyo State,Nigeria e-mail:toyositunde@J Food Sci TechnolDOI 10.1007/s13197-011-0392-2dent Also,the drying of food products with the use of sun-drying usually takes a long time thus resulting in products of low quality.There is a need for suitable alternatives in order to improve product quality.Hot air dryers give far more hygienic products and provide uniform and rapid drying which is more suitable for the food drying processes(Kingsly et al.2007a;Doymaz 2004a).Many types of hot-air dryers are being used for drying agricultural products.However the design of such driers for high-moisture foods constitutes a very complex problem owing to the characteristics of vegetable tissues.One of the most important factor that needs to be considered in the design of driers is the proper prediction of drying rate for shrinking particles and hence the appropriate drying time in the dryer needs to be investigated.This can be achieved by determination of the drying characteristics of the food material.Therefore the thin layer drying studies which normally form the basis of understanding the drying characteristics of food materials has to be carried out for each food material.Studies have been carried out on thin layer drying of some food materials(Gaston et al.2004), fruits(Doymaz2004a,c;Simal et al.2005),leaves and grasses(Demir et al.2004).Though there has been some literature on drying of pumpkin(Alibas2007;Doymaz2007b;Sacilik2007),the selection of appropriate model to describe the drying process for the variety common in the country and within the experimental conditions considered in this study is yet to be done.Thin layer drying models used in the analysis of drying characteristics are usually theoretical,semi-theoretical or purely empirical.A number of semi-theoretical drying models have been widely used by various researchers(Sharma and Prasad2004;Simal et al.2005;Sogi et al.2003;Togrul and Pehlivan2004).A number of pre-treatments can be applied depending on the food to be dried,its end use,and availability(Doymaz 2010).Pretreatment of food materials which includes; blanching,osmotic dehydration,soaking in ascorbic acid before or on drying have been investigated to prevent the loss of colour by inactivating enzymes and relaxing tissue structure.This improves the effect of drying by reducing the drying time and gives the eventual dried products of good nutritional quality(Kingsly et al.2007b;Doymaz 2010).Various commercially used pretreatments include potassium and sodium hydroxide,potassium meta bisul-phate,potassium carbonate,methyl and ethyl ester emul-sions,ascorbic and citric acids,(Kingsly et al.2007a,b; Doymaz2004a,b;El-Beltagy et al.2007).However non-chemical forms of pretreatment are generally preferred especially among small-scale processors in the tropics. Blanching as a pre-treatment is used to arrest some physiological processes before drying vegetables and fruits.It is a heat pre-treatment that inactivates the enzymes responsible for commercially unacceptable darkening and off-flavours.Blanching of fruits and vegetables is generally carried out by heating them with steam or hot water(Tembo et al.2008).However other forms of blanching i.e.oil-water blanching have been used by Akanbi et al.(2006)in a previous study for pretreating chilli.These forms of blanching were observed to have an effect on the drying rate and quality of the dried chili. However oil-water blanching pretreatments have not been studied for pumpkin.The aim of this study was:(a)to study the effect of the different blanching pre-treatments on the drying times and rate,and(b)to fit the experimental data to seven mathematical models available in the literature.Materials and methodsExperimental procedureFresh pumpkin fruit were purchased from Arada,a local market in Ogbomoso,Nigeria.The initial average moisture content of fresh pumpkin samples was deter-mined by oven drying method(AOAC1990),and it was found to be91.7%(wet basis)or10.90(g water/g dry matter).The samples were washed and peeled after which the seeds were removed.The pumpkin was then sliced into pieces of5mm×5mm dimensions.The blanching pre-treatments for inactivation of enzymes are as indicated below while untreated samples(UT)were used as the control.i)Samples submerged in boiling water for3minutes andcooling immediately in tap water-(WB)ii)Samples steamed over boiling water in a water bath (WBH14/F2,England)for3minutes and cooled immediately in tap water-(SB)iii)Samples dipped for3minutes in a homogenized mixture of oil and water of ratio1:20(v/v)with0.1g of butylated hydroxyl anisole(BHA)heated to95°C-(O/W B)In all the pretreatments the excess water was removed by blotting the pretreated pumpkin samples with tissue paper.The moisture contents after pretreatment were12.69(g water/g dry matter)for water and oil-water blanching and11.5(g water/g dry matter)for steam blanching.Drying procedureThe drying experiments of pumpkin samples were carried out in a hot-air dryer(Gallenkamp,UK)having three tiers of trays. Perforated trays having an area of approximately0.2m2wereJ Food Sci Technolplaced on each tier and the trays were filled with a single layer of the pumpkin samples.The air passes from the heating unit and is heated to the desired temperature and channeled to the drying chamber.The hot air passes from across the surface and perforated bottom of the drying material and the direction of air flow was parallel to the samples.The samples utilised for each experimental condition weighed 200±2g.Drying of the pumpkin was carried out at drying temperatures of 40to 800C with 20°C increment,and a constant air velocity of 1.5m/s for all circumstances.The dryer was adjusted to be selected temperature for about half an hour before the start of experiment to achieve the steady state conditions.Weight loss of samples was measured at various time intervals,ranging from 30min at the beginning of the drying to 120min during the last stages of the drying process by means of a digital balance (PH Mettler)with an accuracy of ±0.01g.The samples were taken out of the dryer and weighed during each of the time intervals.Drying was stopped when constant weight was reached with three consecutive readings.The experiments were repeated twice and the average of the moisture ratio at each value was used for drawing the drying curves.Model nameModelReferencesExponential modelMR ¼exp Àkt ðÞEl-Beltagy et al.(2007)Generalized exponential model MR ¼Aexp Àkt ðÞShittu and Raji (2008)Logarithmic model MR ¼aexp ðÀkt Þþc Akpinar and Bicer (2008)(Page ’s model)MR ¼exp Àkt n ðÞSingh et al.(2008)Midilli –Kucuk model MR ¼aexp Àkt n ðÞþbt Midilli and Kucuk (2003)Parabolic modelMR ¼a þbt þct 2Sharma and Prasad (2004),Doymaz (2010)Table 1Mathematical models fitted to pretreated pumpkin drying curves100200300400Drying Time (min)WB SBO/W B UT0.10.20.30.40.50.60.70.80.910100200300Drying Time (min)M o i s t u r e R a t i o00.10.20.30.40.50.60.70.80.91M o i s t u r e R a t i oWB SB O/W B UT00.10.20.30.40.50.60.70.80.91M o i s t u r e R a t i o100200300Drying Time (min)WB SB O/W B UTbacFig.1Drying curve of pump-kin slices dried at (a )40,(b )60and (c )80°C (WB-water blanched,SB-steam blanched,O/W B-oil water blanched,UT-untreated).Each observation is a mean of two replicate experiments (n =2)J Food Sci TechnolMathematical modelingThe moisture content at any time of drying (kg water/kg dry matter),M t was calculated as follows:M t ¼W o ÀW ðÞÀW 1W 1ð1ÞWhere W 0is the initial weight of sample,W is the amount of evaporated water and W 1is the sample dry matter mass.The reduction of moisture ratio with drying time was used to analyse the experimental drying data.The moisture ratio,M R,was calculated as follows:M R ¼M t ÀM eM i ÀM e¼exp ÀKt ðÞð2ÞWhere M t ,M i and M e are moisture content at any time of drying (kg water/kg dry matter),initial moisture content (kg water/kg dry matter)and equilibrium moisture content (kg water/kg dry matter),respectively.The equilibrium moisture contents (EMCs)were deter-mined by drying until no further change in weight wasobserved for the pumpkin samples in each treatment and drying condition (Hii et al.2009)The drying constant,K ,is determined by plotting experimental drying data for each pretreatment and drying temperature in terms of Ln M R against time (t)where M R is moisture ratio.The slope,k,is obtained from the straight line graphs above.The drying rate for the pumpkin slices was calculated as follows:DR ¼M t þdt ÀM tdtð3ÞWhere DR is drying rate,M t +dt is moisture content at t +dt (kg water/kg dry matter),t is time (min).The moisture ratio curves obtained were fitted with six semi-theoretical thin layer-drying models,Exponential,Generalized exponential,Page,Logarithmic,Midilli -Kucuk and Parabolic models (Table 1)in order to describe the drying characteristics of pretreated pumpkin.These linear forms of these models were fitted in the experimental data using regression technique.To evaluate the models,a nonlinear regression procedure was per-formed for six models using SPSS (Statistical Package for00.020.040.060.080.10.12Drying Time (h)D r y i n g r a t e (g w a t e r /g D M .h )WB SB O/W B UT00.020.040.060.080.10.120.140.160.180246Drying time (h)D r y i n g r a t e (g w a t e r /g D M .h )WB SB O/W B UT00.010.020.030.040.050.060246Drying Time (h)D r y i n g R a t e (g w a t e r /g D M .h )WB SB O/W B UTbacFig.2Drying rate curve of pretreated pumpkin dried at (a )40,(b )60and (c )80°C (WB-water blanched,SB-steam blanched,O/W B-oil water blanched,UT-untreated).Each observation is a mean of two replicate experiments (n =2)J Food Sci Technolsocial scientists)11.5.1software package.The correlation coefficient (R 2)was one of the primary criteria to select the best equation to account for variation in the drying curves of dried samples.In addition to R 2,other statistical parameters such as reduced mean square of the deviation (χ2)and root mean square error (RMSE)were used to determine the quality of the fit.The higher the value of R 2and the lower the values of χ2and RMSE were chosen as the criteria for goodness of fit.(Togrul and Pehlivan 2002;Demir et al.2004;Doymaz 2004b ;Goyal et al.2006).The above parameters can be calculated as follows:#2¼PN i ¼1MR ðexp ;i ÞÀMR ðpred ;i ÞÀÁN Àz2ð4ÞRMSE ¼1N XN i ¼1MR pred ;i ÀMR exp ;i ÀÁ2"#12ð5ÞWhere MR exp,i and MR pre,i are experimental and pre-dicted dimensionless moisture ratios,respectively;N is number of observations;z is number of constantsResults and discussion Drying characteristicsThe characteristics drying curves showing the changes in moisture ratio of pretreated pumpkin with time at drying temperatures of 40,60and 80°C are given in Fig.1.Figure 2show the changes in drying rate as a function of drying time at the same temperatures.It is apparent that moisture ratio decreases continuously with drying time.According to the results in Fig.1,the drying air temperature and pre-treatments had a significant effect on the moisture ratio of the pumpkin samples as expected.This is in agreement with the observations of Doymaz (2010)for apple slices.The figures show that the drying time decreased with increase in drying air temperature.Similar results were also reported for food products by earlierTable 2Drying constant (K)values for pretreatment methods and drying temperaturesDrying Temperature (°C)Pretreatment method Steam Blanching Water Blanching Oil-Water Blanching Untreated 400.45290.44580.42260.4029600.62230.61630.60150.5637800.78840.78710.78370.7803Model nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.95180.0058240.071384Exponential Model 0.96550.0051960.062424Logarithmic Model 0.96840.0056210.059272Page Model0.973750.0032790.048397Midilli –Kucuk model 0.863980.0277520.109059Parabolic model 0.99630.0004730.0164460°CGeneralized Exponential Model 0.98740.0013820.035049Exponential Model 0.98820.0013460.032351Logarithmic Model 0.99420.0007850.022878Page Model0.980390.001110.028847Midilli –Kucuk model 0.818570.0145140.085189Parabolic model 0.99410.0004480.01672680°CGeneralized Exponential Model 0.95640.0038620.058588Exponential Model 0.96220.0038610.054796Logarithmic Model 0.97250.0107950.082138Page Model0.95950.0086160.080386Midilli –Kucuk model 0.937020.022370.105759Parabolic model0.98650.0032920.04685Table 3Curve fitting criteria forthe various mathematical models and parameters for pumpkin pre-treated with water blanching and dried at temperatures of 40,60and 80°CJ Food Sci TechnolModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.95930.0048430.065095Exponential Model 0.96890.0042740.056615Logarithmic Model 0.968960.0050650.056262Page Model0.95990.0045870.057237Midilli –Kucuk model 0.818470.0342380.121134Parabolic model 0.99490.0006490.01925260°CGeneralized Exponential Model 0.987370.0013540.034688Exponential Model 0.98760.0014480.033563Logarithmic Model 0.990810.0008640.023994Page Model0.97490.0012870.031073Midilli –Kucuk model 0.982270.0169220.091982Parabolic model 0.99410.0005980.01933680°CGeneralized Exponential Model 0.94360.0054840.069818Exponential Model 0.951870.0108440.060609Logarithmic Model 0.958190.0052010.082325Page Model0.922370.0065670.070179Midilli –Kucuk model 0.955020.0210930.102696Parabolic model0.979590.0047230.058882Table 4Curve fitting criteria for the various mathematical models and parameters for pumpkin pre-treated with steam blanching and dried at temperatures of 40,60and 80°CModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.94540.0067290.076734Exponential Model 0.96030.0059530.066819Logarithmic Model 0.961750.0069530.065922Page Model0.96450.0045960.057295Midilli –Kucuk model 0.838010.0328130.118587Parabolic model 0.99560.0008220.02167260°CGeneralized Exponential Model 0.976650.0017910.039903Exponential Model 0.982710.002020.039642Logarithmic Model 0.986950.0016620.033287Page Model0.974690.0012580.03072Midilli –Kucuk model 0.88840.0083740.064708Parabolic model 0.99280.0004530.01682280°CGeneralized Exponential Model 0.947850.0047940.065279Exponential Model 0.95540.00480.061101Logarithmic Model 0.96250.0120260.086696Page Model0.932230.0106440.089346Midilli –Kucuk model 0.94940.0200940.100234Parabolic model0.983570.0043540.053874Table 5Curve fitting criteria for the various mathematical models and parameters for pumpkin pre-treated with oil-water blanching and dried at temperatures of 40,60and 80°CJ Food Sci TechnolModel nameCoefficient of determination (R 2)Reducedchi-square (χ)Root mean square error (RMSE)40°CGeneralized Exponential Model 0.948760.0058960.071829Exponential Model 0.95850.0056790.065264Logarithmic Model 0.959110.0067350.06488Page Model0.94120.0064750.068007Midilli –Kucuk model 0.78980.0399250.130808Parabolic model 0.985950.0018690.03267860°CGeneralized Exponential Model 0.97350.0024360.046534Exponential Model 0.97490.0028840.047363Logarithmic Model 0.981070.0011850.028035Page Model0.95840.0021470.040127Midilli –Kucuk model 0.76690.0163070.090296Parabolic model 0.990390.0011790.0272280°CGeneralized Exponential Model 0.949980.0045840.063836Exponential Model 0.95720.0047680.060896Logarithmic Model 0.959850.0050460.057999Page Model0.920160.0077380.069542Midilli –Kucuk model 0.95070.0179590.09476Parabolic model0.97750.0042850.056692Table 6Curve fitting criteria for the various mathematical models and parameters for untreated pumpkin and dried at temper-atures of 40,60and 80°CDrying Time (h)M o i s t u r e R a t i oDrying Time (h)M o i s t u r e R a t i oDrying Time (h)M o i s t u r e R a t i obacFig.3Comparison of experi-mental and predicted moisture ratio values using Parabolic model for pretreated pumpkin dried at (a )40,(b )60and (c )80°C (WBE-water blanched experimental,SBE-steam blanched experimental,O/W BE-oil water blanchedexperimental,UTE-untreated experimental,WBP-waterblanched predicted,SBP-steam blanched predicted,O/W BP-oil water blanched predicted,UTP-untreated predicted).Each observation is a mean of two replicate experiments (n =2)J Food Sci Technolresearchers(Sacilik and Elicin2006;Lee and Kim2009; Kumar et al.2010).The drying time required to lower the moisture ratio of water blanched samples to0.034when using an air temperature of40°C(5h)was approximately twice that required at a drying air temperature of80°C (2.5h).This same trend occurred for both untreated and other pretreatment methods.The drying time required to reach moisture ratio of0.018for drying temperature of60°C for samples pretreated with steam blanching was3h while the corresponding values for water blanched,oil-water blanched and control samples were3.5,3.9and4h respectively.The difference in drying times of pre-treated samples with steam blanching was16.7%,30%and33.3%shorter than water blanched,oil-water blanched and control samples,re-spectively.Similar trends were observed at drying temper-atures of40and80°C.All the pretreated samples had higher drying curves than the untreated samples generally(Fig.1).This is an indication of the fact that various forms of blanching pretreatments increase the drying rate for pumpkin samples. This is similar to the observations of Goyal et al.(2008), Doymaz(2007a),Kingsly et al.(2007a),Doymaz(2004b) for apples,tomato,peach slices and mulberry fruits.The difference in drying is more pronounced at the initial stages of drying when the major quantity of water is evaporated while at the latter stages of drying the difference in the amount of water evaporated is not as pronounced as the early drying stages.The difference in the drying of the different pretreatments experienced in the initial stages becomes less pronounced with increase in drying temper-ature.This may be because at higher temperatures the driving force due to diffusion from the internal regions to the surface is higher thus overcoming hindrances to drying more effectively resulting in more uniform drying.The K values for pretreated samples were higher than that of untreated samples for all the drying temperatures(Table2). This confirms the fact that the various forms of blanching pretreatments increased the rate at which drying took place.Analysis of the drying rate curves(Fig.2)showed no constant-rate period indicating that drying occurred during the falling-rate period.Therefore,it can be considered a diffusion-controlled process in which the rate of moisture removal is limited by diffusion of moisture from inside to surface of the product.This is similar to the results reported for various agricultural products such Amasya red apples (Doymaz2010),peach(Kingsly et al.2007a),yam(Sobukola et al.2008),and tumeric(Singh et al.2010).Fitting of drying curveSPSS statistical software package for non-linear regression analysis was used to fit moisture ratio against drying time to determine the constants of the four selected drying models.The R2,χ2and RMSE used to determine the goodness of fit of the models are shown in Tables3,4,5 and6.The Parabolic model gave the highest R2value which varied from0.9963to0.9775for experimental conditions considered in this study.The values ofχ2and RMSE for the Parabolic model which varied from0.000448 to0.004723and0.01644to0.058882respectively were the lowest for all the models considered.From the tables,it is obvious that the Parabolic model therefore represents the drying characteristics of pretreated pumpkin(for individual drying runs)better than the other models(Generalized exponential,Exponential,Logarithmic,Page or Midilli–Kucuk)considered in this study.The comparison between experimental moisture ratios and predicted moisture ratios obtained from the Parabolic model at drying air temperature of40°C,60°C and80°C for pumpkin samples are shown in Fig.3.The suitability of the Parabolic model for describing the pumpkin drying behaviour is further shown by a good conformity between experimental and predicted moisture ratios as seen in Fig.3.This is similar to the observations of Doymaz(2010)for drying of red apple slices at55,65and75°C.ConclusionThe effect of temperature and pre-treatments on thin layer drying of pumpkin in a hot-air dryer was investigated. Increase in drying temperature from40to80°C decreased the drying time from5hours to4hours for all the samples considered.The pretreated samples dried faster than the untreated samples.Samples pretreated with steam blanch-ing had shorter drying times(hence higher drying rates) compared to water blanched,oil-water blanched and control samples The entire drying process occurred in falling rate period and constant rate period was not observed.The suitability of four thin-layer equations to describe the drying behaviour of pumpkin was investigated.The model that had the best fit with highest values of R2and lowest values ofχ2,MBE and RMSE was the Parabolic model. 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修正摩尔库伦模型下的深基坑变形数值分析

修正摩尔库伦模型下的深基坑变形数值分析

第40卷第2期辽宁工程技术大学学报(自然科学版)2021年4月Vol.40 No.2 Journal of Liaoning Technical University(Natural Science)Apr. 2021 胡建林,孙利成,崔宏环,邵博源,王晟华.修正摩尔库伦模型下的深基坑变形数值分析[J].辽宁工程技术大学学报(自然科学版),2021,40(2):134-140.doi:10.11956/j.issn.1008-0562.2021.02.006HU Jianlin,SUN Licheng,CUI Honghuan,SHAO Boyuan,WANG Chenghua.Numerical analysis of deep foundation pit deformation based on modified Mohr Coulomb model[J].Journal of Liaoning Technical University(Natural Science), 2021,40(2):134-140. doi:10.11956/j.issn.1008-0562.2021.02.006修正摩尔库伦模型下的深基坑变形数值分析胡建林1,孙利成1,崔宏环1,邵博源1,王晟华2(1.河北建筑工程学院土木工程学院,河北张家口 075000;2.北旺建设集团有限公司勘察设计室,河北承德 067000)摘要:为研究修正摩尔库伦模型及其参数对于模拟深基坑变形的影响,运用实验和有限元分析的方法,分析摩尔库伦和修正摩尔库伦模型在模拟深基坑排桩支护变形和地表沉降中的不同特点,通过与实际监测结果进行对比分析,得到了修正摩尔库伦模型对于模拟深基坑变形的适用性.研究结果表明:在地表沉降和排桩水平位移预测上,摩尔库伦模型预测沉降值与实测值相差较大;不同参考应力下的修正摩尔库伦模型预测值与实测值的位移规律曲线较为吻合.研究结论揭示了使用不同参考应力下的修正摩尔库伦本构模型进行基坑开挖变形预测更具参考价值.关键词:基坑变形;修正摩尔库伦模型;模量参数;参考应力;实况监测中图分类号:TU 9 文献标志码:A 文章编号:1008-0562(2021)02-0134-07Numerical analysis of deep foundation pit deformation based onmodified Mohr Coulomb modelHU Jianlin1, SUN Licheng1, CUI Honghuan1, SHAO Boyuan1, WANG Chenghua2(1. College of Civil Engineering, Hebei University of Architectural Engineering, Zhangjiakou 075000, China; 2. Survey and Design Office, Beiwang Construction Group Company with Limited Liability,Chengde 067000, China)Abstract: In order to study the modified Mohr Coulomb model and its parameters for simulating the effect of deformation of deep foundation pit, experimental and finite element analysis method are used to analyze the Mohr Coulomb and modified Mohr Coulomb model pile in the simulation of deep foundation pit support different features of deformation and surface subsidence. Through comparative analysis with the actual monitoring results, the applicability of the modified Mohr Coulomb model for simulating the deep foundation pit deformation is obtained. The results show that in the prediction of surface settlement and horizontal displacement of pile row, the settlement value predicted by using Mohr Coulomb model differs greatly from the measured value; the predicted values of modified Mohr Coulomb model under different reference stresses are more consistent with the measured displacement curves. The research results show that the modified Moor Coulomb constitutive model under different reference stresses is more valuable for the excavation deformation prediction.Key words: foundation pit deformation; modified Moore Coulomb model; modulus parameters; reference stress; monitoring of live收稿日期:2020-05-06基金项目:国家自然科学基金(51878242);张家口市科技局科技计划项目(1911035A);河北建筑工程学院创新基金(XB201918)作者简介:胡建林(1986-),男,河北张家口人,硕士,讲师,主要从事岩土工程方面的研究.第2期胡建林,等:修正摩尔库伦模型下的深基坑变形数值分析1350 引言随着中国城镇化的快速发展,土地资源的紧缺,深基坑工程越来越多,而且基坑周边环境也越来越复杂,在基坑的设计和使用过程中变形控制往往成为主要因素,所以对深基坑支护的变形特性进行研究就显得尤为重要.数值分析被认为是一种有效的研究手段.国内外学者[1-4]基于数值分析对深基坑的变形特性展开了大量研究,也取得了一系列的成果.曾超峰[5]、赵秀绍[6]等运用不同数值分析软件进行基坑开挖数值模拟研究,通过分析计算结果与实测数值的关系,得到适用于不同土层的本构模型.研究表明,在数值分析中本构模型的选择对于计算结果影响很大,所以选择可以正确反映土体变形特征的本构模型对数值分析来说至关重要.由于参数少,而且容易获取,在数值分析中本构模型的选择目前还是以采用以莫尔-库伦破坏准则为基础的理想弹塑性模型为主.但是对于岩土材料来说,Mohr-Coulomb破坏准则存在缺陷,开挖卸载过程中土体的回弹模量和压缩模量均采用弹性模量的假设,会致使采用MC模型的计算结果与实测数据相比相差较大,甚至在变形规律上也往往存在较大差异.王卫东[7]等利用PLAXIS软件中的硬化土模型进行深基坑工程的有限元分析,通过试验获得相关参数,模拟得到较好的预测效果,证明该模型在基坑开挖中的适用性,该模型参数的取值与参考围压应力有关,对于数十米深的基坑,不同深度处围压差距较大,模量参数会随着侧限应力的不同而改变,所以参考应力的选择也至关重要.本文依托于实际工程案例,采用可以考虑加载和卸载时弹性模量不同的修正摩尔库伦模型进行有限元分析,模型参数根据室内试验获取,并且分析模型参数采用不同参考围压对计算结果的影响,获得较好模拟结果,为类似工程提供参考.1 深基坑工程概况深基坑工程场地位于河北省张家口市,地处清水河冲洪积扇中下部地貌单元.基坑呈多边形,南北向长约48 m,东西向宽约37 m,开挖深度约为14 m,边壁支护分为一、二、三、四、五区.支护五区与一栋17层高的住宅楼相邻,该住宅楼基础形式为筏板,埋深为 5 m,与基坑边相距5.6 m,该区采用排桩进行支护,桩径为1.0 m,桩间距为1.2 m,桩长为23 m,依托地锚和既有住宅楼的门墩在冠梁处设置了3道预应力锚索.本文选择五区为研究对象,在基坑开挖前进行了监测点的布置,见图 1.在施工过程中,基坑分4步开挖到底,每次挖深分别为3 m、3 m、5 m、3 m,基坑开挖施工过程见图2.图1 基坑平面及监测布置Fig.1 foundation pit plane and monitoring arrangement图2 基坑分析断面(单位:mm)Fig.2 foundation pit analysis section(unit: mm)2 修正摩尔库伦模型采用MIDAS-GTS/NX有限元分析软件,该软件提供了修正摩尔库伦模型(Modified Mohr Coulomb Model,以下简称MMC模型),该模型是对Mohr-Coulomb模型的优化,弹性模量可以根据加载和卸载设置不同的值,故更适用于基坑开挖数值模拟研究.模型具体参数见表1.118841粉土细砂砂砾-23 m第1步开挖第2步开挖第3步开挖第4步开挖59333辽宁工程技术大学学报(自然科学版) 第40卷136 表1 MMC 模型参数 Tab.1 MMC model parameters参数说明参考值/(kN ∙m -1)ref 50E标准三轴试验中的割线模量 试验获取 E 主固结仪加载中的切线模量 试验获取 E三轴试验卸载/重新加载模量试验获取 c ′ 有效黏聚力 试验获取 φ′ 有效内摩擦角试验获取 K 0 正常固结下的侧压力系数1-sin φ′[8] ψ 最终剪胀角 φ′-30° ν 泊松比 工程地质手册[9]R f 破坏比 0.9σref 参考压力由基坑深度确定m 应力水平相关幂指数0.5[10] α 帽盖形状系数 根据K 0得到 β帽盖硬化系数根据E 得到修正摩尔库伦模型中模拟结果的不同受ref 50E 、ref oedE 、refur E 这3个模量影响较大,现将3个模量的关系表示分述如下:对于自然状态土体,一次加载时的应力应变行为是高度非线性的,不同的模量取决于应力水平的不同.因此用参数E 50表示一次加载的应力相关模量[11],代替初始模量E 作为小应变的切线模量. E 50为3ref5050refcot ()cot p mpc E E c σφσφ+⋅=+⋅, (1) 式中,ref50E 是与参考应力σref 相对应的参考割线模量,MPa ;实际的模量取决于较小的主应力σ3,即三轴试验中的有效围压,MPa ,故MMC 模型的参考应力σref 用某一σ3确定值来表示,根据不同的σ3相对应的方程联立,从而准确得出参考割线模量ref50E .见图3,硬化型破坏曲线取15%轴向应变所对应的偏应力值为破坏值q f ,软化型破坏曲线偏应力峰值点为破坏值q f ,1/2破坏值与原点的连线斜率即为参考割线模量ref50E.图3 三轴单次加载试验应力应变Fig.3 stress-strain of triaxial single loading testMMC 模型突出了两种主要的硬化类型,即剪切硬化和压缩硬化.剪切硬化是用来模拟加卸载过程中不可逆应变的主要偏载现象.卸载再加载应力路径采用另一种应力相关模量[11]ur E 为3refur ur ref cot ()cot p m pc E E c σφσφ+⋅=+⋅,(2)式中,ref ur E 为卸载再加载的参考加卸载模量,见图4,两条直线斜率表示峰值应变前后加卸载模量,可以发现,加卸载模量大小不受轴向应变影响,只与参考应力σref 相对应.由于剪切模量被广泛使用,加卸载模量应用较少,所以将剪切模量与加卸载模量相联系.在胡克的弹性理论中,弹性模量E 和剪切模量G 之间的换算公式为21E G υ=+().由于E ur 是一个真正的弹性模量,因此可以写成ur ur ur 21)E G υ=+(,其中G ur 是弹性剪切模量.偏应力q /k P a轴向应变/%图4 三轴加卸载试验应力应变Fig.4 stress-strain of triaxial loading and unloading test压缩硬化是用来模拟在固结仪加载和各向同性加载中由于初次压缩而产生不可逆塑性应变的现象.与基于弹性的模型相比,弹塑性MMC 模型不涉及三轴模量E 50和E oed 之间的固定关系.因此E oed [11]为3refoed oed ref cot ().cot p m pc E E c σφσφ+⋅=+⋅ (3)图5 固结试验p-εFig.5 stress-strain curve of consolidation test如图5,固结试验得到p-ε曲线,将曲线拟合后,求得在每一级轴向载荷下的切线斜率,即为在该级参考应力下的参考切线模量,因为压缩实验是无侧限试验,该级应力参考大主应力,参考应力σref 用某一σ1确定值来表示,根据不用的σ1相对应的方程联立,从而准确得出参考切线模量.0.020.04 0.06 0.08 0.10轴向应变ε50150250350450轴向载荷p /k P a土1 土2土1拟合曲线土2拟合曲线第2期胡建林,等:修正摩尔库伦模型下的深基坑变形数值分析1373 参数获取及模型说明3.1 模拟参数取值分析为获得模拟参数,根据工程要求进行了室内三轴与固结试验.三轴试验采用固结不排水试验,围压设置分别为50 kPa、100 kPa、150 kPa、200 kPa、250 kPa,根据模型参数获取需要同时进行卸载再加载试验.前人在一些深基坑工程中固定参考应力σref 为100 kPa进行参数分析及数值模拟,但是14 m 的深基坑显然选用一个参考应力是不合适的.为了进行更加精确的分析,取0~3 m深的土层的参考应力σref为50 kPa,3~5.5 m深的土层的参考应力σref为100 kPa,5.5~8 m深的土层的参考应力σref为150 kPa,8~11 m深的土层的参考应力σref为200 kPa,11~14 m深的土层的参考应力σref为250 kPa.上述试验在每一种参考应力下求得的参数以及规范建议值见表2、表3.表2 土体物理力学性质指标Tab. 2 physical and mechanical properties of soil地层编号土体名称干密度/(g∙cm-3)含水质量分数/%容重/(kN∙m-3)泊松比ν孔隙比e内摩擦角φ′/(º)粘聚力c′/(kPa)膨胀角ψ/(º)1 粉土 1.80 17.4 180.300.78224.236.62 细砂 1.825 3.2 18.250.260.78 40 103 砂砾 2.00 200.210.7244.8 14.8表3 MMC本构模型参数Tab.3 MMC constitutive model parameters地层编号土体名称基坑深度/m侧压力系数K0幂指数m压缩模量E S/MPa割线模量ref50E/MPa切线模量refoedE/MPa卸荷弹模refurE/MPa 0~3 3.78 5.52 6.49 13.43~5.5 6.6 7.33 8.2 30.21 粉土5.5~8 0.60 0.59.0 9.16 9.3 78.92 细砂8~11 0.36 0.5 10 10.12 10.58 151.53 砂砾 11~14 0.30 0.5 40 40 40 850 3.2 模型说明进行数值模拟材料设定时,围护结构及锚索均按弹性材料考虑,围护桩采用梁单元,锚索采用植入式桁架单元.细砂土几乎没有粘聚力,输入0.3 kN/m2的值以避免分析发生错误.未开挖前应施加土体在自重状态下的初始应力场,并将初始位移置零.由于混凝土和土体的变形模量有很大的差异,为了模拟围护结构与土之间的共同作用,必须在两者之间设置析取接触面单元[12].布置与实际情况相同大小的均布载荷模拟基坑附近的建筑载荷.有限元模型底边与竖向边界都为全约束,为避免约束影响,模拟深基坑水平向长度是基坑深度的7倍,约100 m,竖向深度为基坑深度的3倍,约50 m,有限元模型见图6.图6 有限元模型Fig.6 finite element model将上述求得的岩土参数分别代入MC模型、MMC模型进行数值模拟分析.4 结果分析通过探究深基坑不同深度处设置不同参考应107 m7 m5m辽宁工程技术大学学报(自然科学版) 第40卷138 力的必要性以及MMC 模型的优越性,采用3种方法进行数值分析,分别为单一参考应力下的 MMC 本构模型计算、不同参考应力下的MMC 本构模型计算、MC 本构模型计算,分别将3种计算结果与深基坑实际监测变形位移进行对比分析,得到不同施工步骤下的变形值,见图7、图8.其中MC 表示采用摩尔库伦本构模型模拟基坑开挖的变形情况,MMC 表示采用修正摩尔库伦本构模型在不同参考应力下模拟基坑开挖变形情况,MMC100表示采用修正摩尔库伦本构模型在100 kPa 单一参考应力条件下模拟基坑开挖变形情况,实测表示现场基坑开挖测得的地表沉降和水平位移情况.4.1 地表沉降变形分析图7分别表示为第1、2、3、4步开挖后地表沉降情况.-0.6-0.4-0.200.2距排桩距离/mMC MMC MMC100实测20406080100(a )第1步(b )第2步-8-6-4-20距排桩距离/mMCMMC MMC100实测20406080100(c )第3步 (d )第4步图7 不同施工步骤开挖完成后地表沉降对比Fig.7 comparison of surface settlement under different construction steps由图7可知,在实际开挖过程中,基坑周边地表沉降随着距基坑边壁距离不断增大而呈现类似对勾曲线形式,即沉降先增加后减小最后逐渐趋于零,其中在距基坑边壁10 m 左右地表沉降达最大值.由图7对比发现,基坑周边地表随基坑不断开挖沉降逐渐增大,此过程中MC 模型预测值与实测值相差较大,发生最大沉降处距基坑边壁距离与实测值相差近5 m ,计算沉降影响距离是实测沉降影响距离的2倍左右;但MMC 模型预测沉降与实测值相差不大,且沉降规律较为吻合.详细对比MMC 和MMC100预测沉降结果可知,MMC100在预测开挖前期沉降时要略微小于MMC ,但在开挖后期MMC 和MMC100预测沉降几乎一致,说明低围压下使用100 kPa 较大参考应力是不合适的,依据土压力理论可知,深基坑越深,围压越大,参考应力也随之增大才会与实际情况相符合.总的来说,使用不同参考应力下的MMC 本构模型进行沉降预测更具参考价值. 4.2 排桩变形分析图8分别表示第1、2、3、4步开挖后排桩水平位移情况.第2期 胡建林,等:修正摩尔库伦模型下的深基坑变形数值分析139(a )第1步 (b )第2步(c )第3步 (d )第4步图8 不同施工步骤开挖完成后排桩位移对比Fig.8 comparison of pile displacement in different construction steps由图8可以看出,当每一施工步完成后,排桩水平位移沿桩顶到桩底呈现先增大后减小最后趋于零的趋势,其中在距地面6 m 深左右排桩水平位移达到最大值.由图8对比发现,排桩水平位移随基坑开挖而逐渐增大,此过程中MC 模型计算位移值与实测位移值相差较大,且发生最大水平位移处排桩深度与实际深度不符;但MMC 模型计算位移与实测值相差不大,且位移规律较为符合.详细对比MMC 和MMC100计算值可知, MMC100在计算开挖前期水平位移时要略微小于MMC ,但在开挖后期MMC 和MMC100计算水平位移值几乎一致,说明低围压下使用100 kPa 较大参考应力是不合适的,深基坑越深,围压越大,参考应力也随之增大较能符合实际情况.总的来说,使用不同参考应力下的MMC 本构模型进行水平位移计算更具参考价值.5 结论考虑土体模量和应力相关的影响,理想的弹塑性本构模型(MC 模型)预测结果与实测位移变形存在较大的差距.采用MMC 模型,进行排桩变形和地表沉降的数值模拟,将单一参考应力与不同参考应力下的MMC 本构模型计算的地表沉降和水平位移与深基坑实际监测变形位移进行对比分析,得到以下结论:(1)在地表沉降计算上,基坑周边地表沉降随着距基坑边壁距离不断增大而呈现先增加后减小最后逐渐趋于零的趋势,其中在距基坑边壁 10 m 左右地表沉降达最大值.基坑周边地表随基坑开挖沉降逐渐增大,此过程中MC 模型预测值与实测值相差较大,发生最大沉降处距基坑边壁0.05 0.10 0.15水平位移/mm0.511.5水平位移/mm-25MC MMC MMC100实测MC MMC MMC100 实测MC MMC MMC100实测-5-10-15-20-25深度/m0-5-10-15-20深度/m0水平位移/mm-251230 -5-10-15-20深度/m246水平位移/mmMC MMC MMC100 实测-5-10-15-20-25深度/m辽宁工程技术大学学报(自然科学版)第40卷 140距离与实测值相差近5 m,计算沉降影响距离是实测沉降影响距离的2倍左右,但MMC模型预测沉降与实测值相差不大,且沉降规律较为吻合.(2)在排桩水平位移计算上,排桩水平位移沿桩顶到桩底呈现先增大后减小最后趋于零的趋势,其中在距地面约6 m深基坑位移达到最大值. MC模型计算位移计算值与实测位移值相差较大,且发生最大水平位移处排桩深度与实际深度不符,但MMC模型计算位移与实测值相差不大,且位移规律较为符合.(3)对比MMC和MMC100计算沉降结果可知,MMC100在计算开挖前期沉降位移时要略小于MMC,但在开挖后期MMC和MMC100预测变形几乎一致,说明低围压下使用100 kPa较大参考应力是不合适的,依据土压力理论可知,深基坑越深,围压越大,参考应力也随之增大才会与实际情况相符合.总的来说,使用不同参考应力下的MMC本构模型进行沉降预测更具参考价值.参考文献(References):[1] 李飞,徐劲,张飞,等.渗流作用下深基坑开挖抗隆起破坏数值模拟[J].地下空间与工程学报,2017,13(4):1 088-1 097.LI Fei,XU Jin,ZHANG Fei,et al.Numerical simulation of anti-uplift failure of deep foundation pit excavation under seepage[J].Journal of Underground Space and Engineering,2017,13(4):1 088-1 097. [2] 陈小雨,袁静,胡敏云,等.相邻深大基坑安全距离理论分析与数值模拟[J].地下空间与工程学报,2019,15(5):1 557-1 564,1 572.CHEN Xiaoyu,YUAN Jing,HU Minyun,et al.Theoretical analysis and numerical simulation of safety distance of adjacent deep and large foundation pit[J].Journal of Underground Space and Engineering,2019,15(5):1 557-1 564,1 572.[3] YUAN Changfeng,YU Haojie,YUAN Zijin,et al.Numerical simulationof impact caused by construction of high-rise building upon adjacent tunnels[J].Geotechnical and Geological Engineering, 2019,37(4):3 171-3 181.[4]WANG Jianxiu,FENG Bo,YU Haipeng,et al.Numerical study ofdewatering in a large deep foundation pit[J].Environmental Earth Sciences,2013,69(3):863-872.[5] 曾超峰,薛秀丽,郑刚.软土区基坑预降水引起支护墙侧移的典型参数影响研究[J].岩土力学,2017,38(11):3 295-3 303,3 318.ZENG Chaofeng,XUE Xiuli,ZHENG Gang.Study on the influenceof typical parameters on lateral displacement of retaining wall caused by predewatering of foundation pit in soft soil area[J].Rock and Soil Mechanics,2017,38(11):3 295-3 303,3 318.[6] 赵秀绍,艾成刚,王旭,等.砂性土深基坑开挖与支护变形监测的数值分析[J].河南科技大学学报(自然科学版),2014,35(6):61-65+8.ZHAO Xiushao,AI Chenggang,WANG Xu,et al.Numerical analysis of excavation and support deformation monitoring of deep found-ation pit with sandy soil[J].Journal of Henan University of Science and Technology(Natural Science Edition),2014,35(6):61-65,8.[7] 王卫东,王浩然,徐中华.基坑开挖数值分析中土体硬化模型参数的试验研究[J].岩土力学,2012,33(8):2 283-2 290.WANG Weidong,WANG Haoran,XU Zhonghua.Experimental study on parameters of soil hardening-g model in numerical analysis of foundation pit excavation[J].Rock and Soil Mechanics,2012,33(8):2 283-2 290.[8] 罗耀武,凌道盛,陈云敏,等.环形超深基坑围护结构受力变形特性分析[J].岩土力学,2011,32(02):617-622.LUO Yaowu,LING Daosheng,CHEN Yunmin,et al.Analysis of stress and deformation characteristics of ring super deep foundation pit envelope[J].Rock and Soil Mechanics,2011,32(2):617-622.[9] 《工程地质手册》编写委员会.工程地质手册[M].北京:中国建筑工业出版社,2018.[10] 徐中华,王卫东.敏感环境下基坑数值分析中土体本构模型的选择[J].岩土力学,2010,31(1):258-264,326.XU Zhonghua,WANG Weidong.Selection of soil constitutive model in numerical analysis of foundation pit under sensitive environment[J].Rock and Soil Mechanics,2010,31(1):258-264,326.[11] 李建伟,陈沅江,杜金龙.砂卵石地层基坑开挖土体本构模型辨识研究[J].地下空间与工程学报,2013,9(2):223-228.LI Jianwei,CHEN Yuanjiang,DU jinlong.Identification of soil constitutive model for sand and pebble foundation pit excavation[J].Journal of Underground Space and Engineering, 2013,9(2):223-228.[12] 俞建霖,龚晓南.深基坑工程的空间性状分析[J].岩土工程学报,1999(1):24-28.YU Jianlin,GONG Xiaonan.Spatial character analysis of deep foundation pit engineering[J].Journal of Geotechnical Engineering, 1999(1):24-28.。

00Production of a biopolymer flocculant from Bacillus licheniformis and its flocculation properties

00Production of a biopolymer flocculant from Bacillus licheniformis and its flocculation properties

Production of a biopolymer¯occulant from Bacillus licheniformis andits¯occulation propertiesI.L.Shih a,*,Y.T.Van a,L.C.Yeh a,H.G.Lin a,Y.N.Chang ba Department of Environmental Engineering,Da-Yeh University,112Shan-Jiau Road,Da-Tsuen,Chang-Hwa51505,Taiwan,ROCb Department of Food Engineering,Da-Yeh University,Chang-Hwa51505,Taiwan,ROCReceived14October2000;received in revised form16December2000;accepted22December2000AbstractBacillus licheniformis CCRC12826produced extracellularly an excellent biopolymer¯occulant in a large amount when it was grown aerobically in a culture medium containing citric acid,glutamic acid and glycerol as carbon sources.The biopolymer ¯occulant was an extremely viscous material with a molecular weight over2Â106by gel permeation chromatography.It could be easily puri®ed from the culture medium by ethanol precipitation.It was shown to be a homopolymer of glutamic acid by amino acid analysis and thin layer chromatography and presumed to be poly-glutamic acid(PGA).This bio¯occulant e ciently¯occulated various organic and inorganic suspensions.It¯occulated a suspended kaolin suspension without cations,although its¯occulating activity was synergistically stimulated by the addition of bivalent or trivalent cations Ca2 ,Fe3 and Al3 .However,the synergistic e ects of metal cations were most e ective at neutral pH ranges.The comparison of the¯occulating activity between the present biopolymer and a commercial lower molecular weight product showed that the biopolymer of the present study had much higher activity.The high productivity and versatile applications of PGA make its development as a new biodegradable,harmless, biopolymer¯occulant economical and advantageous.Ó2001Elsevier Science Ltd.All rights reserved.Keywords:Bio¯occulant;Polyglutamic acid;Bacillus licheniformis1.IntroductionFlocculating agents are generally categorized into three major groups,i.e.,inorganic¯occulants:alumini-um sulfate and polyaluminium chloride(PAC);organic synthetic polymer¯occulants:polyacrylamide deriva-tives,polyacrylic acids,and polyethylene imine;natu-rally occurring bio-polymer¯occulants:chitosan,algin, and microbial¯occulants.These¯occulants have been widely used in wastewater treatment and in a wide range of industrial downstream processes(Gutcho,1977;Na-kamura et al.,1976;Kurane et al.,1986).Among these ¯occulants,the organic synthetic polymer¯occulants are most frequently used because they are economical and highly e ective.However,their use often gives rise to environmental and health problems in that some of them are not readily biodegradable and some of their degraded monomers,such as acrylamide,are neuro-toxic and even strong human carcinogens(Vanhorick and Moens,1983;Dear®eld and Abermathy,1988). Thus,the development of a safe biodegradable¯occu-lant that will minimize environmental and health risks is urgently required.In recent years,the use of bio-poly-mers produced by microorganisms has been investigated. These microbial polymers are expected to be useful ¯occulating agents due to their bio-degradability and the harmlessness of their degradation intermediates toward humans and the environment.Several bio¯occ-ulants from di erent microorganisms have been reported recently,they were characterized as proteins,poly-saccharides,and glycoprotein.It has been shown that Rhodococcus erythropolis S-1(Kurane et al.,1986; Takeda et al.,1991),Nocardia amarae YK1(Takeda et al.,1992),and Pacilomyces sp.(Takagi and Kado-waki,1985)produced protein¯occulants.Alcaligenes latus B-18(Kurane and Nohata,1991),Alcaligenes cu-pidus KT201(Toeda and Kurane,1991),and Bacillus sp. DP-152(Suh et al.,1997)produced polysaccharide ¯occulants.The¯occulants produced by Arcuadendron sp.TS-4(Lee et al.,1995),and Arathrobacter sp.(Wang et al.,1995)were glycoproteins.In this study,attention was focused on the microbial¯occulant of BacillusBioresource Technology78(2001)267±272 *Corresponding author.Tel.:+886-4-852-3870;fax:+886-4-852-3870.E-mail address:ils@.tw(I.L.Shih).0960-8524/01/$-see front matterÓ2001Elsevier Science Ltd.All rights reserved. PII:S0960-8524(01)00027-Xlicheniformis.Recently,the use of B.licheniformis CCRC12826for protease production has been investi-gated.It was found that the culture broth obtained was highly viscous when B.licheniformis CCRC12826was cultivated in a medium containing citric acid,glutamic acid and glycerol.Furthermore,the viscous culture broth displayed high¯occulating properties.Therefore, attention turned to characterizing this viscous biopoly-mer,and investigating the factors that a ect its¯occu-lating properties.The results of this investigation are reported herein.2.Methods2.1.MaterialsReagents for cultivation such as nutrient agar(NA), nutrient broth(NB)were purchased from DIFCO Laboratories Michigan,ponents of Medium, glutamic acid,citric acid,glycerol,NH4Cl,MgSO4 7H2O,FeCl36H2O,K2HPO4,and CaCl27H2O were obtained from Sigma Chemicals,USA.Kaolin(Practi-cal Grade)was purchased from Wako Pure Chem.Ind. Ltd.,Osaka,Japan.Activated carbon,cellulose,acid clay and aluminum oxide were from Sigma Chemicals, USA.All other reagents used were of the highest grade available.2.2.Strain and culture conditionsB.licheniformis CCRC12826was obtained from the Culture Collection and Research Center(CCRC)Tai-wan,as a lyophilized powder in a glass ampoule sealed under vacuum.The bacterium was®rst cultured on Nutrient Broth plates(Difco Laboratories)containing agar(15g/l),beef extract(3g/l),peptone(5g/l)to induce spore formation.After cultivation(37°C,pH7.4)over-night,highly mucoid colonies that appeared on the plates were inoculated into5ml of Nutrient Broth (Difco Laboratories)composed of beef extract(3g/l), peptone(1.5g/l),and NaCl(5g/l),pH7.4in a30ml test tube,and then incubated at37°C for48h with shaking at150rpm.After incubation,the culture was inoculated into100ml of Medium(5%v/v)composed of glutamic acid(20g/l),citric acid(12g/l),glycerol(120g/l),NH4Cl (7.0g/l),MgSO47H2O(0.5g/l),FeCl36H2O(0.04g/l), K2HPO4(0.5g/l),and CaCl27H2O(0.15g/l)in a500-ml ¯ask(Leonard et al.,1958).The culture was incubated at37°C,pH6.5with shaking at150rpm.The produc-tion of the bio¯occulant was monitored by measuring the viscosity of the culture broth.At the end of the cultivation,a viscous culture broth was obtained,which was centrifuged(20,000Âg,15min)to separate the cells and tested for¯occulating activities.The viscous mate-rials were further puri®ed by the procedures described below.2.3.Flocculating activity of culture mediumThe¯occulating activities were measured using a previous method with a slight modi®cation,in which kaolin clay was chosen as the suspended solid(Kurane et al.,1986;Yokoi et al.,1995).Kaolin was suspended in distilled water or a bu er solution at a concentration of 5g/l.The kaolin suspension(9.3ml)was added to a test tube,and supplemented with various amounts of culture broth.The mixture was gently mixed and kept standing for5min at room temperature.The formation of visible ¯ocs was observed.The optical density(OD)of the upper phase was measured with a spectrophotometer (UV-260,Hitachi,Japan)at550nm.A control experi-ment without the culture broth was also measured in the same manner.Flocculating activity was calculated according to the following equation(Kurane et al., 1986,1994):Flocculating activity 1=OD550À1= OD550 c;OD550 Optical density of sample at550nm;OD550 c Optical density of control at550nm:2.4.Bio¯occulant puri®cationThe viscous culture broth was diluted with the same volume of distilled water and then centrifuged at 20,000Âg for15min.The supernatant was poured into four volumes of cold ethanol to precipitate the bio-polymer¯occulant.The resultant precipitate was col-lected by centrifugation at25,000Âg for15min and re-dissolved in distilled water.After three such ethanol precipitation steps,the crude biopolymer thus obtained was dialyzed at4°C overnight in de-ionized water and lyophilized to give pure material.2.5.Characterization of¯occulant2.5.1.Molecular weight determinationThe number-average molecular weight(M n)of the ¯occulant was measured by gel permeation chromato-graphy(GPC)using a Hitachi L6200system controller equipped with Shodex KB800series columns(two KB80M,one KB802.5),a refractive index(RI)detector (Bischo ,Model8110),a Waters Model730data module.Dextran-polysaccharide standards obtained from Phenomnex Inc.,USA were used to construct a calibration curve from which molecular weights of ¯occulants were calculated with no further correction. The eluant contained0.3M Na2SO4,0.05%(w/v)NaN3268I.L.Shih et al./Bioresource Technology78(2001)267±272was brought to a pH of4.0using glacial acetic acid,and the¯ow rate was set at1.0ml minÀ1.2.5.2.Amino acid analysis and thin layer chromatography The puri®ed material was hydrolyzed with6N HCl at 110°C for24h in a sealed and evacuated tube,and the amino acid compositions were determined with a Beckman system6300E analyzer.Thin-layer chromato-graphy was performed on a cellulose plate(Merck)with solvent systems of butanol±acetic acid±water(3:1:1,w/ w)and96%ethanol±water(63:37,w/w).Amino acids were detected by spraying with0.2%ninhydrin in ace-tone(Stewart and Young,1984;Yokoi et al.,1995).2.5.3.Viscosity measurementApparent viscosity of cell-free culture¯uid was measured with a Brook®eld Digital Rheometer(model DV-III,USA)equipped with a spindle SC4-34,at dif-ferent shear rates(0.28±56.0sÀ1).2.5.4.Protein and sugar contentsThe total carbohydrate content of the¯occulant was determined by the phenol±sulfuric acid method(Chaplin and Kennedy,1986)and expressed as the glucose equivalent.The protein moiety in the¯occulant mole-cule was determined by the Bradford method(Bradford, 1976)with bovine serum albumin as a standard.Sugar derivatives were investigated by the cabazoic method (Chaplin and Kennedy,1986)for uronic acid,the Friedmann method(Frideman and Haugen,1943)for pyruvic acid,and the hydroxamic acid method (McComb and McCreasy,1957)for acetic acid.2.6.Flocculating activity of pure¯occulant2.6.1.The e ects of¯occulant concentration and molec-ular weight on the¯occulating activityThe puri®ed¯occulant or a lower molecular weight PGA(poly-glutamic acid,Mwt.$1Â105)purchased from Sigma Chemicals was dissolved in distilled water to a ord a¯occulant solution of80mg/l.The kaolin sus-pension(5g/l)9.3ml was added to a test tube,and supplemented with various amounts of¯occulant solu-tion and90mM CaCl2solution(1.0ml).The¯occu-lating activities were then measured and calculated using the procedure described above.2.6.2.Flocculation of various suspended solidsThe¯occulating activities for various organic and inorganic solid suspensions,including activated carbon, cellulose,acid clay and aluminum oxide were also studied.Flocculation tests were carried out as described above with kaolin solution(5g/l)being replaced by the various solid suspensions(5g/l)as indicated.The solid suspension(9.3ml)was treated with0.5ml of¯occulant solution(80mg/l)and90mM CaCl2solution(1.0ml)and the¯occulating activities were measured.A soil suspension was prepared by adding500g of soil to1l of distilled water.After stirring and standing for3min,the upper phase was used for the¯occulation test.A cell suspension of Saccharomyces cerevisiae in distilled water was used as a yeast suspension.2.6.3.E ect of metal salts and pH on the¯occulating activity of¯occulantTo estimate the in¯uence of the salts,distilled water with5g/l of suspended kaolin was used as a test. Flocculation tests were carried out using the method mentioned above,except that the CaCl2solution was replaced by various metal salt solutions,with the¯oc-culation activity being similarly measured.Solutions of KCl,CaCl2,MgCl2,FeSO4Á7H2O,FeCl3Á6H2O and AlCl3Á6H2O were used as the sources of cations.To examine the e ects of pH values of the reaction mixture on¯occulating activity,the reaction mixtures composed of a kaolin suspension,¯occulant and various cations (Ca2 ,Mg2 ,Fe2 ,Fe3 or Al3 )were adjusted to pre-determined pH values using HCl and NaOH,¯occu-lating activities were then measured in the same manner as described above.The pH values of the test suspen-sions ranged from3.0to11.0.The OD of a test sus-pension free from salt was designated as OD550 c and the activity was calculated.The¯occulant productivites and the¯occulating ac-tivities of the¯occulants in this study were the mean value of two experiments.3.Results and discussion3.1.Production of bio¯occulantThe¯occulant productivity of B.licheniformis CCRC 12826was investigated using various nitrogen and car-bon sources.The e ects of carbon sources on bio¯occ-ulant production are shown in ctose,glucose, fructose were not favorable for growth and¯occulantTable1E ects of various carbon sources on¯occulant production of B.licheniformis CCRC12826Carbon source Isolated yield of¯occulant(g/l) Glucose ND aFructose NDLactose NDGlycerol0.1Citric acid0.6Glutamic acid0.4Citric acid Glutamic acid 1.6Citric acid Glycerol 1.0Glutamic acid Glycerol 4.3Glutamic acid Citric acid Glycerol14.2a Non-detectable.I.L.Shih et al./Bioresource Technology78(2001)267±272269production.In contrast,glutamic acid,citric acid,and glycerol were more favorable for growth and ¯occulant production.The e ects of these three carbon sources on ¯occulant production were synergistic.The e ects of nitrogen sources on ¯occulant production were also in-vestigated by cultivating the bacteria in the same medi-um,except that the nitrogen source was varied.As shown in Table 2,B.licheniformis CCRC 12826was able to use ammonium chloride e ectively,but unable to use other ammonium salts.When B.licheniformis CCRC 12826was cultured in medium containing glutamic acid (20g/l),citric acid (12g/l),glycerol (120g/l)as described in Section 2,it was observed that the medium became viscous.The course of ¯occulant production as well as changes of viscosity,pH of the medium,and the cell growth were monitored and shown in Fig.1.The pro-duction of ¯occulant,and the relative viscosity of the medium reached a maximum after 96h of incubation.The cell growth also reached a maximum after 96h.The overall product isolated after incubation for 96h was 14g/l.The pH pro®le showed that the pH fell from 6.5to 5.63in about 60h but rose afterward.3.2.Characterization of ¯occulantThe number-average molecular weight (M n )of the ¯occulant was determined by gel permeation chroma-tography and found to be over 2Â106.The 6N HCL hydrolysate of the puri®ed viscous material was com-posed solely of glutamic acid.Thin-layer chromato-graphy of the hydrolysate performed on a cellulose thin-layer plate and visualized with 0.2%ninhydrin in-dicated a single spot with a R f value identical to that of authentic glutamic acid.Furthermore,the ninhydrin and biuret reactions for the viscous material were neg-ative.The phenol±sulfuric acid method showed that the polysaccharide contained less than 1%(w/w)of total sugar.In the hydrolysate of the ¯occulant,less than 1%of acetic acid,pyruvic acid,and uronic acid were de-tected.From these results,the biopolymer was con-cluded to be poly (glutamic acid).3.3.Flocculating activity of culture broth and puri®ed ¯occulant3.3.1.The e ects of ¯occulant concentration and molec-ular weight on the ¯occulating activityThe ¯occulating activity of the culture broth was es-timated by the addition of various amounts of culture broth to a constant concentration of kaolin suspension (5000mg/l).The ¯occulating activity reached a maxi-mum when 1.5ml of culture broth was added.In a same manner,the e ect of the concentration of puri®ed ¯occulant on the ¯occulating activity was investigated by addition of various amounts of ¯occulant solution (80mg/l)to a constant concentration of kaolin suspen-sion (5000mg/l)containing 9.0mM CaCl 2.The ¯occu-lating activity increased in proportion to the amount of added ¯occulant up to 0.5ml of ¯occulant solution which gave the highest ¯occulation.The optimal ¯occ-ulant concentration in test solution was 3.7mg/l.The ¯occulating activity was clearly reduced when lower molecular weight PGA was tested,but the optimal concentration was the same as that of the experimental PGA.The result of the comparative study between the puri®ed ¯occulant and the commercial PGA ¯occulant indicated the fact that the molecular weight of a ¯occ-ulant as related to the chain length of the polymer is an important factor in the ¯occulating reaction.A large-molecular-weight ¯occulant usually is long enough and has a su cient number of free functional groups which can act as bridges to bring many suspended particles together,and hence cause a larger ¯oc size in the ¯oc-culating reaction (Gutcho,1977;Michaels,1954).It is known that the molecular weights of PGA varied from 100,000to 2,000,000depending upon the species and the cultivation time used to produce them.Whether the variation of molecular weight of PGA of di erentTable 2E ects of various nitrogen sources on ¯occulant production of B.licheniformis CCRC 12826Nitrogen sources Isolated yield of ¯occulant (g/l)NH 4Cl 13.9NH 4NO 3ND a (NH 4)2SO 40.2Peptone ND Urea0.3aNon-detectable.Fig.1.Course of ¯occulant (PGA)production,changes of viscosity,pH and bacterial growth.In the upper panel:(r )pH;(j )OD.In the lower panel:(r )viscosity;(j )PGA.The PGA productivities were according to the isolated yields deriving from the procedures described in Section 2.270I.L.Shih et al./Bioresource Technology 78(2001)267±272species has any in¯uence on the¯occulating activity needs further clari®ed.3.4.E ects of various salts and pH on the¯occulating activityFlocculating activity was markedly increased by the addition of Ca2 and Fe3 ,but decreased by the addition of the trivalent cation Al3 compared with that of the control where distilled water was used instead of cation solutions.The concentration of Ca2 for maximum ¯occulating activity was13.5mM.The dramatic decrease of¯occulating activity by addition of trivalent Al3 was probably due to the drop of pH to about3.5when this cation(as AlCl36H2O)was added into the solutions. To examine the e ects of pH of reaction mixture on ¯occulating activity,the pH of a reaction mixture con-taining kaolin suspension,9.0mM of cations(Ca2 ,Fe3 or Al3 )and biopolymer¯occulant(0.5ml,80mg/l) was adjusted with HCl and NaOH,and then¯occulat-ing activity was measured.The optimal¯occulating ac-tivities for Ca2 ,Fe3 and Al3 were near the neutral pH range;7.0,6.4and7.1for Ca2 ,Fe3 and Al3 ,re-spectively.Kaolin dissolved easily at a pH below3.0,a phenomenon that makes the accurate measurement of ¯occulating activity di cult under this pH.Highly synergistic e ects with addition of bivalent or trivalent cations were observed.Although the synergistic e ects were exerted at di erent pH depending on the cations,the synergistic e ects of the trivalent cations were stronger than the bivalent cations,with Al3 being the most e ective cation at neutral pH.These results suggest that¯occulation is due to change in the charge density,they might indicate that cation e ects resulted in neutralization of the zeta potential and are in accor-dance with Schulze Hardy's law(Klute and Neis,1976).3.5.Flocculation of various suspended solidsThe spectrum of¯occulating ability for various sus-pended solids in aqueous solution is shown in Table3.The¯occulant¯occulated e ciently all the tested solid suspensions,with some variations in activity.The sus-pensions of kaolin,active carbon and soil solid could be ¯occulated e ectively in the presence of Ca2 ,the¯oc-culating activities in suspensions containing3.7mg/l of biopolymer were8.5,3.0,1.4,respectively.The Ca(OH)2 and Mg(OH)2suspensions could be¯occulated by the addition of biopolymer alone.The suspensions of alu-minum oxide,cellulose,CM cellulose and yeast were better¯occulated by the¯occulant in the neutral pH ranges.Recently Yokoi and coworkers(Yokoi et al.,1995, 1996)have demonstrated that poly(c-glutamic acid) produced by Bacillus sp.PY-90and Bacillus subtillis IFO3335had a high¯occulating activity.However,high ¯occulating PGA produced by B.licheniformis species has never been reported.The facts that the¯occulant from B.licheniformis CCRC12826has a¯occulating activity for a wide range of organic and inorganic compounds suggest that such a¯occulant could be ap-plied in many industries.It is anticipated that PGA will be utilized not only in the areas of wastewater treatment but also drinking water processing and downstream processing in the food and fermentation industry be-cause of its harmlessness toward humans and the envi-ronment.AcknowledgementsThis work was supported partially by Grant NSC89-2313-B-212-007from National Science Council of ROC.ReferencesBradford,M.M.,1976.A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding.Anal.Biochem.72,248±254. Chaplin,M.F.,Kennedy,J.F.,1986.Carbohydrate Analysis.IRL Press,Washington,DC,pp.2±5.Dear®eld,K.L.,Abermathy,C.O.,1988.Acrylamide:its metabolism, developmental and reproductive e ects,genotoxicity,and carcino-genicity.Mutant.Res.195,45±77.Frideman,T.E.,Haugen,G.E.,1943.The determination of keto acids in blood and urine.Pyruvic acid,II.J.Biol.Chem.147,415±442. Gutcho,S.,1977.Waste Treatment with Polyelectrolytes and other Flocculants.Noyes Data Corp.,Park Ridge,NJ,pp.1±37. Klute,R.,Neis,U.,1976.Stability of colloidal kaolinite suspensions in the presence of soluble organic compounds.In:M.Ker(Ed.), Proceedings of the International Conference,Colloid Interface Science,vol.4,50th ed.p.113.Kurane,R.,Takeda,K.,Suzuki,T.,1986.Screening for and characteristics of microbial¯occulant.Agric.Biol.Chem.50, 2301±2307.Kurane,R.,Nohata,Y.,1991.Microbial¯occulation of waste liquids and oil emulsion by a bio¯occulant from Alcaligenes latus.Agric.Biol.Chem.55,1127±1129.Kurane,R.,Hatamochi,K.,Kakuno,T.,Kiyohara,M.,Hirano,M., Taniguchi,Y.,1994.Production of a bio¯occulant by RhodococcusTable3The¯occulating activity of¯occulant produced by B.licheniformisCCRC12826on various suspended solidsVarious solid suspensions Flocculating activity(1/OD)Kaoline clay8.5Active carbon 3.0Soil solid 1.4Ca(OH)211Mg(OH)2 5.0Aluminum oxide a 6.1Cellulose powder a 3.0C.M.cellulose a 2.6S.serevisiae a 4.6B.circulans a 2.8a Activity was measured at neutral pH range.I.L.Shih et al./Bioresource Technology78(2001)267±272271erythropolis S-1grown on alcohols.Biosci.Biotech.Biochem.58, 428±429.Lee,S.H.,Lee,S.O.,Jang,K.L.,Lee,T.H.,1995.Microbial¯occulant from Arcuadendron sp.TS-49.Biotechnol.Lett.17,95±100. Leonard,C.G.,Housewright,R.D.,Thorne,C.B.,1958.E ects of some metallic ions on glytamyl polypeptide synthesis by Bacillus subtilis.J.Bacteriol.76,499±503.McComb,E.A.,McCreasy,R.M.,1957.Determination of acetyl in pectin and in acetylated carbohydrated polymers.Anal.Chem.29, 819±821.Michaels,A.S.,1954.Aggregation of suspensions by polyelectrolytes.Ind.Eng.Chem.46,1485.Nakamura,J.,Miyashiro,S.,Hirose,Y.,1976.Conditions for production of microbial cell¯occulant by Aspergillus sojae AJ7002.Agric.Biol.Chem.40,1341±1347.Stewart,J.M.,Young,J.D.,boratory techniques.In:Solid Phase Peptide Synthesis,second ed.Pierce Chemical Co,Rockford, IL,p.104(Chapter2).Suh,H.H.,Kwon,G.S.,Lee,C.H.,Kim,H.S.,Oh,H.M.,Yoon,B.D., 1997.Characterization of bio¯occulant produced by Bacillus sp.DP-152.J.Ferment.Bioeng.84,108±112.Takagi,H.,Kadowaki,K.,1985.Flocculant production by Pacilomy-ces sp.Taxonomic studies and culture conditions for production.Agric.Biol.Chem.49,3151±3157.Takeda,M.,Kurane,R.,Koizumi,J.,Nakamura,I.,1991.A protein biofocculant produced by Rhodococcus erythropolis.Agric.Biol.Chem.55,2663±2664.Takeda,M.,Koizumi,J.,Matsuoka,H.,Hikuma,M.,1992.Factorsa ecting the activity of a protein bio¯occulant produced byNocardia amarae.J.Ferment.Bioeng.74,408±409.Toeda,K.,Kurane,R.,1991.Microbial¯occulant from Alcaligenes cupidas KT201.Agric.Biol.Chem.55,2793±2799. Vanhorick,M.,Moens,W.,1983.Carcinogen of acrylamide.Carci-nogenesis4,1459±1463.Yokoi,H.,Natsuda,O.,Hirose,J.,Hayashi,S.,Takasaki,Y.,1995.Characteristics of a biopolymer¯occulant produced by Bacillus sp.PY-90.J.Ferment.Bioeng.79,378±380.Yokoi,H.,Arima,T.,Hirose,J.,Hayashi,S.,Takasaki,Y.,1996.Flocculation properties of poly(gamma-glutamic acid)produced by Bacillus subtilis.J.Ferment.Bioeng.82,84±87.Wang,Z.,Wang,K.,Xie,Y.,1995.Bio¯occulant-producing micro-organisms.Acta Microbiol.Sin.35(2),121±129.272I.L.Shih et al./Bioresource Technology78(2001)267±272。

翻译

翻译

铬镍铁合金718超级耐热合金中槌头在激光震动中的剩余压力一项热放松有限元研究摘要调查了剩余压力在被槌头(LSP)铬镍铁合金718 以 Ni 为基础的超级耐热合金的激光震动和在他们的上升温暖气流中强调放松行为是基于三度空间的非线性有限之物元素分析。

要解释非线性构成的行为,已经使用JohnsoneCook 模型和这模板叁数为高度紧张率来比较在铬镍铁合金718 的回应以校正与最近的实验的结果。

基于 LSP 模拟,经过在 LS-DYNA 加倍了热结构分析研究了热的放松行为。

为了更明确测试温度的效果,分析并且讨论暴露时间和开始塑料毁坏的程度。

一般观察主要地强调放松在暴露的起始期间发生,和伴随应用温度的增加放松广阔的增加和当做槌头的塑料毁坏。

基于这种模拟的产生,一个计划以ZenereWerteAvrami 功能为基础的分析的模型被用以模型热的剩余压力的放松。

介绍镀镍像铬镍铁合金718这样的超级耐热合金, 广泛地被用在高温耐热材料中,从做在涡轮引擎材料成份中,到杰出机械的财产,体现了腐蚀抵抗,和焊接特性[1]。

为了改良疲劳金属材料的表现,各种不同的机械表面治疗技术,(注射槌头(SP)[2],低可塑性抛光加工(LPB)[3]、和激光震动槌头(LSP)[4], 等)而且激光震动槌头已经开始被发展为疲劳否定倾向或者藉由在表面上和在表面的区域附近感应压力的增加。

与其他机械的表面疗伤相较, LSP它能在复杂的几何学上被运行,而且无用成份中包括链接中有独特特性。

此外,它能使用高能量激光脉冲引诱剩余压力胶棒深度进入金属制的表面。

时常观察在Ni 合金的剩余压力开始放松在预测中是一个重要的问题,这材料成分和罐子的疲累活动可能地影响引擎表现[5]。

热放松感应藉由引擎高温度是重要表面的剩余压力引起有压缩力层的失去可提高的涡轮引擎成份给予保护主要机制之一。

因此,对以Ni为基础超级耐热合金在提高温度理解剩余压力热放松的申请批评性很重要。

德国克鲁伯的一款高温油脂

德国克鲁伯的一款高温油脂

BARRIERTA L 55 series, Art-No. 090014, 090013, 090035, 090042, en Edition 21.12.2011 [replaces edition 21.12.2011]Benefits for your application–Higher machine availability and less need for maintenance –at very high operating temperatures up to 260 °C –under the influence of aggressive media and vapours–where other lubricants might affect sensitive plastic components–Tried and tested over many years in numerous industries and component types–thanks to BARRIERTA base oils, which are made specifically to enable long-term stability –backed by a large number of approvals and references for various applications –four consistency classes to suit different applicationsDescriptionBARRIERTA is Europe's oldest high-quality brand of high-temperature lubricants based on perfluorinated polyether oil (PFPE). 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A low evaporation rate enables longest grease lives and hence longest relubrication intervals.Typical applications include:–conveyors (load and turn rollers)–kiln cart wheel bearings –calender bearings –fan bearings–chain bearings in film stretching stentersBARRIERTA L 55/2 is most frequently used for initial and long-term lubrication.For relubrication softer grades of NLGI class 1 or lower are recommended.Friction points under the influence of mediaBARRIERTA L 55 greases offer exceptionally long service lifetimes even when exposed to any of a large number of aggressive media such as concentrated acids, lyes, organic solvents or gases.In addition to their resistance to media, BARRIERTA L 55/2 and BARRIERTA L 55/3 offer also good adhesion and a sealing effect,which makes them suitable for application in –valves, fittings and installations e.g. in the chemical industry –pneumatic components–level gauges, e.g. for fuels or chemicals –seals (static, dynamic)–extraction systems Food-processing and pharmaceutical industriesAll BARRIERTA L 55 greases are registered as NSF-H1 and are therefore in compliance with FDA 21 CFR § 178.3570. The additional certification according to ISO 21469 supports the compliance with the hygienic requirements in your productionBARRIERTA L 55 seriesHigh-temperature long-term greasesProduct informationplant. You will find further information on ISO Standard 21469 on our website .White-coloured BARRIERTA L 55 special lubricants can therefore also be used on friction points where occasional contact with food products cannot be ruled out for technical reasons, e.g. in rolling and plain bearings and guides operating under high thermal loads in –automatic baking ovens –cooking or frying lines –conveyor systemsPlastic-plastic friction pointsBARRIERTA L 55 greases – irrespective of NLGI grade - are neutral towards the majority of plastic materials. Results of pertinent tests with fluoroelastomers can be found overleaf. We recommend testing lubricant compatibility with the materials in question prior to series application.Application notesFor optimum results we recommend cleaning all friction points with white spirit 180/210 and then with Kl beralfa XZ 3-1 prior to initial lubrication. Subsequently, the friction points should be dried with clean dry compressed air or hot air to remove all solvent residues.The friction point must be free from oil, grease, perspiration and contamination particles before initial lubrication.Please contact our technical sales staff for details of best practice with BARRIERTA L 55 lubricants to ensure longest lifetimes and highest performance outcomes are achieved.Minimum shelf lifeThe minimum shelf life is approx. 60 months if the product isstored in its unopened original container in a dry, frost-free place.Material safety data sheetsMaterial safety data sheets can be downloaded or requested via our website . You may also obtain them through your contact person at Kl ber Lubrication.BARRIERTA L 55 seriesHigh-temperature long-term greasesProduct informationBARRIERTA L 55 series, Art-No. 090014, 090013, 090035, 090042, en Edition 21.12.2011 [replaces edition 21.12.2011]Product data BARRIERTA L55/0BARRIERTA L55/1BARRIERTA L55/2BARRIERTA L55/3Article number090035 090042 090013 090014NSF-H1 registration129 523 129 561 129 400 129 562 Chemical composition, type of oil PFPE PFPE PFPE PFPE Chemical composition, solid lubricant PTFE PTFE PTFE PTFELower service temperature-40 °C / -40 °F-40 °C / -40 °F-40 °C / -40 °F-30 °C / -22 °F Upper service temperature260 °C / 500 °F260 °C / 500 °F260 °C / 500 °F260 °C / 500 °F Colour space white white white whiteDensity at 20 °C approx. 1.95g/cm³approx. 1.95g/cm³approx. 1.96g/cm³approx. 1.96g/cm³Shear viscosity at 25 °C, shear rate 300 s-1; equipment:rotational viscometer approx. 4 500mPasapprox. 10 000mPasapprox. 14 000mPasShear viscosity at 25°C, shear rate 300 s-1,equipment:rotational viscometer, upper limit value5 500 mPas8 000 mPasShear viscosity at 25 °C, shear rate 300 s-1,equipment: rotational viscometer, lower limit value3 500 mPas4 000 mPasKinematic viscosity of the base oil, DIN 51562 pt. 01/ASTM D-445/ASTM D 7042, 40 °C approx. 420mm²/sapprox. 420mm²/sapprox. 420mm²/sapprox. 420mm²/sKinematic viscosity, DIN 51562 pt. 01/ASTM D-445/ASTM D 7042, 100 °C approx. 40mm²/sapprox. 40mm²/sapprox. 40mm²/sapprox. 40mm²/sFlow pressure of lubricating greases, DIN 51805, testtemperature: -30 °C<= 1 400 mbarFlow pressure of lubricating greases, DIN 51805, testtemperature: -40 °C<= 1 400 mbar<= 1 600 mbarFour-ball tester, welding load, DIN 51350 pt. 04>= 6 000 >= 7 000 >= 8 000 >= 8 000Speed factor (n x dm) approx. 300 000mm/min approx. 300 000mm/minapprox. 300 000mm/minapprox. 300 000mm/minCorrosion inhibiting properties of lubricating greases, DIN 51802, (SKF-EMCOR), test duration: 1 week, distilled water <= 1 corrosiondegree<= 1 corrosiondegree<= 1 corrosiondegreeAdditional data: resistance to fluoroelastomersChange75 FKM 58580 FKM 61060 FVMQ 565 Exposure life [h] / exposure temp. [°C]168 / 160168 / 160168 / 150 Volume [%]+ 0.5+0.5- 0.3 Hardness (Shore A)+ 0.5- 1- 2Tensile strength [%]+ 15+ 15- 16 Elongation at tear [%]- 11- 11- 10Klüber Lubrication – your global specialist Innovative tribological solutions are our passion. Throughpersonal contact and consultation, we help our customers to be successful worldwide, in all industries and markets. With our ambitious technical concepts and experienced, competent staff we have been fulfilling increasingly demanding requirements by manufacturing efficient high-performance lubricants for more than 80 years.Klüber Lubrication München KG /Geisenhausenerstraße 7 / 81379 München / Germany /phone +49 89 7876-0 / fax +49 89 7876-333.The data in this document is based on our general experience and knowledge at the time of publication and is intended to give information of possible applications to a reader with technical experience. It constitutes neither an assurance of product properties nor does it release the user from the obligation of performing preliminary field tests with the product selected for a specific application. All data are guide values which depend on the lubricant's composition, the intended use and the application method. The technical values of lubricants change depending on the mechanical, dynamical, chemical and thermal loads, time and pressure. These changes may affect the function of a component. We recommend contacting us to discuss your specific application. If possible we will be pleased to provide a sample for testing on request. Kl ber products are continually improved. Therefore, Kl ber Lubrication reserves the right to change all the technical data in this document at any time without notice.Publisher and Copyright: Kl ber Lubrication M nchen KG. Reprints, total or in part, are permitted only prior consultation with Kl ber Lubrication M nchen KG and if source is indicated and voucher copy is forwarded.a company of the Freudenberg GroupProduct information。

Dolomite occurrence, evolution and economically important

Dolomite occurrence, evolution and economically important
Keywords: dolomite; brine; models; porosity; permeability; oil and gas; base metal
E-mail address: jwarren@brunet.bn ŽJ. Warren..
0012-8252r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 2 - 8 2 5 2 Ž 0 0 . 0 0 0 2 2 - 2
Dolomite is an unusual carbonate mineral. It is common in ancient platform carbonates, yet it is rare in Holocene sediments and, without bacterial mediation, is near impossible to precipitate in the laboratory at earth surface temperatures. Some argue that widespread dolomite forms mostly in association with hypersaline brines, others that it can form from schizohaline waters, or that the time required to form large volumes of dolomite means that the process must be predominantly subsurface and perhaps tied to the long-term circulation of seawater through platform sediments ŽLand, 1985; Hardie, 1987..

Numerical modelling of the self-heating process of a wet porous medium

Numerical modelling of the self-heating process of a wet porous medium

Numerical modelling of the self-heating process of a wet porous mediumA.Ejlali ⇑,D.J.Mee,K.Hooman,B.B.BeamishSchool of Mechanical and Mining Engineering,The University of Queensland,Qld 4072,Australiaa r t i c l e i n f o Article history:Received 25February 2011Received in revised form 15August 2011Accepted 15August 2011Available online 6September 2011Keywords:Self-heating in moist porous material Buoyancy PorosityCoal stockpile Oxidationa b s t r a c tIn this research,the shortcomings of some advanced models for heat and fluid flow and moisture trans-port within and around a reactive porous medium (a coal stockpile)are numerically investigated based on a local thermal non-equilibrium approach and results are compared with experimental data.A full account of the problem is presented,including transient variation of the maximum temperature in the medium and the time required to vaporize the water content.The former is the main parameter used in the study of spontaneous self-ignition of coal stockpiles and the latter is an indication of the last step of self-heating toward self-ignition.Correlations are developed for the maximum dimensionless temper-ature inside the pile when the moisture content is near zero and the time that it takes from the beginning of self-heating until this time as functions of porosity,permeability,moisture content and the overall size of the porous medium.Ó2011Elsevier Ltd.All rights reserved.1.IntroductionThe role of moisture in the self-heating of porous media is a key economic and safety consideration in heat generating porous med-ia,such as coal.Most natural products,such as hay,wool,and coal,contain moisture and their self-heating is affected by the transport of moisture.Simultaneous heat transfer due to oxidation and mois-ture transport through porous media occurs in areas as diverse as food processing,energy production and mining operations.Self-heating is the process by which a temperature rise in a por-ous medium occurs due to internal heat generation from chemical or physical processes taking place within the reactive porous med-ium.When a wet porous medium is subjected to thermal drying,the main processes that occur simultaneously are the transfer of energy and the transfer of internal moisture to the surface and the subsequent evaporation of the moisture due to the energy transfer.Dong [1]uses one dimensional mathematical modelling to determine the effect of moisture content on the maximum temper-ature rise in a coal pile.He included the influence of chemical exo-thermic reactions on phase change of the moisture during the drying process.The influence of coal stockpile height,slope angle and moisture was investigated by Akgun and Essenhigh [2].Their model predicts that the ignition temperature is dependent on the bed porosity,pile shape,and coal type and the time to ignition is usually in excess of half a month or more.Bouddour et al.[3]simplified mathematical models of heat and mass transfer in wet porous media in the presence of evaporation and modelled them using asymptotic expansions for periodic structures.This method uses four homogenization techniques based on double scale expansions to specify the domains where the different descriptions can be used.Spontaneous combustion may happen when the heat generated within the reactive pile cannot be transferred away quickly enough to the environment by natural convection [4].The time needed for the temperature to reach a critical value for a moist porous medium such as coal is much more than that re-quired for a dry one [5–8].The effect of relative pressure on water diffusion/sorption into coal assuming thermal equilibrium has been reported for some specific applications [9,10].Self-heating of stockpiles of bulk porous materials is also affected by the penetration of water into the void volumes and the humidity of the surroundings.For example,an increase in humidity can en-hance the self-heating due to heat released to the stockpile when condensation occurs [11,12].An experimental investigation and a simplified numerical simulation of the influence of humidity on the self-heating of combustible materials are reported by Lohrer et al.[13].They show that a moist atmosphere may lead to a signif-icant increase in the temperature of the medium.The air entering the stockpile can play a major role in increasing the heat removal from the stockpile to the environment.However,increased air flow can also enhance the rates of chemical reactions,leading to an increase in the maximum temperature within the stockpile.This can then lead to spontaneous combustion.An increasing temperature within the stockpile is used as a criterion for possible spontaneous combustion.Ejlali et al.[14]show that there is a particular mass flow rate of air that leads to the lowest maximum temperature within a reacting stockpile.0017-9310/$-see front matter Ó2011Elsevier Ltd.All rights reserved.doi:10.1016/j.ijheatmasstransfer.2011.08.025Corresponding author.E-mail address:arash.ejlali@.au (A.Ejlali).There have been few studies that investigate the effects of mois-ture transport in reactive porous media on actual heat generationand water phase change.In the present research,numerical mod-elling of self-heating within coal stockpiles of typical size and physical properties has been coupled with a set of moisture trans-port correlations with buoyancy effects to study the transient heat transfer and moisture transport in heat generating porous media.The result is a system of non linear time-dependent equations for the flow inside and around the pile.Solutions allow the predic-tion of the magnitude of the maximum dimensionless temperature within the stockpile.As the solution proceeds,the water content goes to zero at different points in the stockpile at different times.The point in time when the water content first goes to zero is also of interest.The solution also gives the temperature at this point and the time at which it occurs.The former is a measure of coal self-heating.After the liquid water has evaporated inside the pile,coal enters the final step of self-heating which can result in self-ignition.The approach is quite general and,in addition to coal stockpiles,could be applied to stockpiles of any reactive material.However,the focus of this particular research is on coal stockpiles of typical sizes that would be found in the coal mining and process-ing industry.2.Mathematical modellingFig.1shows a schematic diagram of the problem under consid-eration.A two-dimensional frustum-shaped geometry has beenadopted in this study because this is representative of the typical shape of a coal stockpile.The third direction is assumed to be rel-atively long so that the governing equations,taken from Vafai et al.[15–17]to cover both porous and non-porous domains,reduce to a two-dimensional form.The conservation equations for moisture in liquid and vapour forms and the energy equation are derived based on Whitaker’s theory as detailed by Kallel et al.[18].The generic forms of the unsteady equations for moisture movement in the so-lid porous materials are given by Murugesan et al.[19].These are based on the conservation of mass,energy and moisture for both porous and non-porous media.The governing equations for the present investigation are as follows.(a)Continuityr Á~V ¼0ð1ÞThe appropriate momentum equation outside the porous media is the Navier–Stokes equation.For the porous region,the momentum equation is(b)Momentum equationq a e @~V þq a e hð~V Ár Þ~V i ¼Àl h ~V i Àq a F e ffiffiffiffiffiffikpp½h ~V i Áh ~V i J þle r 2h ~V i Àr P þq a g b r T ~j ð2Þwhere ~V is the velocity vector with components in the x -and y -directions.Nomenclature a sf specific surface area of the porous media (m À1)C vapour concentration in free ambient environment C P air specific heat at constant pressure (J kg À1K À1)d p solid particle diameter (m)D moisture diffusion coefficient (m 2s À1)D 1stockpile height (m)Da Darcy number (kpD1À2)F inertial coefficientg gravitational acceleration (ms À2)h height of the computational domain (m)h sf interfacial heat transfer coefficient (W m À3K)H enthalpy (J kg À1)j unit vectork thermal conductivity (W m À1K)kp permeability of the porous medium (m 2)l length of the computational domain (m)_m rate of moisture evaporation (kg m À2s À1)M mass of porous media (kg)M ⁄m/M (s À1)n normal directionPr Prandtl number,l C p =kQ volumetric heat generation rate (W m À3)T ⁄temperature (K)t ⁄time (s)t dimensionless time,t ⁄u /D 12T i initial temperature (K)T dimensionless tempera-ture,ðl C p ÞðT ÃÀT i Þ=½q Q 1ÁA Áexp ÀE RT iÁD 12 u ⁄x -direction velocity (ms À1)U dimensionless velocity in x-direction,u ⁄D /t U i inlet velocity (m s À1)U Mmagnitude of the dimensionless velocity vector,ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiU 2þV 2p v ⁄velocity in y -direction (m s À1)V dimensionless velocity in y -direction,v ⁄D /t V velocity vectorw moisture content (kg/kg of dry porous)(x ,y )Cartesian coordinates (m)(X ,Y )dimensionless Cartesian coordinates,(x /D 1,y /D 1)Greek symbols b thermal expansion coefficient (K À1)e porosity of the porous medium l air viscosity (Pa s)t fluid kinematic viscosity (m 2s À1)h porous medium (stockpile)side angle q density (kg m À3)Subscripts 0initial conditions a air e effective f fluid phase i inlet condition l liquid ml isotherm liquid moisture m v isotherm vapour moisture tl non-isotherm liquid moisture t v non-isotherm vapour moisture s solid t total v vapour vl difference between vapour and liquid p porous s solid phase 1ambient value X ,Y X ,Y component of the vectorA.Ejlali et al./International Journal of Heat and Mass Transfer 54(2011)5200–52065201(c)Energy(LTNE)Two separate energy equations are required.Heat generation due to oxidation takes place in the solid phase while water/vapour transport takes place in the void spaces,thuseðqÁCpÞf @TÃf@tþr hðqÁCpÞf~VÁTÃf i¼r h k feÁr TÃf iþh sf a sfðTÃsÀTÃfÞþMÃH v l q eð3Þandð1ÀeÞðqÁCpÞs @TÃs¼r h k seÁr TÃsiÀh sf a sfðTÃsÀTÃfÞþð1ÀeÞQð4ÞHere the effective thermal conductivity for the solid/fluid phase is the stagnant thermal conductivity plus the liquid/vapour thermal conductivity times the species ratio.The moisture movement in the porous media takes places both in liquid and vapour forms [19,20]w v¼q vqpð5Þw l¼qlqpð6Þ@w l @t ¼@@xD ml@w t@xþD tl@TÃf@xþ@@yD ml@w t@yþD tl@TÃf@yþMÃð7Þ@w v @t þV!Ár w v¼@@xD m v@w t@xþD t v@TÃf@xþ@@yD m v@w t@yþD t v@TÃf@yÀMÃð8ÞAdding Eqs.(7)and(8)we get an expression for the total moisture@w t @t þV!Ár w v¼@@xðD mlþD m vÞ@w t@xþðD tlþD t vÞ@TÃf@xþ@ðD mlþD m vÞ@w tþðD tlþD t vÞ@TÃfð9ÞThe diffusion coefficients are taken to be constant and the vapour concentration equation in the ambient environment can be written as in Amanifard and Haghi[21]@CþVÁr C¼D r2Cð10ÞThe values of the diffusion coefficients are also taken from Amanifard and Haghi[21]to be D¼0:0256m2sÀ1,D t v¼10À12m2sÀ1KÀ1;D tl¼10À12m2sÀ1KÀ1;D ml¼10À8m2sÀ1;D m v¼10À12m2sÀ1.The permeability of the porous medium(kp),the form drag coefficient F,and h sf are defined by Alazmi and Vafai[15]as kp¼e3d2p150ð1ÀeÞ2ð11ÞF¼1:75ffiffiffiffiffiffiffiffiffiffiffiffiffi150e3pð12Þh sf¼k f2þ1:1pr1=3q uÃd pl0:6"#,d pð13ÞAlthough the self-heating of porous media is due to a number of exothermic reactions and the amount of air entering the pile,many studies,e.g.[22,23],have shown that the rate of oxygen consump-tion can be expressed by the following Arrhenius modelQ¼qÁQ1ÁAÁexpÀERTÃsð14ÞHere Q1is the heat of reaction,A is the pre-exponential factor,R is the universal gas constant and E is the activation energy.The present numerical solver can be used with any combination of the above parameters,however,in this study,we have used,the Lignite coal specifications[24–26]that presumes Q1=22MJ kgÀ1,A=1000sÀ1, E=55kJ molÀ1,Cp s=1500J kgÀ1KÀ1,k s=0.12WmÀ1KÀ1,and have set h=50°as the side angle.The appropriate dimensionless initial and boundary conditions are given byt¼0;U¼V¼0;TüT i;w t¼w l¼w0;C¼C0ð15ÞX¼0;@U@X¼@V@X¼0;TüT ið16ÞY¼0;U¼0;V¼0;@Tü0ð17ÞY¼h=D1;@U@Y¼@V@Y¼0;@TÃ@Y¼0ð18ÞX¼l=D1;@U¼@V¼0;TüT ið19Þand for the speciesflux balance at the interfaceqaD t v@TÃ@nþD m v@w v@n¼D@C@yð20Þwhere n is the normal direction.3.Numerical procedureThe numerical calculations for the above mentioned unsteady and coupled dimensionless vorticity,stream-function,and Fig.1.Schematic diagram of the model.temperature are achieved by thefiniteAlternating Direction Implicit(ADI)unconditionally stablefinite differencebolic and elliptic partial differentialmomentum,heat and moisture.Thecretized by second-order accurate centraldiscretization results in a tridiagonalfor each half time-step in the X directionother half time step in the Y direction forstep1,a tridiagonal matrix is solved forin the X direction,and for the secondsolved for all j rows of grid points in theplicit method allows significantly longerretains second order accuracy in bothwas implemented in FORTRAN.Thechosen to be large enough to eliminate theulation domain.Values of l/D1=10and hsufficient for this study.Gridning grid sets of250Â100,500Â200convergence criterion of10À6,and15%in velocity were observedium grid and less than5%between theTherefore,the500Â200grid was takenent simulations and all subsequentgrid.4.Results and discussionValidation of the code wasresults from the simulations withindustrial-scale stockpile[29]with18mm,18%humidity,5m width,3mTo compare the results withto investigate the transient performancedimensionless time,t,and dimensionlessplaced by the actual time and[29]located four temperature sensors,T14,T15,and T17,2m above the base ofthese tests are compared with resultssimulations in Fig.2.The overall trendssimulated and measured temperaturesresults from sensor T14that deviatethe other three locations.The reasonsresults for location T14from the others isThe temperature variation versus timethree main stages.At the beginning ofture rises rapidly for about one monthfects increase.This starts the secondgeneration due to oxidation balances with the energy absorption by water due to evaporation of the moisture.This results in analmost constant temperature stage.Nevertheless,the interactions among the processes of oxidation,drying and moisture movement, via diffusion or convection in the void spaces inside the coal,con-tinue until a certain temperature is reached.Beyond this tempera-ture,moisture contents is almost zero and will not restrain the temperature rise which is the last stage of self-heating toward self-ignition.After this point moisture content does not play a ma-jor role.At some point in stage3,ignition could occur.The current simulation does not predict the point of ignition.Another comparison between experiment and simulation has been made for a temperature sensor located1m from the base of the stockpile(temperature sensor T5).As shown in Fig.3,there is quite good agreement between the experiment and the simula-tion.The main source of difference between numerical and exper-imental data could be due to the two dimensional assumption that has been used here.The maximum difference observed between the numerical and experimental results from the beginning of self-heating to the point of almost zero moisture is approximately 10%at time2250h.The numerical predictions underestimate the mean of the experimentally measured temperatures in Fig.2for stage3(be-yond1500h),however,they are in good agreement for all three stages in Fig.3.A possible explanation for this is proposed.Note that the temperature sensors are1m from the surface of the stock-pile for the data in Fig.2and2m from the surface for the data in Fig.3.Since the sensor locations are closer to the surfaces of the stockpile for the data in Fig.2,it is expected that the temperatures at these locations would be more sensitive to variations in ambient conditions that are not under direct control in the experiment (such as local wind speed,and solar radiation).Therefore,the Fig. parison with experiment[29],1m higher above the ground level, Da=1.25Â10À7,e¼0:2,w t=0.18.parison with experiment[29],2m above the ground level,Da=1.25 10À7,e¼0:2,w t=0.18.A.EjlaliCFD boundary conditions and their temporal variations may not be exactly the same as those in the experiments.The influences of these boundary conditions may be expected to be larger closer to the surfaces of the stockpile.The temperatures in the stockpile in stages1and2are controlled to a large extent by the water content and are less sensitive to variations in the boundary condi-tions.However,stage3is associated with no water content and the temperature rise is more affected by the surroundings,particularly closer to the surface.Fig.4shows sample contour plots of the dimensionless solid–fluid temperature difference,T,within the porous medium at the beginning of self-heating(the start of stage 1)and at the beginning of stage3,when the water content is vaporized.This shows that the maximum dimensionless tempera-ture difference moves from the edge of pile at the beginning of self-heating toward the middle of the stockpile at later times.It also reveals that the temperature difference between solid and fluid phase is negligible during the process of self-heating.Iso-therms,velocity vectors and streamlines after20days for sample conditions are presented in Fig.5.As seen,air enters the stockpile from both sides,heat is removed from the stockpile and leaves the stockpile vertically at a higher temperature.The point of maximum temperature is located inside the pile near the symmetry axis and the magnitude of the temperature in this hot spot depends on both the rates of oxidation and moisture transport.difference between solid andfluid,(a)beginning of self-heating,(b)when water content is vaporized,Da Fig.5.Stream lines,velocity vectors,and isotherms after20days:e=0.2,w l=0.18,Da=1.25Â10À7.Figs.6and 7show the maximum temperature versus time for different levels of moisture content and physical properties for pile sizes of 2.5m and 2m height with the same side angle.The start-ing point of the third stage is highlighted by a dot on the curves.The time and temperature at which this occurs is a function of porosity,moisture content,and the Darcy number.When the initial moisture remains unchanged,the temperature at which the point of inflection occurs and the time at which this occurs both increase as the Darcy number decreases.When the Darcy number remains unchanged,the time at which the point of inflection occurs de-creases as the moisture content decreases.The numerical results show that the starting point for stage 3varies as a function of permeability,porosity,oxidation and mois-ture content.A goal of the current study is to be able to predict the dimensionless time at which stage 3starts and the maximum dimensionless temperature in the pile at this time.This has been achieved by running a large number of simulations for a range of conditions and using the Least Squares Curve Fitting Method [30,31].The objective of the curve-fitting process is to adjust a set of parameters of a model function to best fit a data set.The data set consists of n points ðf i ;g i Þ;i ¼1;2;...;n ,where f i is an indepen-dent variable and g i is a dependent variable.The dimensionless time and the dimensionless maximum temperature from the numerical simulations of heat,moisture and fluid flow are func-tions of three independent dimensionless parameters,namely porosity,moisture content and Darcy number.The model function for the dimensionless time and the dimensionless maximum tem-perature takes the form f (f ,a ),where the adjustable parameters are held in the vector a .The least squares method returns the bestfit when s ¼P n i À1r 2i is minimum.Here r i is the residual,defined as the difference between the simulation data and the model data,r i =g i Àf (f i ,a ).Simulations were run for stockpile heights,D 1,of 0.5,1.0,1.5,2.0and 2.5m.Three hundred and twelve simulations were run for each stockpile height for different combinations of e ,w t and Da .Curve fits to functions for dimensionless time,t ,and dimen-sionless maximum temperature,T ,were both taken to be of the forma eb þcw d t þeDa f:Tables 1and 2show the coefficients,a ,c and e ,and the exponents,b ,d and f ,obtained for the curve fits for the five stockpile heights.The largest deviations of the simulation points from the fitted points were 8%for the dimensionless time and 6%for the dimensionless maximum temperature.Fig.6.Variation of maximum temperature versus time,D 1=2.5m.Fig.7.Variation of maximum temperature versus time,D 1=2m.Table 2Coefficients of curve fitting for dimensionless time 0:046w t 60:5,0:16e 60:5,1:6Â10À116Da 64:4Â10À7.ab c d e ft ¼a e b þcw d t þeDa fD 1=0.5 1.01E+01 1.56E+00 2.09E+02 2.47E À01 1.00E+009.99E À01D 1=1À4.59E+02À1.07E À04 5.11E+027.71E À03 1.52E À04À3.03E À01D 1=1.5À1.93E+018.89E À04 5.12E+01 3.07E À02À6.19E À05À5.37E À01D 1=2À1.47E À019.69E À03 2.73E+01 1.68E À01 1.45E À06À5.35E À01D 1=2.52.12E À01À8.87E À03 2.05E+01 2.48E À018.67E À07À5.77E À01Table 1Coefficients of curve fitting for maximum dimensionless temperature 0:046w t 60:5,0:16e 60:5,1:6Â10À116Da 64:4Â10À7.ab c d e fT ¼a e b þcw d t þeDa fD 1=0.5 5.64E À03 5.05E À03À2.35E À02 4.29E+00 1.01E+007.96E À01D 1=1 5.74E À04 3.50E À01 5.69E À03À9.06E À02À2.34E À05À4.01E À02D 1=1.5 1.28E À02 1.55E À03 2.49E À01À3.14E À03À2.54E À01À4.51E À05D 1=2 1.24E À02 3.65E À04 2.49E À01À3.06E À03À2.55E À01 2.15E À04D 1=2.51.21E À02À5.60E À042.48E À01À3.56E À03À2.55E À01 3.95E À04and Mass Transfer 54(2011)5200–52065205For example,for a coal stockpile of0.5m height,the maximum dimensionless temperature in the stockpile would be given byT¼0:00564e0:00505À0:0235w4:29tþ1:01Da0:796To calculate the dimensionless time and dimensionless maximum temperature for a porous medium of another shape and/or size, the same procedure as used in this paper could be applied.5.ConclusionsA numerical approach has been used to study the self-heating mechanisms of wet porous media.Favourable comparisons be-tween numerical and experimental results justify the suitability of the numerical approach.It is observed that the temperature within the porous medium increases at the start of the process mainly due to self-heating.The rate of increase in temperature slows as the simulations proceed due to phase change from liquid to vapour within the medium.The last stage of self heating in-volves a more rapid temperature rise that commences when liquid moisture is fully vaporized and phase change is no longer available to restrain the temperature rise.The required dimensionless time to vaporize the water content and the related dimensionless max-imum temperature are predicted numerically as a function of three dimensionless parameters,porosity of the porous medium,e,mois-ture content,w t,and Darcy number,Da.Forfive typical coal stock-pile sizes,curvefitting using the Least Squares Method has been used to produce functions that can provide an estimation of the time that a typical coal stockpile can be kept safe as well as the maximum temperature inside the pile.References[1]X.Dong Chen,Effect of drying heat and moisture content on the maximumtemperature rise during spontaneous heating of a moist coal pile,Coal Preparation14(3–4)(1994)223–236.[2]F.Akgun,R.H.Essenhigh,Self-ignition characteristics of coal stockpiles:theoretical prediction from a two-dimensional unsteady-state mode,Fuel80 (2001)409–415.[3]A.Bouddour,J.L.Auriault,M.Mhamdi-Alaoui,J.F.Bloch,Heat and mass transferin wet porous media in presence of evaporation–condensation,Int.J.Heat Mass Transfer41(15)(1998)2263–2277.[4]A.Ejlali,K.Hooman,Buoyancy effects on cooling a heat generating porousmedium:coal stockpile,Transp.Porous Med.88(2)(2011)235–248.[5]A.Arisoy,Numerical modelling of coal spontaneous combustion with moistureincluded,in: A.D.S.Gillies(Ed.),Eighth International Mine Ventilation Congress,2005,pp.501–506.[6]S.Krishnaswamy,S.Bhat,R.D.Gunn,P.K.Agarwal,Low-temperature oxidationof coal.1:A single-particle reaction–diffusion model,Fuel75(3)(1996)333–343.[7]K.Brooks,V.Balakotaiah,D.Luss,Effect of natural convection on spontaneouscombustion of coal stockpiles,AIChE J.34(3)(1988)353–365.[8]A.Mao,Y.Li,Numerical heat transfer coupled with multidimensional liquidmoisture diffusion in porous textiles with a measurable-parameterized model, Numerical Heat Transfer Part A–Applications56(3)(2009)246–268.2009.[9]A.L.McCutcheon,W.A.Barton,M.A.Wilson,Kinetics of water adsorption/desorption on bituminous coals,Energy Fuels15(6)(2001)1387–1395. [10]D.Charriere,P.Behra,Water sorption on coals,J.Colloid Interface Sci.344(2)(2010)460–467.[11]B.F.Gray,G.C.Wake,The ignition of hygroscopic combustible materials bywater,Combust.Flame79(1)(1990)2–6.[12]A.C.McIntosh,B.F.Gray,G.C.Wake,The ignition of combustible material in thepresence of a damp atmosphere,Phys.Lett.A191(1–2)(1994)61–70. [13]C.Lohrer,A.Schmidt,U.Krause,A study on the influence of liquid water andwater vapour on the self-ignition of lignite coal-experiments and numerical simulations,J.Loss Prev.Process Ind.18(3)(2005)167–177.[14]A.Ejlali,S.M.Aminossadati,K.Hooman, B.B.Beamish,A new criterion todesign reactive coal stockpiles,mun.Heat Mass Transfer36(7)(2009) 669–673.[15]B.Alazmi,K.Vafai,Analysis of variants within the porous media transportmodels,J.Heat Transfer–Trans ASME122(2)(2000)303–326.[16]K.Vafai,M.Sozen,Analysis of energy and momentum transport forfluid-flowthrough a porous bed,J.Heat Transfer–Trans.ASME112(3)(1990)690–699.[17]A.Amiri,K.Vafai,Analysis of dispersion effects and non-thermal equilibrium,non-Darcian,Variable porosity incompressibleflow through porous media,Int.J.Heat Mass Transfer37(6)(1994)939–954.[18]F.Kallel,N.Galanis,B.Perrin,R.Javelas,Effect of moisture on temperatureduring drying of consolidated porous materials,J.Heat Transfer–Trans.ASME 115(3)(1993)724–733.[19]K.Murugesan,D.C.Lo,D.L.Young,C.W.Chen,C.M.Fan,Convective dryinganalysis of three-dimensional porous solid by mass lumpingfinite element technique,Heat Mass Transfer44(4)(2008)401–412.[20]G.I.Kelbaliev,M.P.Manafov,Mass transfer in the process of drying of porousmaterials,J.Eng.Phys.Thermophys.82(5)(2009)991–999.[21]N.Amanifard, A.K.Haghi,A numerical study on drying of porous media,Korean J.Chem.Eng.25(2)(2008)191–198.[22]D.A.Nield,A.Bejan,Convection in Porous Media,Springer-Verlag,New York,2006.[23]M.A.Smith,D.Glasser,Spontaneous combustion of carbonaceous stockpiles.Part I:The relative importance of various intrinsic coal properties and properties of the reaction system,Fuel84(9)(2005)1151–1160.[24]M.Malow,U.Krause,The overall activation energy of the exothermic reactionsof thermally unstable materials,J.Loss Prev.Process Ind.17(1)(2004)51–58.[25]Y.Madihin,H.Ogaki,ui,O.Okuma,The advantages of vacuum-treatmentin the thermal upgrading of low-rank coals on the improvement of dewatering and devolatilization,Fuel Process.Technol.84(1–3)(2003)147–160.[26]M.I.Nelson,X.D.Chen,Survey of experimental work on the self-heating andspontaneous combustion of coal,Geol.Soc.Am.,Rev.Eng.Geol.XVIII(2007) 31–84.[27]K.A.Hoffmann,S.T.Chiang,Computational Fluid Dynamics,EngineeringEducation System,1998(1),pp.337–352.[28]J.C.Tanehill,D.A.Anderson,H.P.Pletcher,Computational Fluid Mechanics andHeat Transfer,second ed.,Taylor&Francis,Washington,DC,1997.[29]A.H.Ozdeniz,Determination of spontaneous combustion in industrial scalecoal stockpile,Energy Sources32(a)(2010)665–673.[30]O.Bretscher,Linear Algebra with Applications,third ed.,Prentice Hall,UpperSaddle River,1995.[31]K.Hooman,A.A.Merrikh,Theoretical analysis of natural convection in anenclosurefilled with disconnected conducting square solid blocks,Transp.Porous Med.85(2010)641–651.5206 A.Ejlali et al./International Journal of Heat and Mass Transfer54(2011)5200–5206。

24内燃机缸盖水腔内过冷沸腾现象的数值模拟方法综述

24内燃机缸盖水腔内过冷沸腾现象的数值模拟方法综述

北京,2009年10月 A P C联合学术年会论文集 113内燃机缸盖水腔内过冷沸腾现象的数值模拟方法综述李 智,黄荣华(华中科技大学能源与动力工程学院 武汉 430074)摘 要:建立适合于内燃机缸盖水腔内的过冷沸腾数值模型,是精确预测缸盖内冷却水流动与传热状况的必要条件,也是实现内燃机高温冷却方式的必然手段。

本文综述了过冷沸腾现象的三种数值模拟方法及其在内燃机缸盖水腔中的应用,并提出建立适合于内燃机缸盖水腔内过冷沸腾数值模拟的两相流模型是未来发展的必然趋势,以及开展实际缸盖内的沸腾试验以进行模型的标定与验证具有十分重要的意义与价值。

关键词:缸盖水腔;沸腾传热;两相流;数值模拟Abstract:The development of subcooled boiling numerical model for cylinder head water jacket in IC engines is anecessary condition for accurately predicting the coolant flow and heat transfer in cylinder head, is also an inevitablechoice for achieve high-temperature cooling of IC engines. Three numerical simulation method of subcooled boilingphenomena as well as its application in cylinder head water jacket in IC engines, it proposed that developing two phaseflow model for numerical simulation of subcooled boiling in Cylinder Head water jacket in IC engines is the inevitabledirection in the future and it is very important and valuable that carrying out boiling experiments in practical cylinderhead water jacket for the and validation for models.Keywords: Cylinder head water jacket; boiling heat transfer; two phase flow; numerical simulation引言泡核沸腾换热由于其较高的换热率,在能源、化工、冶金以及核工业等行业得到了广泛的应用。

外文翻译原文--常规压力对采用非牛顿学流体润滑的光滑碟片表面的作用

外文翻译原文--常规压力对采用非牛顿学流体润滑的光滑碟片表面的作用

Normal Stress Effects in Slider-Disk InterfaceLubrication with Non-Newtonian FluidChen Haosheng1*, Li Jiang2, Chen Darong1, Wang Jiadao11. State Key Laboratory of Tribology, Tsinghua University, Beijing China 100084.2. Department of mechanology, University of Science and Technology Beijing, China, 100083AbstractTo analyze normal stress effects of non-Newtonian fluid in lubrication of the magnetic head-disk interface, a modified Reynolds equation including the effects of normal stress is established. The expression of the first normal stress difference in the equation is derived from the Rivlin-Ericksen second order flow equation and the fluid momentum equation. Lubrication results of the magnetic head-disk interface is calculated using the modified Reynolds equation. Under the condition of steady laminar lubrication, the pressure and the load capacity of non-Newtonian fluid is increased by the effect of the first normal stress difference, but the effect is constrained by the normal velocity and can be omitted in the calculation. When the slider flying height changes or the lubricant film thickness decreases, the normal velocity increases and the effect of the first normal stress difference need to be considered.Keywords:non-Newtonian fluid, first normal stress difference, magnetic data storage systems1. IntroductionAs mentioned by De Bruyne and Bogy[1], a non-Newtonian lubricant is used for some magnetic recording system to avoid dry contact. It has been proved that a significant reduction of pressure buildup under the slider cover can be achieved by introducing non-Newtonian shear thinning lubricant at high shear rate. To specify the behavior of non-Newtonian fluid in the lubrication of the head-disk interface, Wang-Long Li[2] provides an average Reynolds equation and point out that the effect of the flow behavior index of the power-law fluid on load capacity is more significant than that of the surface roughness. The properties of non-Newtonian fluid are important factors in the lubrication of the magnetic head-disk interface.Normal stress effect is a characteristic of non-Newtonian fluid. Some research results [3-5] have proved that the effects of the normal stress in some lubricants are obviously increased, and the first normal stress difference is far more than the second normal stress difference. The normal stress effect needs to be analyzed in the lubrication, and the method to calculate the first normal stress difference needs to be investigated. In this paper, the expression of the first normal stress difference of a kind of viscoelastic non-Newtonian fluid such as Maxwell fluid is derived and the lubrication equation which contains the effect of the normal stress is established. Numerical method is used to calculate the lubriation results of magnetic head-disk2. Expression of the First Normal StressThe normal stress is derived from Rivilin-Ericksen [6] flow Eq. (1).In Eq. (1), p is the pressure, ijτ is the tensor of the shear stress, ijγ& is the tensor of the shear rate, 1α is the fluid viscosity, 2α,3α are the second order coefficience of the viscoelastic fluid, tij/DDγ& is the material time derivative.Equation (1) is applicable for random coordinate system. In this paper, the Cartisian coordinate system is adopted. The micro unit of non-Newtonian fluid in the coordinate systems is shown in Fig. 1. The (x, y, z) is the fixed coordinate system for the calculation, the (zyx′′′,,) is the reference coordinate system and the (1, 2, 3) is the following coordinate system fixed on the micro unit. ijω′ is defined as the angular velocity of the micro unit to the following coordinate system, ijω is defined as the angular velocity of the following coordinate system to the reference coordinate system. Then, the angular velocity of the micro unit is ijijijωωω+′=.Figure 1. Maxwell micro unit in the coordinate systemsThe following coordinate system (1, 2, 3) is a rigid Cartisian coordinate system. The origin of coordinate is fixed on the micro unit, and the coordinate moves and rotates as the unit moves and rotates. The direction of the following coordinate is always same to that of the tensor of the shear rate. With Prager’s theory, the material time derivative should be expressed by the Rivlin-Ericksen shearing rate and the Jaumann covariant derivative. In the following coordinate system, the new tensor dtdij/γ& is expressed by the partial derivative shown as Eq. (2) and can be used to random coordinate system. is the velocity on the direction .When the lubricant is an ideal viscosity fluid, the direction of the principal axis of the following coordinate system is same to the axis of the principal stress tensor in the reference coordinate system, that means 0=ω and the first normal stress difference is zero.Material time derivative in Eq. (2) is defined in the following coordinate system. If the material time derivative is in the reference coordinate, the angular of the micro unit to the following coordinate system should be added.From the Eq. (4), the normal stress can be expressed as Eq. (5):In Eq. (5), α1 is the viscosity of the fluid. The second item on the right of the equation shows the effect of the viscosity to the normal stress. The third item shows the effect of the first normal stress difference. α2 is expressed as Eq. (6). λis r elaxtion time and ηˆ is the differential viscosity[7].The fourth item shows the effect of the second normal stress difference. It is commonly recognized that the second normal stress difference is far less than the first normal stress difference, the fourth item is omitted and the expression of the first normal stress difference is expressed as Eq. (7).In the lubrication, the lubricant film thickness is far less than other dimensions. Compared with the dominating velocity u and velocity gradient yu∂∂, xv∂∂ and xay∂∂ are omitted in Eq. (7), the first normal stress difference is simplified to Eq. (8).In Eq. (8), λ is the relaxation time, ω is the angular velocity of the following coordinate to the reference coordinate. ω is caused by the elasticity of non-Newtonian fluid and is considered as the natural frequency ωn of the viscoelastic micro unit . Thus, the first normal stress difference is specified as Eq. (9).Generally, the definition of the first normal stress difference is1γυ& is the fu nction of the first normal stress difference, with Eq. (9) and (10), the )(1γυ& is expressed as Eq. (11).3. Reynolds Equation including the First Normal Stress DifferenceTo analyze the effect of the normal stress in lubrication, a modified form ofReynolds equation that includes the first normal stress difference is firstly established under the condition of the steady laminar lubrication. In head-disk interface lubrication, the expression of the shear stress and the normal stress in the random coordinate system are shown as Eq. (12) after the conversion of the coordinates.Eq. (12) is derived from the conversion of the coordinates, another equation showing the relation between the pressure and the stress is derived from the momentum equation shown as Eq. (13)Under the real lubrication condition, Eq. (13) is simplified with some basic assumptions, and the momentum equation is changed to Eq. (14).(1) The inertia force and the external force are not considered,(2) The fluid can not be compressed,(3) Compared with the principal floware omitted.With Eq. (14) and Eq. (12), a modified Reynolds equation is derived and shown as Eq. (15). In Eq.(15), p is the pressure, U is the surface velocity, h is the thickness of the lubricant film, v is the velocity normal to the lubricant film. xyz are coordinates. The simplified slider geometry is shown as figure 2.4. Numerical Results of LubricationIn this section, numerical method is used to calculate the lubrication results. Based on the results, the effect of the first normal stress difference to the pressure profile and the load capacity is analyzed. The dimensionless equation used in calculation is shown as Eq. (16).In Eq. (16), the dimensionless parameters are illustrated as follows.To simplify the calculation, other factors such as temperature are considered as constant under the stable laminar lubrication, and the over relaxation method is used.4.1 Numerical results in stable laminar lubricationThe first normal stress difference of non-Newtonian fluid will affect the pressure profile and the load capacity. The pressure of the intermediate cross-section at b/2 is shown in Fig. 3. 0p is the dimensionless pressure of the lubricant which is not affected by the first normal stress difference, 1p is the dimensionless pressure which is under the effect of the first normal stress difference. Corresponding load capacities are shown in Fig. 4. In Fig. 4, W1 is the dimensionless load capacity under the effect of the first normal stress difference, W2 is the dimensionless load capacity not considering the effect of the first normal stress difference. Wn is the dimensionless load capacity of Newtonian fluid.Figure 3. Effect of the first normal stress difference to the pressure profileFigure 4. Effect of the first normal stress difference to the load capacityFrom the results shown in Fig. 3 and Fig. 4, the pressure and the load capacity of the lubricant increase under the effect of the normal stress. But the increment is not significant. So the effect of the normal stress can be omitted in lubrication calculation. In real lubrication, the effect of the first normal stress difference is to increase the load capacity, omitting the first normal stress difference is a safe design method.From Fig. 4, we may also find that the variation of the load capacity is caused principally by the variation of the viscosity. For example, in Maxwell fluid lubrication, the differential viscosity is affected by the shearing rate greatly. The variation of the viscosity is the key factor to affect the lubrication effect, and the effect of the first normal stress difference is to increase the load capacity in small variation range.The effect of the first normal stress difference is affected by two factors. One is the material dynamic property. From equation (11), the first normal stress difference is determined by the natural frequency and the relaxtion time of non-Newtonian fluid. Also, the differential viscosity is affected by the shearing rate.The other factor is the normal velocity. In Eq. (16), the function of the first normal stress difference is constrained by the normal velocity v. Commonly in theoretical analysis, the normal velocity is considered small and is omitted compared with principal velocity .The normal velocity weakens the effect of the first normal stress difference. For example, under the parameters used in calculation, the value ofthe dimensionless first normal stress difference is -36, but the calculated normal velocity Uv is no more than 0.0021, so the effect of the second item on the right side of the Eq. (16) is far less than the geometric effect.If the normal velocity is large enough, the effect of the first normal stress difference needs to be considered in the numerical calculation. The pressure profiles under different dimensionless normal velocities are shown in Fig. 5. With the increment of the normal velocity, the effect of the first normal stress difference to the pressure profile become more and more important. When the dimensionless normal stress is 1% of the surface velocity, that is 01.0=v, the increment of the pressure peak is about 5%.Figure 5. Effect of the normal stress difference to the pressure profileNormal velocity sometimess may be considered in the true magnetic head-disk interface lubrication. For example, in the head-disk contact dection experiment [8], the flying height of the slider changes. Also, it has been proved that the lubricant film thickness decreases under flying heads during drive operation[9,10]. The Reynolds equation needs to be modified to include the effect of normal velocity.4.2 Effect of normal velocityThe normal veloctiy not only affects the effect of the first normal stress difference, but also causes extrusion effect [11]. With the item of the extrusion effect, the Reynolds equation is expressed as Eq. (17).In Eq. (17),. The normal velocity of the lubricant is expressed as Eq. (18).With Eq. (18) and Eq. (17), the equation used for numerical calculation is shown as Eq. (19).The first item on the right of Eq. (19) represents the effect of the geometry, the second item represents the extrusion effect, the third item represents the effect of thefirst normal stress difference. When, the pressure profiles are shown in Fig.6 considering different effects.Figure 6. Pressure profiles under different effectsIn Fig. 6, 0p represents the pressure profile under the only effect of lubricant geometry. 1p represents the pressure profile under the effect of the geometry and the extrusion effect. 2p represents the pressure profile under the effect of the geometry and the first normal stress difference. And as the normal velocity ofthe lubricant increases, the effect of the first normal stress difference is enlarged, the first normal stress difference should not be omitted according to the results shown in Fig. 6.From the results, we may also find that the extrusion effect is more important than the first normal stress difference. Shown in Fig. 6, the increment of the pressure peak caused by the extrusion effect is far more than the increment caused by the first normal stress difference. So, in head-disk interface lubrication, the effect of the lubricant extrusion and the first normal stress difference should both be considered.5. ConclusionsWhen a non-Newtonian lubricant is used in the magnetic head-disk interface lubrication, its normal stress effect can be calculated by a modified Reynolds equation which includes a function of first normal stress difference. The expression of the first normal stress difference is derived from the Rivlin-Ericksen equation and the momentum equation, together with the coordinates conversion. The effect of the normal stress in the head-disk interface lubrication is not only affected by the non-Newtonian fluid material parameters, but also is affected by the normal velocity.Under the steady laminar flow, the pressure and the load capacity are increased by the effect of the first normal stress difference. But the effect is not significant because of the small normal velocity. Considering both from the theoretical analysis and from the true lubrication calculation, the first normal stress difference can be omitted while the differential viscosity is the principal factor that determines the lubrication effect.When the normal velocity of the lubricant increases because of the movement of the slider, the Reynolds equation is modified again to include the effect of the normalvelocity. The numerical result shows that the effect of the first normal stress difference is enlarged and needs to be calculated in lubrication.AcknowledgmentThis work is supported by the Research Fund for the Doctoral Program of Higher Education,No. 20030003026.References:[1] De Bruvne F.A., Bogy D.B., Numerical simulation of the lubrication of the head-disk interface using a non-Newtonian fluid. ASME Journal of Tribology. 116, 1994, pp: 541-548.[2] Wang L.L., Cheng I. W., An average Reynolds equation for non-Newtonian fluid with application to the lubrication of the magnetic head-disk interface. Tribology transaction, 1997(1), pp: 111-119.[3] Swamy, S.T. Caculated load capacity of non-newtonian lubricants in finite width journal bearings. Wear, 1975, 31:.277-285.[4] Christensen R.M., Saibel E.A. Normal stress effects in viscoelastic fluid lubrication. Journal of Non-Newtonian fluid mech. 1980, 7, pp: 63-75.[5] Harnoy A. Analysis of stress relaxation in elasitco-viscous fluid lubrication of journal bearing. J. of Lubr. Tech. 1978, 100, pp: 287-291.[6] Rivlin R.S. The hydrodynamics of non-newtonian fluids. Proc. Roy. Soc. A, 1948, 193, pp: 261-280[7] Becker E. Simple non-Newtonian fluid flows. Advances in Applied Mech. 1980, 20, pp 212-226.[8] Khurshudov, Andrei; Ivett, Peter,Head–disk contact detection in the hard-disk drives,Wear 255, 2003(8-9), pp. 1314-1322[9] Kawakubo, Y.; Ishii, M.; Sasaki, N,Sliding-pin shapes and lubricant thickness change on a thin-film magnetic disk,Tribology International 2003(8), V ol 36, pp. 593-598[10] Novotny VJ, Baldwinson MA. Lubricant dynamics in sliding and flying. J Appl Phys 1991, V ol 70, pp:5647–52.[11] Pinkus O. Theory of hydrodynamic lubrication, New York : McGraw-Hill Book Co., Inc., 1961.。

基于大数据分析的海上多层油田精细开发实践——以渤海L油田为例

基于大数据分析的海上多层油田精细开发实践——以渤海L油田为例

石油地质与工程2021年3月PETROLEUM GEOLOGY AND ENGINEERING 第35卷第2期文章编号:1673–8217(2021)02–0044–06基于大数据分析的海上多层油田精细开发实践——以渤海L油田为例姜立富,徐中波,张章,李冰,孟云涛(中海石油(中国)有限公司天津分公司渤海石油研究院,天津300459)摘要:针对常规数据分析方法自动化与智能化程度低,难以满足海上油田开发规律深度挖掘的需求,基于标准化数据平台建设和大数据程序开发,在渤海L油田开展了大规模、多专业和复杂生产规律下的油田开发数据的高效分析和应用。

结果表明,通过油井生产数据参数相关性分析和产液规律数据挖掘,可完成油井产液能力影响因素分析;综合储层物性与注采动态等多专业数据,可完成不同井区平面及纵向注水状况分析;结合油藏大数据体构建与机器学习方法,可完成优势产能潜力区域筛选。

研究成果直接应用于油田实际措施优选与方案优化后,达到了提高油田精细开发效果的目的。

关键词:大数据;产液结构;分层调配;井位优化;精细开发中图分类号:TE53 文献标识码:AFine development practice of offshore multi-layer oilfield based on big data analysis--by taking Bohai L oilfield as an exampleJIANG Lifu, XU Zhongbo, ZHANG Zhang, LI Bing, MENG Yuntao(Bohai Petroleum Research Institute, Tianjin Company of CNOOC (China) Co., Ltd., Tianjin 300459, China) Abstract:The conventional data analysis method has low degree of automation and intelligence, which is difficult to meet the needs of deep mining of offshore oilfield development law. Based on the construction of standardized data platform and the development of big data program, the efficient analysis and application of oilfield development data under large-scale, multi professional and complex production rules were carried out in Bohai L oilfield. The results show that through the correlation analysis of oil well production data parameters and the data mining of liquid production law, the analysis of influencing factors of oil well liquid production capacity can be completed. Through the correlation analysis of oil well production data parameters and data mining of liquid production law, the influencing factors of oil well liquid production capacity can be analyzed.Through the comprehensive analysis of reservoir physical properties and injection production performance data, the plane and vertical water injection situation of different well areas can be analyzed. Combined with reservoir big data volume construction and machine learning method, the region selection of dominant productivity potential can be realized. After the research results are directly applied to the optimization of practical measures and schemes, the purpose of improving the effect of oilfield fine development is achieved.Key words:big data; fluid production structure; layered deployment; well location optimization; fine development1 概述L油田位于渤海中部,为岩性–构造层状油藏,纵向上储层数较多,可达40余个小层,由于长期大段合采合注,注采矛盾突出[1]。

国际节能效果测量和认证规程(IPMVP)

国际节能效果测量和认证规程(IPMVP)

3.4.4.2 方案 D: 校验
3.4.4.3 方案 D: 最佳应用பைடு நூலகம்
3.5
遵守 IPMVP
第四章、常见问题
4.1 影响节能效果结果的因素
4.2 评价节能量的不确定性
4.3 最低能耗标准
4.4 最低运行条件
4.5 能源价格
4.6 由第三方进行认证
4.7 排放物交易的数据
4.8 基准值的调整(非常规)
4.9 天气数据
1 IPMVP 委员会约 150 个成员对规程的内容直接负责。另外,大约有同数量的国际专家,作为顾问或审查 者,虽然在此因篇幅原因不能一一列出。诚挚地感谢他们的指导和协助。调整、IEQ、新建筑物、可再生 能源、以及水委员会的成员在第 II 和 III 卷中列出,并在 IPMVP 的网站上列出。
7
可再生能源委员会 联合主席:David Mills, 澳大利亚新威尔士州大学 联合主席:Andy Walker¸美国全国可再生能源实验室 水委员会 联合主席:Tom Horner,美国水资源管理有限公司 联合主席: Warren Leibold, 美国 NYC 环境保护署 技术协调者 Satish Kumar, 美国劳伦斯 伯克利国家实验室 Email: Skumar@, 电话:202-646-7953 我们希望感谢众多的机构,是他们使得 IPMVP 成为可能。特别,我们要感谢美国 能源部的能效和可再生能源办公室的建筑物技术办公室,州和社区项 目,他们提 供了 IPMVP 基本的资金需要,包括这一文件的出版。 因为美国能源部的联邦能源管理项目(FEMP)的赠款,IPMVP 的第一卷、和第二 卷的重新印刷才有可能。诚挚地感谢 FEMP。
2虽然节能措施(ECM)和能效措施(EEM)这两个术语之间存在的差异尚存在一些争论,在本文中两 者可以互用。 3 斜体字标注的术语在第 6.1 章节中进行界定。

2004_72_Numerical_Simulation_of_Coupled_Heat_and_Mass_Transfer_in_Hygroscopic_Porous_Materials_Consi

2004_72_Numerical_Simulation_of_Coupled_Heat_and_Mass_Transfer_in_Hygroscopic_Porous_Materials_Consi

NUMERICAL SIMULATION OF COUPLED HEAT AND MASS TRANSFER IN HYGROSCOPIC POROUS MATERIALS CONSIDERING THE INFLUENCE OF ATMOSPHERIC PRESSURE Li FengzhiDepartment of Engineering Mechanics,Dalian University of Technology,Dalian,China and Institute of Textiles and Clothing,The Polytechnic University of Hong Kong,Hung Hom,Hong KongLi YiInstitute of Textiles and Clothing,The Polytechnic University of Hong Kong,Hung Hom,Hong KongLiu YingxiDepartment of Engineering Mechanics,Dalian University of Technology,Dalian,ChinaLuo ZhongxuanDepartment of Applied Mathematics,Dalian University of Technology,Dalian,ChinaA model of simultaneous transport in hygroscopic porous materials was developed.Water in fabrics is considered to be present in three forms:liquid water in the void space between fibers,bound water in the fibers,and vapor.It is assumed that the heat and mass transport mechanisms include movement of liquid water due to the capillarity and atmospheric pressure gradient,diffusion of vapor within interfibers due to the partial pressure gradient of vapor and total gas pressure gradient,diffusion of vapor into fiber,evaporation,and condensation of water.The moisture diffusion process into hygroscopic porous materials such as wool fabrics was simulated.At normal atmospheric pressure,the results were compared with experimental data on the temperature and water content changes reported in the literature.The distribution of temperature,moisture concentration,liquid water saturation,and atmospheric pressure in the void space between fibers at different boundary conditions are numerically computed and compared.The conclusion is that atmospheric pressure gradient has significant impact on heat and mass transport processes in hygro-scopic porousmaterials.Received 13March 2003;accepted 16July 2003.We would like to thank The Hong Kong Polytechnic University for funding this research throughprojects T612and A188in the ASD in Fashion,Design and Technology Innovation.Address correspondence to Li Yi,Institute of Textiles and Clothing,The Polytechnic University ofHong Kong,Hung Hom,Hong Kong.E-mail:tcliyi@.hkNumerical Heat Transfer,Part B ,45:249–262,2004Copyright #Taylor &Francis Inc.ISSN:1040-7790print/1521-0626online DOI:10.1080/104077904902688142491.INTRODUCTIONThe clothing system plays a very important role in determining the human body core temperature and other human thermal responses because it determines how much of the heat generated in the human body can be exchanged with the environment.With the development of new technology,it is becoming important to know how the human body will behave thermally under different environmental conditions with various clothing systems.In order to obtain a comfortable micro-climate for human body,it is necessary to understand the thermal and moisture transport behavior of the clothing.The first clothing model that describes the mechanism of transient diffusion of heat and moisture transfer into an assembly of hygroscopic textile materials was introduced and analyzed by Henry [1].He developed a set of two differential coupled governing equations for the mass and heat transfer in a small flat piece of clothingNOMENCLATUREc v volumetric heat capacity of the fabric,J =m 3Kc vf volumetric heat capacity of fiber,J =m 3KC fwater vapor concentration in the fibers of the fabric,kg =m 3div divergenceDdiffusivity of vapor,m 2=sD f ðw c ;t Þdiffusion coefficient of water vapor in the fibers grad gradienth c convection mass transfer coefficient,m =sh l $g mass transfer coefficient,m =sh t convection heat transfer coefficient,J =m 2Kh vap latent heat of evaporation =condensation,J =kgk mix thermal conductivity of the fabric,W =m KK intrinsic permeability,m 2K rg relative permeability of water vapor K rw relative permeability of liquid water L thickness of fabricm a mass flux of dry air under gas pressure gradient driving m v mass flux of vapor under gas pressure gradient drivingm D v diffusion mass flux of water vapor m w diffusion mass flux of liquid water M w mole mass of water vapor,kg =mol p a dry air partial pressure,kg =m s 2p c capillary pressure,kg =m s 2p g pressure of gas phase,kg =m s 2p v water vapor partial pressure,kg =m s 2rradius,mS liquid water volumetric saturation (liquid volume =pore volume)S v specific area of the fabric,l =m T temperature of the fabric,K T 1environmental temperature,KWevaporation or condensation flux of water in interfiber void space of fabric,kg =m 3sw c water content of the fibers in the fabric,kg =kg G boundarye porosity of fabricl heat of sorption or desorption water or vapor by fibers,J =kgm g dynamic viscosity of gas,kg =m s m w dynamic viscosity of water,kg =m s r a density of dry air,kg =m 3r w density of liquid water,kg =m 3r vwater vapor concentration in the air filling the interfiber void space,kg =m 3r vs saturated water vapor concentration,kg =m 3r v ?environmental water vapor concentration,kg =m 3Superscripts n previous time n þ1present time Subscripts 0initial value 1left boundary N right boundary ?environment250L.FENGZHI ET AL.SIMULATION OF COUPLED HEAT AND MASS TRANSFER251 material.The analysis of Henry was based on a simplified analytical solution. Downes and Mackay[2]and Watt[3]found experimentally that the sorption of water vapor by wool is a two-stage process.In order to describe the complicated process of the two-stage adsorption behavior in textile materials,Nordon and David [4]presented a model in terms of experimentally adjustable parameters appropriate for thefirst and second stages of moisture sorption.However,their model did not take into account the physical mechanisms of the process.For this reason,Li and Holcombe[5]introduced a new two-stage absorption model to better describe the coupled heat and moisture transport in fabrics.Li and Luo[6]improved the sorption rate equation by assuming that the moisture sorption by a woolfiber can be generally described as a uniform-diffusion equation for both stages of sorption.Luo et al.[7]presented a dynamic model of heat and moisture transfer with sorption and condensation in porous clothing assemblies.Their model considers the effect of water content in the porousfibrous batting on the effective thermal conductivity as well as radiative heat transfer.However,the above-mentioned models ignore the effect of liquid water movement.Fan and Wen[8]reported a model,in which eva-poration and mobile condensates are considered.Li and Zhu[9]reported a new model that takes into account the condensation=evaporation and liquid diffusion by capillary action,which is a function offiber surface energy,contact angle,and fabric pore size distributions.Furthermore,they investigate the effects of the pore size distribution,fiber diameter[10],thickness and porosity[11]on the coupled heat and moisture transfer in porous textiles.Wang Zhong et al.[12]reported a model con-sidering the radiation and conduction heat transfer coupled with liquid water transfer,moisture sorption,and condensation in porous polymer materials.How-ever,in the above references[1–12],the models were based on the mass diffusion due only to the concentration gradient;the effect of the atmospheric pressure gradient on heat and moisture transfer in porous materials was ignored.For example,when a person runs,the atmospheric pressure between the inside and the outside of clothing is different,which may have significant impact on the thermal comfort and protec-tion of clothing.Although the effects of the atmospheric pressure gradient on mass transfer in porous media were considered by previous researchers[13,16],these models are established for nonhygroscopic materials.In order to investigate the influence of atmospheric pressure gradient on heat and mass transfer within hygroscopic textile materials,and to provide insights into the functional design of clothing for windy conditions and active sportswear outdoors,we report a new model by introducing new equations=terms and integrating them.2.MATHEMATICAL FORMULATIONA schematic diagram of the physical mechanisms within fabric is shown in Figure1.To obtain the governing equation,we have made the following assump-tions.First,the clothing is isotropic,and the gas phase is considered to be an ideal gas composed of dry air and water vapor,which are regarded as two miscible species. Second,local thermal equilibrium exists among all phases.This is reasonable,as the pore dimension in the textile material is small.Third,volume changes of thefibers due to changing moisture content are neglected.Fourth,diffusion within thefiber is considered to be so slow that the moisture content at thefiber surface is always insorptive equilibrium with that of the surrounding air.Fifth,water in fabrics is considered to be present in three forms:free liquid water in the void space of interfibers,bound water in the fibers,and water vapor.The mass transport mechanisms are movement of free liquid water due to the capillarity and atmo-spheric pressure gradient,diffusion of vapor in the space between fibers due to the partial pressure gradient of vapor and total gas pressure gradient,and diffusion of vapor in the fiber due to sorption =desorption.Based on the above assumptions,we can establish the following mathematical equations for the coupled heat and mass transfer in the clothing according to the conservation of masses and heat energy:q E ð1ÀS Þr v ½ ½ q t þq C f 1ÀE ðÞÂÃq t ÀE ð1ÀS ÞS v h l $g r vs ðT ÞÀr v ½ ¼div ðÀm D v Þþdiv ðÀm v Þq ½E S r wq t þE ð1ÀS ÞS v h l $g r vs ðT ÞÀr v ½ ¼div ðÀm w Þq E ð1ÀS Þr a ½ ½ q t ¼div ðm D v Þþdiv ðÀm a ÞC v q T q t Àl ð1ÀE Þq C f q t þh vap E ð1ÀS ÞS v h l $g r vs ðT ÞÀr v ½ ¼div ½k mix ðgrad T Þ :8>>>>>>>>>>>>>>>>><>>>>>>>>>>>>>>>>>:ð1ÞHere,E is porosity;S is saturation of free liquid water;S v is the specific area of thefabric;r v is water vapor concentration;r a is the concentration of dry air;r w is the density of liquid water;C f is the water vapor concentration in the fibers;h l $gisFigure 1.Schematic diagram of the physical model.252L.FENGZHI ET AL.the mass transfer coefficient;r vsðTÞis the concentration of saturated water vapor at T;h vap is the latent heat of evaporation=condensation;l is the sorption heat of thefiber;m v is the massflux of vapor under total atmospheric pressure gradient;m Dv isthe diffusion massflux of water vapor;m a is the massflux of dry air under total atmospheric pressure gradient;m w is the diffusion massflux of liquid water;c v is the effective volume heat capacity;and k mix is the effective thermal conductivity of the fabric.Thefirst equation in(1)represents the mass balance of water vapor.Thefirst term on the left side represents vapor storage within the void space of the interfiber; the second term represents water vapor accumulation rate within thefiber,i.e.,the sorption rate offibers;whereas the third term is evaporation or condensationflux of the water in the interfiber void space.The right side of the equation represents water-vapor diffusion.Thefirst term denotes the effect of the vapor diffusion under vapor partial pressure gradient,whereas the second term represents the effect of the vapor diffusion under total gas pressure gradient driving.Similarity,the second equation represents the mass balance of liquid water,and the third equation represents the mass balance of dry air.The fourth equation in(1)represents the energy balance. Thefirst term on the left side of the fourth equation represents energy storage, whereas the second and third terms represent sorption latent heat of water vapor by fibers and the latent heat of liquid water within interfibers,respectively.The right side represents the heat conduction.Sorption and desorption of moisture by thefibers obey the Fickian Law[6]:q C f q t ¼1rqq rrD fðw c;tÞq C fq r!ð2Þwhere D fðw c;tÞis the diffusion coefficient,which has different presentation at dif-ferent stages of moisture sorption;C f is the moisture concentration in thefiber; w c is the bound water content in thefibers.The boundary condition around thefiber is determined by assuming that the moisture concentration at thefiber surface is instantaneously in equilibrium with the surrounding air.Hence,the moisture concentration at thefiber surface is determined by the relative humidity of the surrounding air and temperature,i.e.,[6]C fðR fÞ¼fðRH;TÞð3Þwhere f is the moisture sorption isotherm of thefibers,which can be determined experimentally for differentfibers.The pore sizes infibrous materials are generally small,so that the diffusion of flow is governed by Darcy’s law[1].In the pores formed betweenfibers,we have the massflux of water vapor and dry air under total atmospheric pressure gradient, respectivelym v¼Àr v KK rgm ggradðp gÞÂÃð4Þm a¼Àr a KK rgm ggradðp gÞÂÃð5ÞSIMULATION OF COUPLED HEAT AND MASS TRANSFER253where p g is the atmospheric pressure,K is intrinsic permeability,K rg is relative permeability of water vapor,and m g is the gas-phase dynamic viscosity.For the free liquid water phase,Darcy’s law is expressed as[13]m w¼Àr w KK rwm wgradðp gÀp cÞð6Þwhere K rw is the relative permeability of liquid water,m w is the dynamic viscosity of the liquid water,and p c is the capillary pressure.For the water-vapor phase,diffusion massflux under partial pressure gradient is expressed as[13]m Dv ¼À1:952Â10À7eð1ÀSÞÂT0:8p agradðp vÞð7Þwhere p a is the partial pressure of dry air and p v is the partial pressure of water vapor.In order to generate a solution to the equations mentioned above,we need to specify an initial condition and boundary conditions on the fabric surfaces in terms of concerntration,saturation,temperature,and atmospheric pressure.Initially,a fabric is equilibrated to a given atmospheric pressure,temperature,saturation,and humidity;the temperature,atmosphere,saturation,and concentration are uniform throughout the clothing at known value.T¼T0r v¼r v0S¼S0p g¼p g0C f¼fðRH0;T0Þð8ÞThe boundary conditions arem DvÁ n j GþmÁ n j G1¼h cðr vÀr v1ÞS jG1¼S B;m wÁ n j g12¼qð9ÞK mix grad TÁ n j G¼h tðTÀT1Þp g j G¼p g Gwhere G denotes the boundary,h c is the mass transfer coefficient,h t is the convective heat transfer coefficient,and the subscript1denotes the environment.3.DISCRETIZATION OF THE CONTINUUM EQUATIONTo derive a numerical solution for Eqs.(1),(8),and(9)by using thefinite-volume method,we select a well-distributed control volume with D x,as shown in Figure2.For example,thefirst equation of(1)can be rewritten as follows:A qr vq tþBq Sq tþCþD¼qq xEqr vq xþqq xFq Tq xþqq xGq p gq xð10ÞwhereA¼eð1ÀSÞB¼Àer v C¼e f q C fq tD¼Àeð1ÀSÞh lg S v r vsðTÞÀr v½254L.FENGZHI ET AL.E ¼ð1:952e À7Þe ð1ÀS ÞT 0:8p a RTM wF ¼ð1:952e À7Þe ð1ÀS ÞT 0:8p a R r vM wG ¼r vKK rg m gIf the control volume P is located in the inner of zone,integration of Eq.(10)over the control volume givesZ Or A qr v q t þB q S q t þC þDdx ¼Z Orq q x E qr v q x þq q x F q T q x þq q x G q p gq x !dxð11ÞThenA n p r n þ1vp Àr n v D t D x þB n p S n þ1vp ÀS n vp D t D x þC n pD x þD np D x ¼E qr v q x eÀE qr v w þF q T e ÀF q T w þG q p g e ÀGq p gwð12ÞNow the right-hand side of Eq.(12)can be written asE qr v q x e ÀE qr v q x w þF q T q x e ÀF q T q x w þG q p g q x e ÀGq p g q xw¼E n e r n þ1v E Àr n þ1v pD xÀE n wr n þ1v p Àr n þ1v WD xþF n eT n þ1EÀT n þ1p D xÀF n wT n þ1p ÀT n þ1W D x þG n e p n þ1gE Àp n þ1gp D x ÀG n wp n þ1gpÀp n þ1gW D xð13ÞWith the new symbols,Eq.(12)becomesK w v r n þ1v W þK wt T n þ1W þK wg p n þ1gW ÀK p v r n þ1v P ÀK ps S n þ1P ÀK pt T n þ1P ÀK pg p n þ1gPþK e v r n þ1v EþK et T n þ1EþK eg p n þ1gE¼RRð14ÞFigure 2.Schematic map of control volume.SIMULATION OF COUPLED HEAT AND MASS TRANSFER 255where K wv¼m E n w,K wt¼m F n w,K wg¼m G n w,K pv¼m E n wþm E n eþA n p,K pt¼m F nw þm F n e,K ps¼B n p,K pg¼m G n wþm G n e,K cv¼m E n e,K et¼m F n e,K eg¼m G n e,RR¼ÀA np r nvpÀB n p S n pþC n p D tþD n p D t.If control volume P is located on the left boundary,we consider that the integration area is half of the inner volume andE qr vq xþFq Tq xþGq p gq xP ¼h cl r nþ1vpÀr vp1ð15ÞThen,we can obtainÀK pv r nþ1vP ÀK ps S nþ1PÀK pt T nþ1PÀK pg p nþ1gPþK ev r nþ1vEþK et T nþ1EþK eg p nþ1gE¼RRð16Þwhere m¼D t=D x2,Z¼D t=D x,K pv¼2m E n eþA n pþ2Z h cl,K pt¼2m F n e, K ps¼B n p,K pg¼2m G n e;K ev¼2m E n e,K et¼2m F n e,K eg¼2m G n e,RR¼ÀA n p r n vpÀB n p S n pþC n pD tþD npD tÀ2Z h el r vp1.Similarly,if control volume P is located on the right boundary:K w v r nþ1n W þK wt T nþ1WþK wg p nþ1gWÀK p v r nþ1n PÀK ps S nþ1PÀK pt T nþ1PÀK pg p nþ1gP¼RRð17Þwhere m¼D t=D x2,Z¼D t=D x,K w v¼2m E n w,K wt¼2m F n w,K wg¼2m G n w, K p v¼2m E n wþA n pþ2Z h cN,K pt¼2m F n w,K ps¼B n p,K pg¼2m G n w;RR¼ÀA n p r n n pÀB n p S npþC n p D tþD n p D tÀ2Z h cN r n p1N,where h cN is the mass transfer coefficient in theright boundary,and r n p1N is the concentration of right environment water vapor.Following the same procedure as for the water-vapor mass conservation,we can derive the discretization equations for liquid water,energy,and dry air.By specifying initial conditions we can calculate the coefficient of the discretization and its right term,the values of water-vapor concentration,liquid water saturation, temperature,and atmospheric pressure at next time,can be obtained.The dis-cretization errors of these schemes are O½ðD xÞ2þðD tÞ2 .And the full implicit scheme is stable without any restriction on D t=D x2.In order to obtain grid independence of the solution,we selected grids of different sizes and did computations.The difference in solution betweenfine and coarse grids with increase of size by2is less than2%.4.NUMERICAL SIMULATION AND DISCUSSIONFor computation,the values of the main parameters are listed as Table1.parison between Theoretical Predictions and Experimental MeasurementsAs reported previously by Li Yi et al.[5],a wool fabric sample measuring 3cm615cm62.96mm was suspended in a cell from an electronic balance,which can measure the weight change of fabric during the sorption process.In the cell the temperature was controlled at293.12Æ2K,and the atmospheric pressure was controlled at1atm.The relative humidity was produced by aflow-divider humidity 256L.FENGZHI ET AL.generator,which can change the relative humidity (RH)from 0%to 99%in 1%steps to an accuracy of Æ0.1%of total flow.Fabric was equlibriated in the cell at 0%RH for 90min,then the RH in the cell was rapidly changed from 0%to 99%at time zero,and this RH was maintained for 90min.The surface temperature of the specimen was measured by using a fine thermocouple wire attached to fabric surface.The mean water content of the fabric was calculated on the basis of the weight recorded by a balance.The detailed information on experimental measurements and uncertainty analysis for experimental data was reported previously [5].Table 1.Parameter values for computation ParameterSymbol Unit Value and referenceDensity of a fiber r f kg =m 31,300[14]Volumetric heat C vf kJ =m 3K 373.3þ4661W c þ4.221T [14]capacity of fabric Thermal conductivity of fabricK fab W =m 2K (38.49370.72w c þ0.113w c 270.002w c 3)61073[14]Head of sorption of water vapor by fibers l kJ =kg 1602.5exp(711.72w c )þ2522.0[14]Diffusion coefficientof water vapor in fiberD f ðw c ;t Þm 2=s(1.04þ62.8w c 71342.59w c 2)610714t <540s [14]1:6164f 1Àexp ½À18:16exp ðÀ28:0w c Þ g t !540s [14]Dynamic viscosity of gasm g kg =m s 1.8361075[15]Dynamic viscosity of liquid waterm w kg =m s 1.061073[15]Intrinsic permeability of fabricKm 21.5610713[16]Figure parison of the temperature between theoretical predictions and experimental measurements.SIMULATION OF COUPLED HEAT AND MASS TRANSFER257The new model described in Section2is applied to this experimental condition by specifying corresponding boundary conditions.The comparisons of temperature and bound water content between theoretical prediction and experimental mea-surements[10]are shown in Figures3and4,respectively.Figure3shows tem-perature changes at the surface of the fabric during the dynamic-moisture diffusion process.Since the temperature of surroundings was kept constant at293.15K,in the testing conditions,no external heatflow was provided to the fabric.Hence the temperature rise in the fabric was due purely to the heat released during the moisture-sorption process.The mean error between theoretical prediction and experimental measurements on temperature is0.13%.Figure4shows the mean moisture uptake of the fabric during humidity transient.The mean error on water content of fabric is4.6%.The diameter of thefiber significantly affects its sorption rate.The difference between measured and actual diameter of thefiber and porosity of fabric may be the main sources of paring the predicted temperature and mean moisture uptake with the experimental results,it is obvious that the models are able to predict the moisture sorption of thefibers with satisfactory accuracy.4.2.Influence of Atmospheric Pressure GradientIn order to investigate the effect of the pressure gradient on heat and mass transfer in porous materials,we carried out a series of computational experiments; two cases are selected to show the trends.In the computations,the fabric sample is assumed to be made of woolfiber with thickness L¼3.0mm,and the initial and boundary conditions were specified as follows.The initial conditions wereT0¼298:15K r v0¼0:01kg=m3S0¼0p g0¼1:0135e5PaAt the left (x ¼0)boundary,T 1¼298:15K r v 1¼0:02kg =m 3m w j G ¼0p g G ¼1:0135e 5PaAnd at the right (x ¼L)boundary,T 1¼298:15K ;r v 1¼0:02kg =m 3S ¼0:4p g G ¼2:0135e 5Pa in Case 11:0135e 5Pa in Case 2(The difference between case 1and case 2is the pressure boundary condition at the location x ¼L .Other conditions are the same.The corresponding numerical simu-lations and comparisons are shown in Figures 5–9.Figure 5a shows the atmospheric pressure variation in the fabric with time under the pressure boundary condition of case 1.We can see that the atmospheric pressure is uniform constant initially,then the atmospheric pressure redistributes with the new boundary pressures.The pressure at x ¼L reaches 2atm.Figure 5bFigure 5.Distribution of atmosphericpressure.Figure 6.Distribution of water-vapor concentration.SIMULATION OF COUPLED HEAT AND MASS TRANSFER 259represents the pressure distribution under the condition of case 2.The pressure is uniform because of the same initial and boundary conditions.Figure 6a shows the distribution of the water-vapor concentration in case 1:the water vapor concentration at x ¼0is higher than that at x ¼L .This is because of the effect of atmospheric pressure.The atmospheric pressure gradient,which is the main driving force of water-vapor movement,makes the water vapor diffuse from high pressure (location x ¼L )to low pressure (location x ¼0).Figure 6b shows dis-tribution of the water-vapor concentration in case 2.The water vapor diffuses due to the driving force of water vapor partial pressure gradient only,when the total pressure gradient does not exist.Because of the water-vapor diffusion,the water-vapor concentration in the fabric increases from 0.01to 0.02kg =m 3with time.Then,the concentration of the water vapor reaches equilibrium.Figure 7shows the distribution of water content in fiber.Since the hygro-scopicity of wool is high,water vapor is absorbed.The different water vapor con-centration affects the relative humidity of the water vapor,and then affects the amount of water absorbed by the fiber.From Figures 7a and 7b we can see that the distributions of water content in the fiber agree with the water-vapor concentration in Figures 6a and 6b ,respectively.Figure 7.Water content distribution offiber.Figure8shows the predicted temperature distribution in the wool fabric during the vapor diffusion process.From Figure8,we can see that the temperature rises in the middle layer of the fabric because of the heat released during moisture sorption. At the beginning,the temperature rises quickly,due to the large sorption rate of fiber.Then the temperature decreases gradually,with time reaching equilibrium with the environmental temperature because of much lower sorption rate and heat exchange with environment.At the beginning,the temperature in case1is lower than that in case2.The reason is that the atmosphere pressure gradient affects the dis-tribution of the water-vapor concentration.The different water-vapor concentration affects the amount of water absorbed by thefiber,which leads to different sorption heat.Most of the water content of thefibers in the fabric,except at the boundary x¼L in case1,is lower than that in case2(see Figure7),so the temperature in case1 is lower than that in case2.From Figures8a and8b we also can see the temperature at location x¼L is larger than that at location x¼0in thefirst stage.This is because there is much more liquid water at location x¼L than that at x¼0,and the conductivity of liquid water is greater than that of fabric.Figure9shows the distribution of liquid water saturation during the process of liquid water diffusion into the wool fabric by capillary action.We can see that the liquid water diffuses from side x¼L to x¼0.The reason is that the diffusion potential of the liquid water is liquid water pressure,which equals the difference between atmospheric pressure and capillary pressure.The capillary pressure in the region of high saturation of liquid water(x¼L)is lower than that in the region of low saturation of liquid water(x¼0).In case1,the atmospheric pressure at x¼L is higher than that at x¼0.And in case2,the atmospheric pressure at x¼0is equal to that at x¼L.So the total liquid water driving potential at location x¼L is higher than that at location x¼0in both case1and case2.From Figures9a and9b we can see that the difference in the distributions of liquid water saturation between case1 and case2is not large in this computational condition.5.CONCLUSIONIn this article,we report a new model of heat and moisture transfer inhygroscopic porous textile materials,considering the influence of the atmospheric。

New Perspectives on the Structure of Graphitic Carbons

New Perspectives on the Structure of Graphitic Carbons

Critical Reviews in Solid State and Materials Sciences,30:235–253,2005 Copyright c Taylor and Francis Inc.ISSN:1040-8436printDOI:10.1080/10408430500406265New Perspectives on the Structure of Graphitic CarbonsPeter J.F.Harris∗Centre for Advanced Microscopy,University of Reading,Whiteknights,Reading,RG66AF,UKGraphitic forms of carbon are important in a wide variety of applications,ranging from pollutioncontrol to composite materials,yet the structure of these carbons at the molecular level ispoorly understood.The discovery of fullerenes and fullerene-related structures such as carbonnanotubes has given a new perspective on the structure of solid carbon.This review aims toshow how the new knowledge gained as a result of research on fullerene-related carbons canbe applied to well-known forms of carbon such as microporous carbon,glassy carbon,carbonfibers,and carbon black.Keywords fullerenes,carbon nanotubes,carbon nanoparticles,non-graphitizing carbons,microporous carbon,glassy carbon,carbon black,carbonfibers.Table of Contents INTRODUCTION (235)FULLERENES,CARBON NANOTUBES,AND CARBON NANOPARTICLES (236)MICROPOROUS(NON-GRAPHITIZING)CARBONS (239)Background (239)Early Models (241)Evidence for Fullerene-Like Structures in Microporous Carbons (242)New Models for the Structure of Microporous Carbons (242)Carbonization and the Structural Evolution of Microporous Carbon (243)GLASSY CARBON (244)CARBON FIBERS (245)CARBON BLACK (248)Background (248)Structure of Carbon Black Particles (249)Effect of High-Temperature Heat Treatment on Carbon Black Structure (250)CONCLUSIONS (250)ACKNOWLEDGMENTS (251)REFERENCES (251)INTRODUCTIONUntil quite recently we knew for certain of just two allotropes of carbon:diamond and graphite.The vast range of carbon ma-∗E-mail:p.j.f.harris@ terials,both natural and synthetic,which have more disordered structures have traditionally been considered as variants of one or other of these two allotropes.Because the great majority of these materials contain sp2carbon rather than sp3carbon,their struc-tures have been thought of as being made up from tiny fragments235236P.J.F.HARRISFI G.1.(a)Model of PAN-derived carbon fibres from the work of Crawford and Johnson,1(b)model of a non-graphitizing carbon by Ban and colleagues.2of crystalline graphite.Examples of models for the structures of carbons in which the basic elements are graphitic are reproduced in Figure 1.The structure shown in Figure 1(a)is a model for the structure of carbon fibers suggested by Crawford and Johnson in 1971,1whereas 1(b)shows a model for non-graphitizing car-bon given by Ban and colleagues in 1975.2Both structures are constructed from bent or curved sheets of graphite,containing exclusively hexagonal rings.Although these models probably provide a good first approximation of the structures of these car-bons,in many cases they fail to explain fully the properties of the materials.Consider the example of non-graphitizing carbons.As the name suggests,these cannot be transformed into crystalline graphite even at temperatures of 3000◦C and above.I nstead,high temperature heat treatments transform them into structures with a high degree of porosity but no long-range crystalline order.I n the model proposed by Ban et al.(Figure 1(b)),the structure is made up of ribbon-like sheets enclosing randomly shaped voids.It is most unlikely that such a structure could retain its poros-ity when subjected to high temperature heat treatment—surface energy would force the voids to collapse.The shortcomings of this and other “conventional”models are discussed more fully later in the article.The discovery of the fullerenes 3−5and subsequently of re-lated structures such as carbon nanotubes,6−8nanohorns,9,10and nanoparticles,11has given us a new paradigm for solid car-bon structures.We now know that carbons containing pentago-nal rings,as well as other non-six-membered rings,among the hexagonal sp 2carbon network,can be highly stable.This new perspective has prompted a number of groups to take a fresh look at well-known forms of carbon,to see whether any evidence can be found for the presence of fullerene-like structures.12−14The aim of this article is to review this new work on the structure of graphitic carbons,to assess whether models that incorporate fullerene-like elements could provide a better basis for under-standing these materials than the conventional models,and to point out areas where further work is needed.The carbon ma-terials considered include non-graphitizing carbon,glassy car-bon,carbon fibers,and carbon black.The article begins with an outline of the main structural features of fullerenes,carbon nanotubes,and carbon nanoparticles,together with a brief dis-cussion of their stability.FULLERENES,CARBON NANOTUBES,AND CARBON NANOPARTICLESThe structure of C 60,the archetypal fullerene,is shown in Figure 2(a).The structure consists of twelve pentagonal rings and twenty hexagons in an icosahedral arrangement.I t will be noted that all the pentagons are isolated from each other.This is important,because adjacent pentagonal rings form an unstable bonding arrangement.All other closed-cage isomers of C 60,and all smaller fullerenes,are less stable than buck-minsterfullerene because they have adjacent pentagons.For higher fullerenes,the number of structures with isolated pen-tagonal rings increases rapidly with size.For example,C 100has 450isolated-pentagon isomers.16Most of these higher fullerenes have low symmetry;only a very small number of them have the icosahedral symmetry of C 60.An example of a giant fullerene that can have icosahedral symmetry is C 540,as shown in Figure 2(b).There have been many studies of the stability of fullerenes as a function of size (e.g.,Refs.17,18).These show that,in general,stability increases with size.Experimentally,there is evidence that C 60is unstable with respect to large,multiwalled fullerenes.This was demonstrated by Mochida and colleagues,who heated C 60and C 70in a sublimation-limiting furnace.19They showed that the cage structure broke down at 900◦C–1000◦C,although at 2400◦C fullerene-like “hollow spheres”with diameters in the range 10–20nm were formed.We now consider fullerene-related carbon nanotubes,which were discovered by Iijima in 1991.6These consist of cylinders of graphite,closed at each end with caps that contain precisely six pentagonal rings.We can illustrate their structure by considering the two “archetypal”carbon nanotubes that can be formed by cutting a C 60molecule in half and placing a graphene cylinder between the two halves.Dividing C 60parallel to one of the three-fold axes results in the zig-zag nanotube shown in Figure 3(a),whereas bisecting C 60along one of the fivefold axes produces the armchair nanotube shown in Figure 3(b).The terms “zig-zag”and “armchair”refer to the arrangement of hexagons around the circumference.There is a third class of structure in which the hexagons are arranged helically around the tube axis.Ex-perimentally,the tubes are generally much less perfect than the idealized versions shown in Figure 3,and may be eitherNEW PERSPECTIVES ON GRAPHITIC CARBONS STRUCTURE237FI G.2.The structure of (a)C 60,(b)icosahedral C 540.15multilayered or single-layered.Figure 4shows a high resolu-tion TEM image of multilayered nanotubes.The multilayered tubes range in length from a few tens of nm to several microns,and in outer diameter from about 2.5nm to 30nm.The end-caps of the tubes are sometimes symmetrical in shape,but more often asymmetric.Conical structures of the kind shown in Fig-ure 5(a)are commonly observed.This type of structure is be-lieved to result from the presence of a single pentagon at the position indicated by the arrow,with five further pentagons at the apex of the cone.Also quite common are complex cap struc-tures displaying a “bill-like”morphology such as thatshownFI G.3.Drawings of the two nanotubes that can be capped by one half of a C 60molecule.(a)Zig-zag (9,0)structure,(b)armchair (5,5)structure.20in Figure 5(b).21This structure results from the presence of a single pentagon at point “X”and a heptagon at point “Y .”The heptagon results in a saddle-point,or region of negative curvature.The nanotubes first reported by Iijima were prepared by va-porizing graphite in a carbon arc under an atmosphere of helium.Nanotubes produced in this way are invariably accompanied by other material,notably carbon nanoparticles.These can be thought of as giant,multilayered fullerenes,and range in size from ∼5nm to ∼15nm.A high-resolution image of a nanopar-ticle attached to a nanotube is shown in Figure 6(a).22In this238P.J.F.HARRISFI G.4.TEM image of multiwalled nanotubes.case,the particle consists of three concentric fullerene shells.A more typical nanoparticle,with many more layers,is shown in Figure 6(b).These larger particles are probably relatively im-perfect instructure.FI G.5.I mages of typical multiwalled nanotube caps.(a)cap with asymmetric cone structure,(b)cap with bill-like structure.21Single-walled nanotubes were first prepared in 1993using a variant of the arc-evaporation technique.23,24These are quite different from multilayered nanotubes in that they generally have very small diameters (typically ∼1nm),and tend to be curledNEW PERSPECTIVES ON GRAPHITIC CARBONS STRUCTURE239FI G.6.I mages of carbon nanoparticles.(a)small nanoparticle with three concentric layers on nanotube surface,22(b)larger multilayered nanoparticle.and looped rather than straight.They will not be considered further here because they have no parallel among well-known forms of carbon discussed in this article.The stability of multilayered carbon nanotubes and nanopar-ticles has not been studied in detail experimentally.However,we know that they are formed at the center of graphite electrodes during arcing,where temperatures probably approach 3000◦C.I t is reasonable to assume,therefore,that nanotubes and nanopar-ticles could withstand being re-heated to such temperatures (in an inert atmosphere)without significant change.MICROPOROUS (NON-GRAPHITIZING)CARBONS BackgroundIt was demonstrated many years ago by Franklin 25,26that carbons produced by the solid-phase pyrolysis of organic ma-terials fall into two distinct classes.The so-called graphitizing carbons tend to be soft and non-porous,with relatively high den-sities,and can be readily transformed into crystalline graphite by heating at temperatures in the range 2200◦C–3000◦C.I n con-trast,“non-graphitizing”carbons are hard,low-densitymateri-FI G.7.(a)High resolution TEM image of carbon prepared by pyrolysis of sucrose in nitrogen at 1000◦C,(b)carbon prepared bypyrolysis of anthracene at 1000◦C.I nsets show selected area diffraction patterns.30als that cannot be transformed into crystalline graphite even at temperatures of 3000◦C and above.The low density of non-graphitizing carbons is a consequence of a microporous struc-ture,which gives these materials an exceptionally high internal surface area.This high surface area can be enhanced further by activation,that is,mild oxidation with a gas or chemical pro-cessing,and the resulting “activated carbons”are of enormous commercial importance,primarily as adsorbents.27−29The distinction between graphitizing and non-graphitizing carbons can be illustrated most clearly using transmission elec-tron microscopy (TEM).Figure 7(a)shows a TEM image of a typical non-graphitizing carbon prepared by the pyrolysis of sucrose in an inert atmosphere at 1000◦C.30The inset shows a diffraction pattern recorded from an area approximately 0.25µm in diameter.The image shows the structure to be disordered and isotropic,consisting of tightly curled single carbon layers,with no obvious graphitization.The diffraction pattern shows symmetrical rings,confirming the isotropic structure.The ap-pearance of graphitizing carbons,on the other hand,approxi-mates much more closely to that of graphite.This can be seen in the TEM micrograph of a carbon prepared from anthracene,240P.J.F.HARRI Swhich is shown in Figure 7(b).Here,the structure contains small,approximately flat carbon layers,packed tightly together with a high degree of alignment.The fragments can be considered as rather imperfect graphene sheets.The diffraction pattern for the graphitizing carbon consists of arcs rather than symmetrical rings,confirming that the layers are preferentially aligned along a particular direction.The bright,narrow arcs in this pattern correspond to the interlayer {0002}spacings,whereas the other reflections appear as broader,less intense arcs.Transmission electron micrographs showing the effect of high-temperature heat treatments on the structure of non-graphitizing and graphitizing carbons are shown in Figure 8(note that the magnification here is much lower than for Figure 7).I n the case of the non-graphitizing carbon,heating at 2300◦C in an inert atmosphere produces the disordered,porous material shown in Figure 8(a).This structure is made up of curved and faceted graphitic layer planes,typically 1–2nm thick and 5–15nm in length,enclosing randomly shaped pores.A few somewhat larger graphite crystallites are present,but there is no macroscopic graphitization.n contrast,heat treatment of the anthracene-derived carbon produces large crystals of highly or-dered graphite,as shown in Figure 8(b).Other physical measurements also demonstrate sharp dif-ferences between graphitizing and non-graphitizing carbons.Table 1shows the effect of preparation temperature on the sur-face areas and densities of a typical graphitizing carbon prepared from polyvinyl chloride,and a non-graphitizing carbon prepared from cellulose.31It can be seen that the graphitizing carbon pre-pared at 700◦C has a very low surface area,which changes lit-tle for carbons prepared at higher temperatures,up to 3000◦C.The density of the carbons increases steadily as thepreparationFI G.8.Micrographs of (a)sucrose carbon and (b)anthracene carbon following heat treatment at 2300◦C.TABLE 1Effect of temperature on surface areas and densities of carbonsprepared from polyvinyl chloride and cellulose 31(a)Surface areas Specific surface area (m 2/g)for carbons prepared at:Starting material 700◦C 1500◦C 2000◦C 2700◦C 3000◦C PVC 0.580.210.210.710.56Cellulose 4081.601.172.232.25(b)Densities Helium density (g/cm 3)for carbons prepared at:Starting material 700◦C 1500◦C 2000◦C 2700◦C 3000◦C PVC 1.85 2.09 2.14 2.21 2.26Cellulose1.901.471.431.561.70temperature is increased,reaching a value of 2.26g/cm 3,which is the density of pure graphite,at 3000◦C.The effect of prepara-tion temperature on the non-graphitizing carbon is very different.A high surface area is observed for the carbon prepared at 700◦C (408m 2/g),which falls rapidly as the preparation temperature is increased.Despite this reduction in surface area,however,the density of the non-graphitizing carbon actually falls when the temperature of preparation is increased.This indicates that a high proportion of “closed porosity”is present in the heat-treated carbon.NEW PERSPECTIVES ON GRAPHITIC CARBONS STRUCTURE241FI G.9.Franklin’s representations of(a)non-graphitizing and(b)graphitizing carbons.25Early ModelsThefirst attempt to develop structural models of graphitizingand non-graphitizing carbons was made by Franklin in her1951paper.25In these models,the basic units are small graphitic crys-tallites containing a few layer planes,which are joined togetherby crosslinks.The precise nature of the crosslinks is not speci-fied.An illustration of Franklin’s models is shown in Figure9.Using these models,she put forward an explanation of whynon-graphitizing carbons cannot be converted by heat treatmentinto graphite,and this will now be summarized.During car-bonization the incipient stacking of the graphene sheets in thenon-graphitizing carbon is largely prevented.At this stage thepresence of crosslinks,internal hydrogen,and the viscosity ofthe material is crucial.The resulting structure of the carbon(at ∼1000◦C)consists of randomly ordered crystallites,held to-gether by residual crosslinks and van der Waals forces,as inFigure9(a).During high-temperature treatment,even thoughthese crosslinks may be broken,the activation energy for themotion of entire crystallites,required for achieving the struc-ture of graphite,is too high and graphite is not formed.Onthe other hand,the structural units in a graphitizing carbon areapproximately parallel to each other,as in Figure9(b),and thetransformation of such a structure into crystalline graphite wouldbe expected to be relatively facile.Although Franklin’s ideason graphitizing and non-graphitizing carbons may be broadlycorrect,they are in some regards incomplete.For example,thenature of the crosslinks between the graphitic fragments is notspecified,so the reasons for the sharply differing properties ofgraphitizing and non-graphitizing carbons is not explained.The advent of high-resolution transmission electron mi-croscopy in the early1970s enabled the structure of non-graphitizing carbons to be imaged directly.n a typical study,Ban,Crawford,and Marsh2examined carbons prepared frompolyvinylidene chloride(PVDC)following heat treatments attemperatures in the range530◦C–2700◦C.I mages of these car-bons apparently showed the presence of curved and twistedgraphite sheets,typically two or three layer planes thick,enclos-ing voids.These images led Ban et al.to suggest that heat-treatednon-graphitizing carbons have a ribbon-like structure,as shownin Figure1(b).This structure corresponds to a PVDC carbonheat treated at1950◦C.This ribbon-like model is rather similar to an earlier model of glassy carbon proposed by Jenkins andKawamura.32However,models of this kind,which are intendedto represent the structure of non-graphitizing carbons follow-ing high-temperature heat treatment,have serious weaknesses,as noted earlier.Such models consist of curved and twistedgraphene sheets enclosing irregularly shaped pores.However,graphene sheets are known to be highlyflexible,and wouldtherefore be expected to become ever more closely folded to-gether at high temperatures,in order to reduce surface energy.Indeed,tightly folded graphene sheets are quite frequently seenin carbons that have been exposed to extreme conditions.33Thus,structures like the one shown in Figure1(b)would be unlikelyto be stable at very high temperatures.It has also been pointed out by Oberlin34,35that the modelsput forward by Jenkins,Ban,and their colleagues were basedon a questionable interpretation of the electron micrographs.In most micrographs of partially graphitized carbons,only the {0002}fringes are resolved,and these are only visible when they are approximately parallel to the electron beam.Therefore,such images tend to have a ribbon-like appearance.However,because only a part of the structure is being imaged,this appear-ance can be misleading,and the true three-dimensional structuremay be more cagelike than ribbon-like.This is a very importantpoint,and must always be borne in mind when analyzing imagesof graphitic carbons.Oberlin herself believes that all graphiticcarbons are built up from basic structural units,which comprisesmall groups of planar aromatic structures,35but does not appearto have given a detailed explanation for the non-graphitizabilityof certain carbons.The models of non-graphitizing carbons described so farhave assumed that the carbon atoms are exclusively sp2and arebonded in hexagonal rings.Some authors have suggested thatsp3-bonded atoms may be present in these carbons(e.g.,Refs.36,37),basing their arguments on an analysis of X-ray diffrac-tion patterns.The presence of diamond-like domains would beconsistent with the hardness of non-graphitizing carbons,andmight also explain their extreme resistance to graphitization.Aserious problem with these models is that sp3carbon is unsta-ble at high temperatures.Diamond is converted to graphite at1700◦C,whereas tetrahedrally bonded carbon atoms in amor-phousfilms are unstable above about700◦C.Therefore,the242P.J.F.HARRI Spresence of sp 3atoms in a carbon cannot explain the resistance of the carbon to graphitization at high temperatures.I t should also be noted that more recent diffraction studies of non-graphitizing carbons have suggested that sp 3-bonded atoms are not present,as discussed further in what follows.Evidence for Fullerene-Like Structures in Microporous CarbonsThe evidence that microporous carbons might have fullerene-related structures has come mainly from high-resolution TEM studies.The present author and colleagues initiated a series of studies of typical non-graphitizing microporous carbons using this technique in the mid 1990s.30,38,39The first such study in-volved examining carbons prepared from PVDC and sucrose,after heat treatments at temperatures in the range 2100◦C–2600◦C.38The carbons subjected to very high temperatures had rather disordered structures similar to that shown in Figure 8(a).Careful examination of the heated carbons showed that they often contained closed nanoparticles;examples can be seen in Figure 10.The particles were usually faceted,and often hexagonal or pentagonal in shape.Sometimes,faceted layer planes enclosed two or more of the nanoparticles,as shown in Figure 10(b).Here,the arrows indicate two saddle-points,similar to that shown in Figure 5(b).The closed nature of the nanoparticles,their hexagonal or pentagonal shapes,and other features such as the saddle-points strongly suggest that the parti-cles have fullerene-like structures.I ndeed,in many cases the par-ticles resemble those produced by arc-evaporation in a fullerene generator (see Figure 6)although in the latter case the particles usually contain many more layers.The observation of fullerene-related nanoparticles in the heat treated carbons suggested that the original,freshly prepared car-bons may also have had fullerene-related structures (see next section).However,obtaining direct evidence for this is diffi-cult.High resolution electron micrographs of freshly prepared carbons,such as that shown in Figure 7(a),are usuallyratherFI G.10.(a)Micrograph showing closed structure in PVDC-derived carbon heated at 2600◦C,(b)another micrograph of same sample,with arrows showing regions of negative curvature.38featureless,and do not reveal the detailed structure.Occasion-ally,however,very small closed particles can be found in the carbons.30The presence of such particles provides circumstan-tial evidence that the surrounding carbon may have a fullerene-related structure.Direct imaging of pentagonal rings by HRTEM has not yet been achieved,but recent developments in TEM imaging techniques suggest that this may soon be possible,as discussed in the Conclusions.As well as high-resolution TEM,diffraction methods have been widely applied to microporous and activated carbons (e.g.,Refs.40–44).However,the interpretation of diffraction data from these highly disordered materials is not straightforward.As already mentioned,some early X-ray diffraction studies were interpreted as providing evidence for the presence of sp 3-bonded atoms.More recent neutron diffraction studies have suggested that non-graphitizing carbons consist entirely of sp 2atoms.40It is less clear whether diffraction methods can establish whether the atoms are bonded in pentagonal or hexagonal rings.Both Petkov et al .42and Zetterstrom and colleagues 43have interpreted neutron diffraction data from nanoporous carbons in terms of a structure containing non-hexagonal rings,but other interpreta-tions may also be possible.Raman spectroscopy is another valuable technique for the study of carbons.45Burian and Dore have used this method to analyze carbons prepared from sucrose,heat treated at tem-peratures from 1000◦C–2300◦C.46The Raman spectra showed clear evidence for the presence of fullerene-and nanotube-like elements in the carbons.There was also some evidence for fullerene-like structures in graphitizing carbons prepared from anthracene,but these formed at higher temperatures and in much lower proportions than in the non-graphitizing carbons.New Models for the Structure of Microporous Carbons Prompted by the observations described in the previous section,the present author and colleagues proposed a model for the structure of non-graphitizing carbons that consists ofNEW PERSPECTIVES ON GRAPHITIC CARBONS STRUCTURE243FI G.11.Schematic illustration of a model for the structure of non-graphitizing carbons based on fullerene-like elements.discrete fragments of curved carbon sheets,in which pentagons and heptagons are dispersed randomly throughout networks of hexagons,as illustrated in Figure11.38,39The size of the micropores in this model would be of the order of0.5–1.0nm, which is similar to the pore sizes observed in typical microp-orous carbons.The structure has some similarities to the“ran-dom schwarzite”network put forward by Townsend and col-leagues in1992,47although this was not proposed as a model for non-graphitizing carbons.I f the model we have proposed for non-graphitizing carbons is correct it suggests that these carbons are very similar in structure to fullerene soot,the low-density, disordered material that forms on walls of the arc-evaporation vessel and from which C60and other fullerenes may be ex-tracted.Fullerene soot is known to be microporous,with a sur-face area,after activation with carbon dioxide,of approximately 700m2g−1,48and detailed analysis of high resolution TEM mi-crographs of fullerene soot has shown that these are consis-tent with a structure in which pentagons and heptagons are dis-tributed randomly throughout a network of hexagons.49,50It is significant that high-temperature heat treatments can transform fullerene soot into nanoparticles very similar to those observed in heated microporous carbon.51,52Carbonization and the Structural Evolutionof Microporous CarbonThe process whereby organic materials are transformed into carbon by heat treatment is not well understood at the atomic level.53,54In particular,the very basic question of why some organic materials produce graphitizing carbons and others yield non-graphitizing carbons has not been satisfactorily answered. It is known,however,that both the chemistry and physical prop-erties of the precursors are important in determining the class of carbon formed.Thus,non-graphitizing carbons are formed, in general,from substances containing less hydrogen and more oxygen than graphitizing carbons.As far as physical properties are concerned,materials that yield graphitizing carbons usu-ally form a liquid on heating to temperatures around400◦C–500◦C,whereas those that yield non-graphitizing carbons gen-erally form solid chars without melting.The liquid phase pro-duced on heating graphitizing carbons is believed to provide the mobility necessary to form oriented regions.However,this may not be a complete explanation,because some precursors form non-graphitizing carbons despite passing through a liquid phase.The idea that non-graphitizing carbons contain pentagons and other non-six-membered rings,whereas graphitizing car-bons consist entirely of hexagonal rings may help in understand-ing more fully the mechanism of carbonization.Recently Kumar et al.have used Monte Carlo(MC)simulations to model the evo-lution of a polymer structure into a microporous carbon structure containing non-hexagonal rings.55They chose polyfurfuryl al-cohol,a well-known precursor for non-graphitizing carbon,as the starting material.The polymer was represented as a cubic lattice decorated with the repeat units,as shown in Figure12(a). All the non-carbon atoms(i.e.,hydrogen and oxygen)were then removed from the polymer and this network was used in the。

油脂的工作锥入度的英语

油脂的工作锥入度的英语

油脂的工作锥入度的英语The Penetration of Lubricating OilsLubricating oils play a crucial role in the smooth and efficient operation of various mechanical systems. One of the key properties of these oils is their ability to penetrate and spread throughout the surfaces they are intended to protect. This property, known as the "penetration" of the oil, is a crucial factor in determining the effectiveness of the lubrication process.Penetration is a measure of the oil's ability to seep into the microscopic pores and crevices of the surfaces it is applied to. The higher the penetration, the better the oil can reach and lubricate the critical components of a mechanical system. This is particularly important in applications where the surfaces being lubricated are rough, uneven, or have tight clearances, such as in engines, gearboxes, and bearings.The penetration of a lubricating oil is influenced by a variety of factors, including the oil's viscosity, surface tension, and molecularstructure. Viscosity, which is a measure of the oil's resistance to flow, plays a crucial role in determining the oil's ability to penetrate. Generally, lower viscosity oils have better penetration characteristics, as they are able to flow more easily into the small spaces and gaps within the mechanical system.Surface tension is another important factor that affects the penetration of lubricating oils. Surface tension is the force that acts on the surface of a liquid, causing it to resist being pulled apart. Oils with lower surface tension are better able to spread and penetrate the surfaces they are applied to, as they can more easily overcome the adhesive forces that hold the oil in place.The molecular structure of the oil also plays a role in its penetration ability. Oils with smaller, more linear molecules tend to have better penetration characteristics than those with larger, more complex molecules. This is because the smaller molecules can more easily fit into the microscopic pores and crevices of the surfaces being lubricated.In addition to these physical properties, the application method and the condition of the surfaces being lubricated can also affect the penetration of the oil. For example, applying the oil under pressure or using specialized application techniques, such as spraying or dipping, can help to improve the oil's ability to penetrate thesurfaces.The importance of oil penetration cannot be overstated, as it directly impacts the performance and longevity of mechanical systems. When an oil is able to effectively penetrate and spread throughout the surfaces it is intended to lubricate, it can help to reduce friction, wear, and heat buildup, which are the primary causes of mechanical failure.In conclusion, the penetration of lubricating oils is a critical factor in the performance and reliability of mechanical systems. By understanding the factors that influence oil penetration and using the appropriate lubricants and application methods, engineers and maintenance professionals can help to ensure the smooth and efficient operation of a wide range of mechanical equipment.。

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