An Analytical Energy Consumption Model for Packet Transfer over Wireless Links
Analytical model
An_Intertemporal_General_Equilibrium_Model_of_Asset_Prices(经典3)
An Intertemporal General Equilibrium Model of Asset PricesAuthor(s): John C. Cox, Jonathan E. Ingersoll, Jr., Stephen A. RossSource: Econometrica, Vol. 53, No. 2 (Mar., 1985), pp. 363-384Published by: The Econometric SocietyStable URL: /stable/1911241Accessed: 28/12/2009 13:28Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at/action/showPublisher?publisherCode=econosoc.Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@.The Econometric Society is collaborating with JSTOR to digitize, preserve and extend access to Econometrica.内在地偏微分方程结合。
图表类英语作文模板
When writing an essay based on a chart,graph,or diagram,its essential to follow a structured approach to ensure clarity and coherence.Here is a template you can use as a guide for writing a descriptive and analytical essay on a chart:1.IntroductionBegin by introducing the chart and providing a brief overview of what it represents. Mention the time period or the context in which the data was collected. Example:The chart illustrates the trends in the consumption of various energy sources over the past decade,highlighting the shift towards renewable energy.2.General OverviewProvide a general statement about the overall trend or the most noticeable feature of the chart.This should be a summary that encapsulates the main idea without going into specific figures.Example:A clear trend can be observed in the data,showing a significant increase in the use of renewable energy sources compared to fossil fuels.3.Detailed DescriptionDescribe the data in detail,starting with the most prominent features.Use comparative language to discuss the differences between the data points.Example:In2010,coal was the dominant energy source,accounting for40%of the total energy consumption.However,by2020,this percentage had dropped to25%,while the use of solar and wind energy combined rose from10%to35%.4.Data AnalysisAnalyze the data by discussing possible reasons for the trends or changes observed. Consider economic,social,or environmental factors that might have influenced the data.Example:The decline in coal usage can be attributed to stricter environmental regulations and the increasing cost of mining,while the rise in renewable energy is likely due to technological advancements and government incentives.parison and ContrastCompare and contrast different elements of the chart,such as different energy sources or different time periods.Use comparative structures like while,whereas,and on the other hand.Example:While solar energy has seen a steady increase,wind energy has experienced a more dramatic rise,possibly due to the greater efficiency of wind turbines in certain regions.6.ConclusionSummarize the main points of the essay and restate the overall trend or the most significant findings.End with a final thought or a prediction based on the current trends.Example:In conclusion,the chart clearly demonstrates a shift towards more sustainable energy sources.It is predicted that this trend will continue as technological improvements and environmental concerns drive further adoption of renewable energy.7.Recommendations OptionalIf appropriate,provide recommendations based on the analysis.These could be policy suggestions,further research,or actions for individuals or organizations.Example:It is recommended that governments continue to invest in renewable energy infrastructure and provide incentives for individuals to adopt greener energy solutions.Remember to use a variety of vocabulary and sentence structures to make your essay engaging and academic.Additionally,ensure that your essay is wellorganized,with smooth transitions between paragraphs to guide the reader through your analysis.。
船舶运输行业能源消耗统计及分析方法
附件一:船舶运输行业能源消耗统计及分析方法ICS船舶运输行业能源消耗统计及分析方法The statistical and analytical methods for energy consumption in shipping industryXXXX- XX-XX 发^布XXXX - XX -XX 实施本标准附录A、附录B 为标准性附录。
本标准由中华人民共和国交通部提出并归口。
本标准起草单位:交通部水运科学争论院本标准主要起草人:刖言 (I)1 范围 (1)2 标准性引用文件 (1)3 术语和定义 (1)4 船舶分类 (2)5 能源消耗统计抽样方法 (3)6 能耗指标的统计与计算 (3)7 能源消耗统计分析方法 (5)附表A 〔标准性附录〕:船舶运输行业营运船舶能源消耗报表 (7)附表B 〔标准性附录〕:标准中相关变量的计算方法: (9)船舶运输行业能源消耗统计及分析方法1范围本标准规定了承受典型调查的手段进展船舶运输行业能源消耗统计指标计算及分析方法。
本标准适用于全国、地区及企业的船舶运输能源消耗统计及分析。
本标准统计的船舶为从事水上客、货运输活动的我国企业或私人拥有的营业性机动船舶〔含我国企业或私人拥有的悬挂外国旗的船舶〕,非运输船舶、驳船及农业、渔业生产船舶不包含在本统计范围内。
2标准性引用文件以下文件中的条款通过本标准的引用而成为本标准的条款。
但凡注日期的引用文件,其随后全部的修改单〔不包括订正的内容〕或均不适用于本标准,然而,鼓舞依据本标准达成协议的各方争论是否可使用这些文件的最版本。
凡是不注日期的引用文件,其最版本适用于本标准。
《中华人民共和国统计法》《交通运输综合统计报表制度》3术语和定义本标准米用以下术语和定义。
3.1换算周转量conv erted turno ver客、货周转量是指航运企业在报告期内船舶实际进展旅客运输与货物运输量与标准里程的乘积之和。
计算单位:千吨海里〔公里〕。
人类未来生活中的ai英语作文
人类未来生活中的ai英语作文Here is an essay on the topic of "AI in the Future of Human Life" with more than 1,000 words, written in English without any additional titles or unnecessary punctuation marks.The rapid advancements in artificial intelligence (AI) have ushered in a new era of technological transformation that is poised to reshape the future of human life. As we stand on the cusp of an AI-driven revolution, the integration of this powerful technology into various aspects of our daily lives holds immense promise and profound implications.One of the most significant ways in which AI will impact the future of human life is in the realm of healthcare. AI-powered diagnostic tools and medical imaging analysis systems have the potential to revolutionize the early detection and treatment of diseases. By leveraging machine learning algorithms, these systems can analyze vast amounts of medical data, identify patterns, and provide healthcare professionals with more accurate and timely insights. This could lead to earlier interventions, personalized treatment plans, and improved patient outcomes. Furthermore, AI-driven robotic assistants and autonomous surgical systems could enhance theprecision and efficiency of medical procedures, ultimately enhancing the quality of care and reducing the risk of human error.Beyond the healthcare sector, AI will also play a pivotal role in transforming the way we live, work, and interact with the world around us. In the realm of transportation, self-driving vehicles equipped with AI-powered decision-making algorithms could significantly reduce the number of accidents caused by human error, while also improving traffic flow and reducing congestion. This could lead to more efficient and sustainable transportation systems, ultimately enhancing the quality of life for urban dwellers.In the workplace, AI-powered automation and intelligent systems will reshape the nature of employment. Repetitive and labor-intensive tasks will be increasingly automated, freeing up human workers to focus on more creative, analytical, and strategic roles. This shift will require a fundamental rethinking of education and workforce training, as individuals will need to develop new skills and adapt to the changing job market. However, the integration of AI in the workplace also holds the potential to boost productivity, reduce costs, and enable more personalized and efficient service delivery.The integration of AI into our homes and personal lives will also have a profound impact. Smart home technologies powered by AI can optimize energy consumption, automate household tasks, andprovide personalized recommendations and assistance. Virtual assistants, such as Alexa and Siri, are already becoming ubiquitous, enabling us to control our environments, access information, and manage our schedules with voice commands. As these technologies continue to evolve, they will become increasingly adept at anticipating our needs and preferences, creating a more seamless and personalized experience.Moreover, AI will play a crucial role in addressing some of the most pressing global challenges, such as climate change, resource scarcity, and sustainable development. AI-powered systems can analyze vast amounts of environmental data, identify patterns and trends, and help policymakers and scientists develop more effective strategies for mitigating and adapting to the impacts of climate change. Additionally, AI can optimize the use of resources, improve the efficiency of energy systems, and support the development of renewable energy technologies.However, the integration of AI into our lives is not without its challenges and ethical considerations. Concerns around data privacy, algorithmic bias, and the potential displacement of human jobs will need to be addressed through robust governance frameworks and proactive public policy initiatives. It will be crucial to ensure that the development and deployment of AI technologies are guided by principles of transparency, accountability, and fairness, ensuring thatthe benefits of AI are equitably distributed and the risks are mitigated.As we look to the future, it is clear that AI will be a transformative force that will profoundly shape the way we live, work, and interact with the world around us. While the challenges and complexities of this technological revolution are not to be underestimated, the potential benefits of AI are vast and far-reaching. By embracing this technology with a thoughtful and responsible approach, we can harness the power of AI to enhance the quality of human life, address global challenges, and pave the way for a more sustainable and prosperous future.。
能源管理员岗位职责
能源管理员岗位职责Title: Responsibilities of an Energy ManagerIntroduction:An energy manager plays a crucial role in ensuring efficient and sustainable energy use within an organization. This article will outline the key responsibilities of an energy manager and the skills required to excel in this role.1. Energy Audits and Analysis1.1 Conducting energy audits to identify areas of energy waste and opportunities for improvement.1.2 Analyzing energy consumption data to track trends and make recommendations for energy efficiency measures.1.3 Developing energy management plans and strategies to reduce energy consumption and costs.2. Implementing Energy Efficiency Projects2.1 Identifying and prioritizing energy efficiency projects based on cost-effectiveness and potential energy savings.2.2 Coordinating with internal teams and external vendors to implement energy efficiency measures, such as lighting upgrades or HVAC system optimizations.2.3 Monitoring and evaluating the performance of energy efficiency projects to ensure they meet energy savings targets.3. Regulatory Compliance and Reporting3.1 Staying up-to-date on energy regulations and standards to ensure compliance with local, state, and federal requirements.3.2 Preparing and submitting reports on energy consumption, savings, and greenhouse gas emissions to regulatory agencies and stakeholders.3.3 Collaborating with legal and compliance teams to address any regulatory issues or concerns related to energy management.4. Employee Training and Engagement4.1 Providing training and education to employees on energy conservation practices and the importance of energy efficiency.4.2 Engaging employees in energy-saving initiatives through awareness campaigns, incentives, and recognition programs.4.3 Encouraging a culture of energy efficiency within the organization by promoting sustainable practices and behaviors.5. Performance Monitoring and Continuous Improvement5.1 Monitoring key performance indicators related to energy consumption, costs, and savings to track progress towards energy management goals.5.2 Conducting regular reviews and audits to identify areas for improvement and implement corrective actions.5.3 Continuously seeking opportunities to optimize energy use, reduce waste, and enhance the overall energy performance of the organization.Conclusion:In conclusion, the responsibilities of an energy manager are diverse and require a combination of technical knowledge, analytical skills, and strategic thinking. By effectively managing energy resources and implementing energy efficiency measures, energy managers can help organizations reduce costs, minimize environmental impact, and achieve sustainable energy goals.。
ProceedingsoftheIMechE,PartBJournalofEngineering
Proceedings of the IMechE, Part B:Journal of Engineering Manufacture 《英国机械工程师学会志,B辑:“工程制造杂志”》Issue 3 Mar. 2015序号目次信息1 篇名:Thermal and mechanical effects of high-speed impinging jet in orthogonal machining operations: Experimental, finite elements and analytical investigations高速冲击射流在正交加工中的热机械效应:实验,有限元分析作者:Andrea Bareggi and Garret E O’Donnell2 篇名:A statistical analysis applied for optimal cooling system selection and for a superior surface quality of machined magnesium alloy parts用于镁合金零件优化冷却系统选择的统计分析作者:Bogdan Chirita, Gheorghe Mustea, and Gheorghe Brabie3 篇名:An analytical investigation on the workpiece roundness generation and its perfection strategies in centreless grinding无心磨削的工件圆度的产生及其完善对策研究作者:Qi Cui, Hui Ding, and Kai Cheng4 篇名:A study of the micro-machining process on quartz crystals using an abrasive slurry jet基于磨料浆射流的石英晶体微加工工艺研究作者:Huan Qi, Jingming Fan, and Jun Wang5 篇名:On the use of cyclic shear, bending and uniaxial tension–compression tests to reproduce the cyclic response of sheet metals循环剪切,弯曲和单轴拉压试验,再现金属板材的循环响应作者:Abbas Ghaei, Daniel E Green, Sandrine Thuillier6 篇名:Dimensional variation stream modeling of investment casting process based on state space method基于状态空间法的熔模铸造工艺尺寸变化作者:Changhui Liu, Sun Jin, Xinmin Lai7 篇名:Suspended SiC particle deposition on plastic mold steel surfaces in powder-mixed electrical discharge machining粉末混合电火花加工中的塑料模具钢表面悬浮颗粒沉积作者:Bülent Ekmekci, Fevzi Ulusöz, Nihal Ekmekci8 篇名:Unified variation modeling of sheet metal assembly considering rigid and compliant variations考虑刚性和柔性变化的板料装配统一建模作者:Na Cai, Lihong Qiao, and Nabil Anwer9 篇名:An approach to minimizing surplus parts in selective assembly with genetic algorithm用遗传算法优化选择装配多余零件的方法作者:Cong Lu and Jun-Feng Fei10 篇名:Parameter analysis and identification of the multiple-advanced manufacturing mode diffusion model多先进制造模式扩散模型的参数分析与辨识作者:Chaogai Xue, Haiwang Cao, and Yu Sheng11 篇名:Functional cause analysis of complex manufacturing systems using structure复杂制造系统应用结构的功能原因分析作者:MK Loganathan, Minu Shikha Gandhi, and OP Gandhi12 篇名:A novel artificial ecological niche optimization algorithm for car sequencing problem considering energy consumption考虑能量消耗的汽车排序问题的一种新的人工生态位优化算法作者:Sanqiang Zhang, Daoyuan Yu, Xinyu Shao。
节能审查指南2018
节能审查指南2018The purpose of these guidelines is to provide a comprehensive overview of the energy audit process, from planning and preparation to reporting and implementation. These guidelines are intended to be used by energy auditors and other professionals who are responsible for conducting energy audits.The energy audit process typically involves the following steps:1. Planning and preparation: This step involves gathering information about the facility to be audited, including its energy consumption history, operating hours, and equipment inventory. The auditor will also need to develop a plan for the audit, including the scope of work, the methods to be used, and the schedule for the audit.2. Data collection: This step involves collecting data on the facility's energy consumption. The auditor will usea variety of methods to collect this data, including interviews with facility staff, review of energy bills, and installation of data loggers.3. Data analysis: This step involves analyzing the data collected in the previous step to identify opportunitiesfor energy savings. The auditor will use a variety of analytical tools to perform this analysis, including energy modeling software and statistical analysis.4. Report preparation: This step involves preparing a report that summarizes the findings of the energy audit. The report will typically include a description of the facility, a summary of the energy consumption data, an analysis of the energy savings opportunities, and recommendations for implementing the energy savings measures.5. Implementation: This step involves implementing the energy savings measures that were identified in the energy audit report. The auditor will typically work with the facility staff to develop a plan for implementing themeasures and to track the progress of the implementation.These guidelines are intended to provide a general overview of the energy audit process. The specific steps involved in an energy audit may vary depending on the size and complexity of the facility being audited.中文回答:节能审查指南 2018。
英文文献-一种生产水果发酵饮料的方法
ORIGINAL ARTICLEProduction of Star Fruit Alcoholic Fermented BeverageFla´via de Paula Valim 1•Elizama Aguiar-Oliveira 2•Eliana Setsuko Kamimura 3•Vanessa Dias Alves 1•Rafael Resende Maldonado 1,4Received:19January 2016/Accepted:18May 2016/Published online:28May 2016ÓAssociation of Microbiologists of India 2016Abstract Star fruit (Averrhoa carambola )is a nutritious tropical fruit.The aim of this study was to evaluate the production of a star fruit alcoholic fermented beverage utilizing a lyophilized commercial yeast (Saccharomyces cerevisiae ).The study was conducted utilizing a 23central composite design and the best conditions for the production were:initial soluble solids between 23.8and 25°Brix (g 100g -1),initial pH between 4.8and 5.0and initial con-centration of yeast between 1.6and 2.5g L -1.These conditions yielded a fermented drink with an alcohol content of 11.15°GL (L 100L -1),pH of 4.13–4.22,final yeast concentration of 89g L -1and fermented yield from 82to 94%.The fermented drink also presented low levels of total and volatile acidities.Keywords Star fruit ÁFruit wine ÁFactorial design ÁSaccharomyces cerevisiaeIntroductionThe alcoholic fermentation of fruit can be used for the production of alcoholic drinks and it is commonly realized by yeast such as the Saccharomyces cerevisiae [1,2].In such process occurs the production of ethanol and carbon dioxide,which are obtained by the anaerobic conversion of the sugars naturally contained in the fruit or added to it.According to the Brazilian legislation [3],wine is a drink with alcohol content from 4to 14°GL (L 100L -1)at 20°C,produced from the alcoholic fermentation of healthy,ripe and fresh grapes.The term ‘‘fruit wine’’is applied to alcoholic fermented drinks produced from fruits other than grapes.Any fruit with reasonable amounts of fer-mentable sugars can be utilized as must for the production of wine.The usage of different fruits may lead to the obtain-ment of drinks with different flavors.Therefore,many exotic fruits have been utilized in the production of wine.Carambola or star fruit (Averrhoa carambola )is a tropical fruit originally from Indonesia and India,being very popular in South-eastern Asia,South Pacific and some regions of Eastern Asia.The carambola tree is grown in other countries out from Asia,such as Colombia,Guiana,Dominican Republic,Brazil and the USA [4].The star fruit chemical characteristics depend on climatic factors,the type of soil utilized for cultivation,the fruit ripeness level,etc.Almeida et al.[5]characterized ripe star fruit from north-eastern Brazil and obtained average values of soluble solids and pH of 8.0°Brix and 3.7,respectively.Star fruit is rich in vitamins,oxalic acid,polyphenols,dietary fiber,volatile compounds,etc.Such traits allow innumerable usages for the fruit,as well as providing benefits for the health of the consumers [6,7].The aim of this study was to evaluate the production of a star fruit alcoholic fermented drink incorporating the fruitElectronic supplementary material The online version of this article (doi:10.1007/s12088-016-0601-9)contains supplementary material,which is available to authorized users.&Rafael Resende Maldonadoratafta@.br1Municipal College Professor Franco Montoro (CMPFM),R.dos Estudantes,s.n.,Mogi Guac ¸u,SP 13.843-971,Brazil 2Multidisciplinary Institute on Health (IMS),FederalUniversity of Bahia (UFBA),campus Anı´sio Teixeira (CAT),R.Rio de Contas,58,Vito´ria da Conquista,Bahia 45.029-094,Brazil3Faculty of Zootecnic and Food Engineering (FZEA),University of Sa˜o Paulo (USP),Av.Duque de Caxias,225,13.635-900Pirassununga,Sa˜o Paulo,Brazil 4Technical College of Campinas (COTUCA),University ofCampinas (UNICAMP),R.Jorge Figueiredo Correˆa,735,Pq.Taquaral,Campinas,Sa˜o Paulo 13.087-261,Brazil Indian J Microbiol (Oct–Dec 2016)56(4):476–481DOI 10.1007/s12088-016-0601-9traits in the wine.The effects of the initial concentration of yeast(S.cerevisiae),initial pH and initial sugar concen-tration were evaluated utilizing a23central composite design whereby the fermentation kinetics data and the physico-chemical characteristics of the product were analyzed.Materials and MethodsPreparation of the Star Fruit Pulp and MustFor the star fruit pulp preparation was selected ripe fruits with good appearance,smell and texture.They were bought at a local supermarket in the region of Campinas-SP,Brazil, in March2014.The star fruits were cleaned,cut and blitzed in a blender until a pulp consistency was achieved.There-after,the pulp wasfiltered with be means of a cotton cloth, in order to remove the insoluble solids and then pasteurized at80°C for5min.The pasteurized pulp was transferred to plasticflasks which were closed and left cooling to room temperature and then frozen and stored at-18°C to the moment they were utilized on the must preparation.In order to prepare the must for the fermentation,the star fruit pulp was thawed at room temperature(*25°C)and mixed with saccharose to adjust the concentration of sol-uble solids indicated on Table1(varying between19and 25°Brix).Thereafter,calcium carbonate was added to each test to adjust the pH,also regarding the values indicated on Table1(ranging between4.0and5.0).Alcoholic FermentationThe must obtained was then utilized for the alcoholic fer-mentation.A lyophilized commercial yeast S.cerevisiae (Fermentais Lessaffe GroupÒ)was utilized.The lyophi-lized yeast was rehydrated with a small portion of must and thereafter added to the total must volume(1.0L).All fer-mentations were conducted at room temperature(*25°C), without agitation in glassflasks covered with plasticfilm, in which small orifices were made to facilitate the elimi-nation of the carbon dioxide produced during the fermen-tation process.Each fermentation lasted9days and the soluble solids consumption of the must,as well as its pH, were measured daily.By the end of the fermentation,the alcoholic content and the yeast concentration were also measured.The total,fixed and volatile acidities were also analyzed for each wine obtained.Factorial DesignThe study of the star fruit alcoholic fermentation by S. cerevisiae was conducted utilizing a central composite rotatable design with23factorial points?6star points?3 central points,totaling17tests[8]with independent vari-ables:soluble solids concentration(SS)(19a25°Brix), initial pH(4.0–5.0)and initial yeast concentration(IY)(1.0 a4.0g L).The effects of these three variables on the fer-mentation process responses(alcoholic content(AC),final pH,volumetric fermented yield(VFY)andfinal yeast concentration(FY))as well as on thefinal product(total,fixed and volatile acidities)were evaluated.The results obtained were analyzed by means of the software Statistica 8.0(Statsoft)and the factorial design matrix is shown on Table1.Analytical MethodsThe pH was measured directly with a bench pHmeter(Bel EngineeringÒ,W3B model).The soluble solids (S)(°Brix=g100g-1)were determined with a portable refractometer(InstrutempÒ,model ITREF25). From the S values,and knowing the stoichiometry ratio of sugar consumption/ethanol production(1:4mol of sac-charose per mol of ethanol)it was possible to estimate the alcohol content as the function of the fermentation time.At the end of each fermentation a sample was collected to measure the alcohol content(°GL=L100L-1)in an Alcolyzer equipment(Anton PaarÒ).The conversion fac-tors:fermentable sugars into biomass(Y X/S=dX/-dS), fermentable sugar into alcohol(Y P/S=dP/-dS)and bio-mass into alcohol(Y P/X=dP/dX)were also calculated considering(-dS:g L-1)the fermentable sugar con-sumption,(dX:g L-1)the cell growth,and(dP:g L-1)the alcohol production.Thefixed and total acidities(meq L-1) were measured using titrimetric methods according to Adolfo Lutz Institute[9];the volatile acidity was calcu-lated by the difference between total andfixed acidities. Results and DiscussionThe responses obtained from the central composite rotat-able design applied for the production of star fruit alcoholic fermented by S.cerevisiae can be observed in Table1. From these results,the analysis of variance(ANOVA) (Online Resource1)was performed for each response and second order models were evaluated to explain the process.Considering the ANOVA results for the alcohol content (Online Resource1),it was possible to obtain a significant and predictive codified model with94%confidence (p=0.06),represented by Eq.1,where OH is the alcohol content(°GL),S is the initial soluble solids concentration (°Brix),P is the initial pH and Y is the initial yeast con-centration(g L-1).The resulting surface responses and contour lines can be observed on the Online Resource2.The analysis of thisfigure shows that the best results for the AC,after9days of fermentation,were obtained from the highest soluble solids concentrations,the highest pH and the initial yeast concentration levels between-1 (1.6g L-1)and?1(3.4g L-1).These conditions,pre-dicted by the model,can be verified in Table1,wherein the highest alcohol content were obtained in trials4,8,10and 12,which resulted in values from10.45to11.15°GL.The results obtained in this study were better than others cited in the literature,which also used star fruit as a substrate for alcoholic fermentation.Napahde et al.[10]obtained only 0.2°GL of alcohol after21days of fermentation,while Sibounnavong et al.[11]obtained8.3°GL(average)after 2weeks.In other study,Bridgebassie and Badrie obtained a similar alcohol content(from10.25to11.50°GL)after 4weeks of fermentation using star fruit must pretreated with different concentrations of pectolase and using dif-ferent yeast strains[12].The best conditions(trials4,8,10and12)were selected for the analysis of their consumption of soluble solids and the ethanol production during the fermentation time.The obtained kinetic profiles can be seen in Fig.1.The con-sumption of soluble solids and the ethanol production showed similar profiles for the selected assays,indicating no significant difference among the chosen conditions.It was also possible to see that after5days of fermentation there was a trend for stabilization(Fig.1),with low sugar consumption and little ethanol production.OH¼10:01þ0:61SðÞÀ0:05S2ÀÁþ0:34PðÞÀ0:05S2ÀÁþ0:34PðÞÀ0:05P2ÀÁþ0:02YðÞÀ0:42Y2ÀÁþ0:44SðÞPðÞÀ0:26SðÞYðÞþ0:22PðÞYðÞð1ÞRegarding to thefinal pH of the star fruit alcoholic fermented,it was not possible to obtain a predictive model (p\0.10)to represent the process(Online Resource1).By observing Table1,however,it was possible to notice that the averagefinal pH value,considering the17trials,was 4.05and the initial value was4.50,this reduction is a good indicator for the fermentation process because it indicates that most of the substrate was used for ethanol production and cellular growth and there was little formation of acid. Excessive production of acids may indicate microbial contamination by acetic bacteria,excess oxygen in the fermentation medium or excessive fermentation time. Bridgebassie and Badrie[12]obtained a lowerfinal pH of about3.1in star fruit wine,but these authors applied a pretreatment in the must using1%of citric acid before the fermentation.Similarly,for the volumetric fermented yield,the cor-relation with the independent variables was very low (R2=0.24)and thus,none of the studied variables had aTable1Kinetic parameters for star fruit alcoholic fermentation at room temperature after9days of fermentationRuns SS(°Brix)pH Yeast(g L-1)Alcohol(°GL)Final pH Yield(%v/v)Final yeast(g L-1)120.2(-1) 4.2(-1) 1.6(-1)9.06 3.7485.4783.94223.8(?1) 4.2(-1) 1.6(-1)9.76 3.7785.0054.58320.2(-1) 4.8(?1) 1.6(-1)8.36 4.2390.0065.22423.8(?1) 4.8(?1) 1.6(-1)11.15 4.2294.0089.66520.2(-1) 4.2(-1) 3.4(?1)8.36 3.8285.5088.14623.8(?1) 4.2(-1) 3.4(?1)8.36 3.8790.1074.54720.2(-1) 4.8(?1) 3.4(?1)9.06 4.2491.5063.85823.8(?1) 4.8(?1) 3.4(?1)10.45 4.2190.8070.74919.0(-1.68) 4.5(0) 2.5(0)9.06 4.1178.6091.731025.0(?1.68) 4.5(0) 2.5(0)11.15 4.1381.7489.841122.0(0) 4.0(-1.68) 2.5(0)9.76 3.9483.33131.811222.0(0) 5.0(?1.68) 2.5(0)10.45 4.1975.76167.081322.0(0) 4.5(0) 1.0(-1.68)8.36 4.0583.33144.861422.0(0) 4.5(0) 4.0(?1.68)9.76 4.0082.69121.881522.0(0) 4.5(0) 2.5(0)9.76 4.3587.78190.801622.0(0) 4.5(0) 2.5(0)10.45 4.0075.30213.071722.0(0) 4.5(0) 2.5(0)9.76 4.1077.69190.89Central composite rotatable design23?6star points?3central points for the star fruit alcoholic fermentation by S.cerevisiae after9days at room temperature(*25°C)without agitation.Independent variables are:the soluble solid concentration(S)(°Brix=g100g-1),initial pH and initial yeast concentration(Yeast)(gL-1).Response variables are:thefinal alcohol content(Alcohol)(°GL=L100L-1),final pH,volumetric fermentation yield(Yield)(%v/v)andfinal yeast concentration(Final Yeast)(g L-1)Codified values are presented in parenthesissignificant influence on this response (Online Resource 1).Considering the 17trials (Table 1),the average volumetric fermented yield was 84.6±5.6%(L 100L -1).Volume variations in similar processes are common and can occur due to the consumption of must to produce gas (carbon dioxide)during the fermentation.Regarding to cell growth,i.e.,the final yeast concen-tration,the ANOVA (Online Resource 1)proved that it was possible to obtain a statistically significant and predictive model at 93%of confidence (p =0.07).The complete codified model obtained is represented by Eq.2where FY is the final yeast concentration (g L -1),S is the soluble solid concentration (°Brix),P is the initial pH,Y is the initial yeast concentration (g L -1).The resulting surface response and the contour curves can be observed on the Online Resource 3,in which it appears that the final con-centration of yeast (FY )was the highest in the conditions close to the central points.However,when it comes toalcoholic fermentation process,the best condition for cell growth is not necessarily the best condition for ethanol production.Reddy and Reddy [13]evaluated the effect of different parameters over the S.cerevisiae growth in mango must and it was observed a great influence of temperature;at 25and at 30°C they obtained the highest cell populations within 6and 8days of fermentation,respectively,with an ethanol production 70%smaller at 25°C than at 30°C (41.3g L -1day -1).FY ¼201:32À1:08S ðÞÀ48:35S 2ÀÁþ3:48P ðÞÀ27:56P 2ÀÁÀ2:55Y ðÞÀ33:25Y 2ÀÁþ9:28S ðÞP ðÞÀ0:22S ðÞY ðÞÀ5:56P ðÞY ðÞð2ÞThe conversion factors were also calculated for each trial (Online Resource 4).The ANOVA was also obtained (data not presented),but it was not possible to obtain second order models to explain each of the conversion factors with a reasonable level of confidence.The results (Online Resource 4)indicate that the highest values of Y X/S and Y P/S were obtained in trial 12,however,the highest value of Y P/X was obtained in trial 2.It confirms the pre-viously mentioned suggestion that a greater microbial growth does not necessarily result in a greater ethanol production.Confronting the three conversion factors and the two models obtained (for OH and FY),the best con-ditions for the production of star fruit alcoholic fermented (aiming the highest alcohol content)were those in which conditions where the substrate was better used for con-version into product than cell growth.These conditions correspond to trials 4and 10which presented the highest initial concentrations of soluble solids (23.8–25.0°Brix),the highest initial pH (4.8–5.0)and initial concentrations of yeast in the intermediate level (1.6–2.5g L -1).Regarding to the acidity levels (Online Resource 4),the obtained values were also evaluated by ANOVA (data not presented)but none of the three responses there were sig-nificantly correlations with the independent variables of the process.This means that under all conditions evaluated the resulting acidity values were very similar to each other,a fact that is in agreement with the analysis for the final pH of the fermentation,which had the same behavior.The composition of the acidity of star fruit alcoholic fermented (Online Resource 4)suggests that most of it is related to the fixed acidity (89%)and a small part (11%)to the volatile acidity.This is an excellent result,since excessive volatile acidity may indicate microbial contamination or excess of oxygen in both production and storage of fermented drink.Paul and Sahu [14]obtained star fruit alcoholic fermented with a titratable acidity of around 119meq L -1and,despite of all the other similar parameters obtained,this value is almost 2.5times higher than the average value obtained in our study (48.8meq L -1).Fig.1a Soluble solids (°Brix =g 100g -1)consumption and b production of alcohol (°GL =L 100L -1)in star fruit alcoholic fermentation by S.cerevisiae as a function of the fermentation time.The conditions for the concentration of soluble solids (°Brix)/pH/concentration of yeast (g L -1)were:23.8/4.8/1.6(trial 4);23.8/4.8/3.4(trial 8);25.0/4.5/2.5(trial 10);and 22.0/5.0/2.5(trial 12).The experiments were conducted at room temperature (*25°C)without agitation,for 9days.Lines were used to connect the points and guide the eyesBrazilian law,for instance,specifies some quality stan-dards for grape wines[3],but it does not specify any stan-dard for wines from other paring with the current legislation,star fruit wine obtained showed an average total acidity of48.8±7.7meq L-1similar to the minimum established by Brazilian law(55meq L-1);however vola-tile acidity was much lower,5.4±2.0meq L-1,than the maximum established(20meq L-1)indicating low levels of acetic acid,or low oxidation of ethanol.The volatile and total acidities of jabuticaba(Myrciaria jaboticaba)wines determined by da Silvaet al.[15],were higher than those obtained in our study,with values above185and 17meq L-1,respectively.Other study conducted for the production of watermelon (Citrullus lanatus natus)alcoholic fermented[16] led to similar results to those obtained with star fruit fer-mented for:pH(4.1),final concentration of SS(6.6°Brix), alcohol content(10°GL),fermented yield(94%)and Y P/S (0.67).However thefinal biomass concentration (20g L-1)and Y X/S(0.14)were lower and the fermenta-tion time(48h)was reduced comparing to those obtained with star fruit wine.These results indicate that the alco-holic fermentation with star fruit was predominantly anaerobic,with good ethanol production.A wine made from jackfruit(Artocarpus heterophyllus)[17]stored for 11months presented a higher alcohol content(13°GL)and a higher total acidity(100meq L-1),but the volatile acidity was very similar to the star fruit wine(6meq L-1).Using a similar initial concentration of yeast (1.65g L-1),Andrade et al.[18].had a slightly more acidic wine(pH=3.51)from strawberry(Fragaria ana-nassa)and similar alcohol content(9.62°GL)after 30days of fermentation,with soluble solids consumption varying from27to9°Brix.For this product,the authors observed that the consumption of soluble solids were more intense in thefirst10days.Tamarind(Tamarindus indica) and soursop(Annona muricata)were also objects of study [19]for the production of alcoholic drinks,resulting in alcohol levels of8.1and6.3°GL,respectively.Star fruit was applied by Paul and Sahu[14]and it was obtained an alcoholic fermented drink with similar alcohol content of 12.15°GL,however these authors obtained higher titrable acidity(0.76%w/w),lower pH(3.94),lower total soluble solid concentration(4.6°Brix)and they used an inoculum step which added more time to the process. ConclusionAt the initial conditions of:23–25°Brix;pH=4.8–5.0and 1.6–2.5g L-1of yeast concentration,it was possible to obtain a fermented drink,from star fruit,with:an alcohol content of11.15°GL,pH from4.13to4.22,afinal yeast concentration of89g L-1,volumetric fermentation yield from82to94%(v/v),total acidity from42to52meq L-1, a volatile acidity of5meq L-1andfixed acidities of 37–47meq L-1.The average conversion factors were:Y X/ S=0.79;Y P/S=0.67and Y P/X=1.02.The fermentation kinetics showed that after5days of fermentation(*25°C), the process reached thefinal stage.The obtained drink had similar characteristics to drinks made from other fruits mentioned in the literature and the differences among their parameters are basically due to the differences in the pro-cesses and in the compositions of raw materials used. Acknowledgments Authors are grateful to the Municipal College Professor Franco Montoro(Mogi Guac¸u,Sa˜o Paulo,Brazil)where all the experiments were conducted and also to the Coordenac¸a˜o de Aperfeic¸oamento de Pessoal de Nı´vel Superior(CAPES,Brazil)for theirfinancial support.References1.Nyanga LK,Nout MJR,Smid EJ,Boekhout T,Zwietering MH(2013)Fermentation characteristics of yeasts isolated from tradi-tionally fermented masau(Ziziphus mauritiana)fruits.Int J Food Microbiol166:426–432.doi:10.1016/j.ijfoodmicro.2013.08.003 2.Santo DE,Galego L,Gonc¸alves T,Quintas C(2012)Yeastdiversity in the Mediterranean strawberry tree(Arbutus unedo L.) fruits’fermentations.Food Res Int47:45–50.doi:10.1016/j.foodres.2012.01.0093.Brazilian Ministry of Agriculture,Livestock and Supply(2004)Law n°10970.Ministe´rio da Agricultura,Pecua´ria e Abasteci-mento.Lei n.10970,de12de novembro de2004(in Portuguese)..br.Accessed on11Nov20154.Warren O,Sargent SA(2011)Carambola(Averrhoa carambolaL.).In:Yahia A(ed)Postharvest Biology and Technology of Tropical and Subtropical Fruits:Ac¸ai to Citrus.Woodhead Pub-lishing Series in Food Science,Technology and Nutrition, pp397–414e.doi:10.1533/9780857092762.3975.Almeida MB,Souza WCO,Barros JRA,Barroso PA,Villar FCR(2011)Physical and chemical characterization of star fruits (Averroa carambola L.)produced in Petrolina,PE,Brazil.Rev Semia´rido Visu1:116–1256.Chau C-F,Chen C-H,Lin C-Y(2004)Insolublefiber-rich frac-tions derived from Averrhoa carambola:hypoglycemic effects determined by in vitro methods.LWT Food Sci Technol 37:331–335.doi:10.1016/j.lwt.2003.10.0017.Wei S-D,Chen H,Yan T,Lin Y-M,Zhou H-C(2014)Identifi-cation of antioxidant components and fatty acid profiles of the leaves and fruits from Averrhoa carambola.LWT Food Sci Technol55:278–285.doi:10.1016/j.lwt.2013.08.0138.Rodrigues MI,Iemma AF(2015)Experimental design and opti-mization.CRC Press,Boca Raton9.Instituto Adolfo Lutz(2008)Analytical Standards of the AdolfoLutz Institute.Normas Analı´ticas do Instituto Adolfo Lutz(in Portuguese),Sa˜o Paulo:IMESP.Accessed on11Nov2015..br/10.Napahde S,Durve A,Bharati D,Chandra N(2010)Wine Pro-duction from Carambola(Averrhoa carambola)juice using Sac-charomyces n J Exp Biol Sci1:20–2311.Sibounnavong P,Daungpanya S,Sidtiphanthong S,Keoudone C,Sayavong M(2010)Application of Saccharomyces cerevisiae forwine production from star gooseberry and carambola.Int J Agric Technol6:99–10512.Bridgebassie V,Badrie N(2004)Effects of different pectolaseconcentration and yeast strains on carambola wine quality in Trinidad,West Indies.Fruits59:131–14013.Reddy LVA,Reddy OVS(2011)Effect of fermentation condi-tions on yeast growth and volatile composition of wine produced from mango(Mangifera indica L.)fruit juice.Food Bioprod Process8:487–491.doi:10.1016/j.fbp.2010.11.00714.Paul SK,Sahu JK(2014)Process optimization and qualityanalysis of Carambola(Averrhoa carambola L.)wine.Int J Food Eng10:457–465.doi:10.1515/ijfe-2012-012515.Silva PHA,Faria FC,Tonon B,Mota SJD,Pinto VT(2008)Evaluation of the chemical composition of wine produced from jabuticaba(Myrciaria jabuticaba).Avaliac¸a˜o da composic¸a˜o quı´mica de fermentados alcoo´licos de jabuticaba(Myrciaria jabuticaba)(in Portuguese).Quı´m Nova31:595–600.doi:10.1590/S0100-4042200800030002516.Fontan RDCI,Verı´ssimo LAA,Silva WS,Bonomo RCF,VelosoCM(2011)Kinetics of the alcoholic fermentation from the prepa-ration of watermelon wine.Cine´tica da fermentac¸a˜o alcoo´lica na elaborac¸a˜o de vinho de melancia(in Portuguese).Boletim Centro Pesq Process Aliment29:203–210.doi:10.5380/cep.v29i2.25485 17.Asquieri ER,Rabelo AMDS,Silva AGDM(2008)Fermentedjackfruit:study on its physicochemical and sensorial character-istics.Cieˆn Tecnol Alim28:881–887.doi:10.1590/S0101-2061200800040001818.Andrade MB,Perim GA,Santos TRT,Marques RG(2013)Fer-mentation and characterization of fermented strawberry.Fer-mentac¸a˜o alcoo´lica e caracterizac¸a˜o de fermentado de morango (in Portuguese).Biochem Biotechnol Rep2:265–268.doi:10.5433/2316-5200.2013v2n3espp26519.Mbaeyi-Nwaoha IE,Ajumobi CN(2015)Production andmicrobial evaluation of table wine from tamarind(Tamarindus indica)and soursop(Annona muricata).J Food Sci Technol 52:105–116.doi:10.1007/S13197-013-0972-4。
能源基准与能源绩效参数确定指南
能源基准与能源绩效参数确定指南## English Answer:Energy Benchmarking and Performance Parameters Determination Guide.Introduction.Energy benchmarking provides a valuable tool for organizations to assess and improve their energy performance. By comparing their consumption to similar organizations, organizations can identify areas where they can make improvements. The Energy Benchmarking and Performance Parameters Determination Guide provides guidance on how to collect and analyze energy usage data, and how to determine energy performance parameters.Section 1: Data Collection.The first step in energy benchmarking is to collectenergy usage data. This data can be collected from avariety of sources, including utility bills, building management systems, and energy monitoring systems. It is important to collect data for a period of at least one year to account for seasonal variations in energy consumption.Section 2: Data Analysis.Once the energy usage data has been collected, it can be analyzed to identify trends and patterns. This analysis can be used to identify areas where energy consumption is high and where improvements can be made.Section 3: Energy Performance Parameters.Energy performance parameters are metrics that are used to quantify an organization's energy performance. These parameters can be used to compare an organization's performance to other similar organizations, or to track the organization's performance over time. Common energy performance parameters include energy intensity, energy consumption per square foot, and greenhouse gas emissions.Section 4: Implementation.The final step in energy benchmarking is to implement the necessary changes to improve energy performance. This can involve implementing energy efficiency measures, such as upgrading lighting systems or installing new insulation, or changing operational practices, such as turning off lights when leaving a room.Conclusion.Energy benchmarking can be a valuable tool for organizations to assess and improve their energy performance. By following the guidance in this guide, organizations can collect and analyze energy usage data, determine energy performance parameters, and implement the necessary changes to improve their energy performance.## 中文回答:能源基准与能源绩效参数确定指南。
Studying the Feasibility of Energy Harvesting in a Mobile Sensor
Studying the Feasibility ofEnergy Harvesting in a Mobile Sensor NetworkMohammad Rahimi,Hardik Shah,Gaurav S.Sukhatme,{mhr,hardiksh,gaurav@} John Heideman{johnh@},Deborah Estrin{destrin@}ABSTRACTWe study the feasibility of extending the lifetime of a wireless sen-sor network by exploiting mobility.In our system,a small percent-age of network nodes are autonomously mobile,allowing them to move in search of energy,recharge,and deliver energy to immobile, energy-depleted nodes.We term this approach energy harvesting. We characterize the problem of uneven energy consumption,sug-gest energy harvesting as a possible solution,and provide a sim-ple analytical framework to evaluate energy consumption and our scheme.Data from initial feasibility experiments using energy har-vesting show promising results.1.INTRODUCTION AND BACKGROUND Wireless sensor networks[1]are an exciting new area of re-search.They belong to the class of ad-hoc networks,where the individual nodes have limited sensing,computation,communica-tion and energy.The(envisaged)large scale of such networks pro-hibits human intervention for network maintenance.One of the very scarce resources for these types of networks is energy.These networks are expected to have a long lifetime(weeks to years)with-out human intervention for energy replenishment(recharging or changing the batteries).Human intervention is undesirable since large number of nodes imply high operational cost.Current approaches to energy management mainly focus on low power architecture and low power network design at different com-munication layers.These include(Figure1):•Low power hardware architectures•Low power software techniques•Limiting transmission range and power control at physical layer to bound device consumption[2].•Low power MAC mainly by increasing MAC layer sleep time of the nodes[3].•Dynamic configuration of nodes with extra deployment of them in any geographic region for sleep cycles in higher time granularity[4].•Geographic and power aware routing to bound network traf-fic[5].•Data Aggregation to increase the good put of the network and to suppress unnecessarily data traffic[6,7].In parallel,there has also been active research in environmental power scavenging techniques[8].There is also some work on en-ergy replenishment in a sensor network using robots[9].InthisFigure1:Low power network design techniques project a robot is used to recharge the sensor nodes connected to plants.The robot is also used to water the plants.In terms of harvesting energy from the environment,the current mature tech-nology is based on solar cells.While solar cells are attractive out-doors,they have poor indoor performance especially withfluores-cent lights sources.They also suffer from a large dynamic range outdoors.There is a difference of up to three orders of magnitude between the available solar power in cloudy,shadowy and sunny environments.Other potential energy sources are vibration,fuel cells,thermal diffusion and acoustic noise.These new technolo-gies are not mature,which precludes their use in the near future. Solar cells remain the current main source of ambient environmen-tal energy.2.THE ENERGY HUNTING MECHANISM Consider a geographically distributed sensor network composed of many individual nodes.We assume that some of the nodes are capable of recharging themselves using energy available in the en-vironment using solar panels.We call these nodes energy produc-ers.The rest of the nodes only consume energy in computation and communication.We call them energy consumers.There are two key problems to be addressed.Energy producers need to work with a non-uniform geographic energy distribution,i.e.,the avail-able energy pattern in the network environment may be completely different from the energy consumption pattern and it may lead to energy starvation in some portion of the network.This may ulti-Figure2:The topfigure is available environmental power,the nextfigure is power consumption.The difference of the two is an important factor in longevity of the network.The last figure shows discrete samples of power distribution across the network nodes.mately result in a fragmented network and uneven sensor coverage because some set of nodes has been completely energy depleted. The second problem is for the energy producers to deliver the en-ergy they have gathered to the consumer nodes.We propose a method to exploit robotic mobility by having en-ergy producers be mobile robots.These nodes try to keep them-selves recharged by moving to locations with abundant energy sup-ply.Once charged,they migrate to the service areas in the network for delivering energy to the(static)consumer nodes that have re-quested energy.In essence,mobile energy producers act as energy-equalizers in the network by carrying energy‘payloads’from areas where environmental ambient energy is plentiful to areas where it is either unavailable or being used faster than it can be harvested. Although in this paper we explore energy harvesting via mobile nodes,related problems use mobile nodes for other purposes,such as to maintain network connectivity[10]or to improve localiza-tion[11,12].It is interesting to note that the mobile nodes can also serve other purposes such as maintaining network connectivity.)3.ANALYTICAL DISCUSSION3.1Self Contained NetworkWe consider the sensor network to be a closed energy system consisting of producers and consumers.Each node is capable of producing energy with rate P p(i,t)and consumes energy with rate P c(i,t).The network longevity depends on P p−P c over the entire network(Figure2).At any instant of time the amount of energy consumed at node i is:E(i,t)=tt0[P p(i,t)−P c(i,t)]dt(1)The network energy is the summation of the individual node en-ergies across the network:E(t)=tt0(i[P p(i,t)−P c(i,t)])dt(2)Figure3:Energy Cell defines the territory of each robot andits zone of service.The area spanned by the network is dividedinto Energy Cells.The number of robots per cell depends onthe number of static client nodes,the rate of their energy con-sumption and available environmental energyIn fact,since consumption and production are discrete quantitiesacross the nodes of the network.If we get energy samples acrossthe network then we have the discrete energy distribution function(Figure2).The summation of the discrete energy function acrossthe network is the network energy.A node is self contained if:E(i,t)>0;∀t>0(3)Since energy consumption and generation varies across the net-work,some nodes may be self contained while others may not.For example,a node sitting in shadow and actively sensing andcommunicating would have a large consumption and so probablywould not be self contained,while a lightly used node in brightsunlight will have plentiful energy.The goal of our system is to de-tect these kinds of energy imbalances and even them out by movingnodes.Thus we can define the network to be self-contained if:E(t)>0;∀t>0(4)Note that it is a necessary but not sufficient condition for a staticnetwork to be self contained mainly because the formula does notconsider energy distribution variation.If we assume that energyoverhead of the energy-equalizing algorithm is zero,then there ex-ists an algorithm such that by implementing that algorithm therewould be no energy failure of any element of a self-contained net-work.In practice the overhead of such an energy-equalizing algorithmmay not be be negligible,specifically in our case,in which we ex-ploit motion.Then a network is a self-contained network if:E(t)−E overhead>0;∀t>0(5)where,E overhead is the overhead of the energy harvesting algo-rithm.Notice that E overhead,is not afixed value and to a greatextent,depends on the variance of the environmental energy avail-ability,spacial distribution of the network and number of staticnodes.3.2Energy CellsLet the maximum amount of energy that any mobile node can store be E max,the amount of energy it consumes to move(per unit distance)be E mov,the maximum amount of energy any static node can store(and thus require a mobile node to transport)be E payload. The longest profitable distance a mobile robot can move is:(E max−E payload)(2×E mov)(6) We divide the network into service zones of linear size L(we use square service zones of diagonal L).Each such zone is called the Energy Cell Area(ECA)and it shows the zone of service or the territory of a mobile node(Figure3).This defines a minimum bound on the number of serving robots needed in the network:Number of Serving Robots≥AECA(7)3.3The Effect of Energy Availability and Net-work ConsumptionEnergy cells determine a theoretical minimum bound on the num-ber of robots but the actual number of needed service robots may be larger depending on the network consumption and the available environmental energy.If we assume the density of static nodes is ∆and the network coverage area is A,also assume that average node’s power consumption is P c and average power availability for production is P p then the minimum number of service robots is:Number of Serving Robots≥(AECA)×(P cP p)×(ECA×∆)(8)In this formula the parameter A is the number of Energy Cells,the ECA×∆stands for expected number of static nodes per Energy Cell and P cP pis the rate of power consumption(discharg-ing)compared to the rate of power production(charging)in cells. The value of P c is the average value of consumption of the nodes and the value of P p is the average power production(i.e.average power per unit area multiplied by the solar panel area).Note that the efficiency of the solar charging system may also be accounted for in the calculation.Also notice that increasing the number of robots can compensate for the effect of low available energy den-sity across the cells,although,this may not make sense beyond a certain limit.Finally,the effect of large variance of available energy distribution can be compensated by increasing the energy capacity (battery size)of each robot.This will increase its energy searching territory.4.EXPERIMENTAL TESTBEDCreating a meaningful experiment needs large number of static nodes and mobile nodes.We have created a smaller version of such a testbed for our experiments[13,14].The current testbed (Figure4)has15static nodes and three mobile robots.The static nodes are Berkeley motes[15].We use them as our network ele-ments capable of sending and receiving packets.They also act as beacon elements replying to robot queries.These beacons are used by the robots to localize themselves.The robots used in our experiments are Robomotes[16]that we have designed previously.They are able to send queries to the network and get replies from the beacons to localize themselves. The algorithm for localization is simple.Each robot localizes it-self to the centroid of all static nodes from which it receives bea-cons[17].This localization scheme,with all beacons calibrated in range,gives an accuracy of approximately0.3grid spaces and variance of0.15grid spaces.The grid spaces are2feet apart. Transmission ranges of all the beacons are calibrated to be one-grid cell with an auto calibration routine we developed.This cur-rently provides us a three-hop network.Both robots and static nodes can send information packets destined to a specific destina-tion node in the network.We currently useflooding as our routing mechanism.Each robot constantly sends a query to the network tofind out if any static node needs service(i.e.energy replenishment).The static node(s)that needs the service replies back with its location and the amount of time it can survive without assistance.The robots then select the node with most urgent service and navigate across the testbed toward the service location.The robots have wheel odometers,which produces10pulses per inch.They also have a compass with resolution of better than5◦. The combination of RF localization service,wheel odometer and compass enables s reasonable navigation across the testbed.A cam-era suspended above the testbed is used for ground truth[18].The robots can be charged via a wall adapter or an optical dock-ing station.The charging time with wall adapter is3hours and with the optical docking station it takes about6hours.The robots also can go to deep sleep for minimum power consumption.5.EXPERIMENTSTo begin to understand the viability of energy harvesting we per-formed a series of experiments using our testbed.These tests are:•quantifying the network power consumption•quantifying robot parameters(energy consumption,produc-tion and capacity)•characterize the running overhead of the robotsWe also calculated the necessary number of robots for running on our testbed.5.1Maximum Network Consumption:Sim-ple PingThefirst experiment characterizes the lifetime and energy con-sumption of the network over different traffic patterns.The energy reservoir of the network is a known parameter,which is the battery capacity of individual nodes multiplied by number of nodes.En-ergy consumption of the network is the other important parameter for determination of the network lifetime.Energy consumption is dependent on network activity or network traffic.We ran multiple ping experiments with different transmission rates each for30minutes and measured the actual number of pack-ets passed through the network(Table1).These measurements are obtained using snooper nodes across the network that listen con-stantly to the network traffic and dump data on a central debugging PC machine.Table1shows that the amount of information which can pass through the network,is maximized at certain point.This maximum capacity point is also the maximum energy consuming point for the network.We call it Peak Consumption Point.Note that in reality the actual power consumption may be more than the peak energy point if the amount of information sourced in the net-work is more than the network capacity,which waste the energy resources without any improvement in traffic.Clearly this is not a good design point.At the Peak Consumption Point the network passes16700pack-ets per30minutes or9.3packets per second.Since there are only(a)robots with solarcells(b)testbed from localiza-tionweb-cam(c)Navigation of the robotacross testbed from point(3,1)to point(3,7) Figure4:Robots,testbed and navigation of robots across the testbed.15nodes in the network this works out to0.62packets per second per node.Nodes in the network are either transmitting or receiving (or idle)with almost the same amount of energy consumption in receiving and idle state.The packet transmit time is approximately 25ms,the energy that the node consumes in transmission is60mW, in reception36mW and in sleep it is only240µW.This suggests that the maximum node consumption is obtained by multiplying the transmit power by the maximum percentage of transmit time to which we add the reception or idle power consumption for the rest of the ing numbers from our testbed:Transmit time=25ms×0.62=0.015sec=1.5%Max Node consumption=60mW×1.5%+36mW×98.5%=36.4mW This shows that with the current MAC and routing protocol the consumption of the multi-hop network is mostly dominated by idle power,which is basically reception power(since the radio listens in idle mode).This power consumption may increase if we have bet-ter routing protocol or a MAC protocol with RTS/CTS,enabling higher amount of traffic that network can carry.Finally it also shows that by being able to change some percentage of idle time from receiving to sleeping we may gain a lot of power saving. Using current values for static node battery capacity in our testbed (550mWh)allow us to compute network lifetime:550mWh/36.4mW=15.1hour5.2Robot Energy States and Robot Territory Mobile nodes in our system have different energy states(Ta-ble2).The main states are transmitting,receiving,moving and charging.Charging state gives a variable rate,depending on the available environmental energy.The charging value in Table2is the typical value for our optical docking station.Based on our previous discussion for the robot territory and the fact that the robot’s moving power consumption(per unit distance) is0.210J/inch,the robot battery capacity is1100mW and each static node has capacity of550mWh,we compute the diagonal of ECA:(E max−E payload)(2×E mov)=(1100mW−550mW h)2×0.21inch/joule=110footThis clearly shows that a robot may cover a large,building-sized territory.Although this appears to be a promising result,we note that the rate of charging of the robot is very close to the rate of dis-charging the nodes.This requires a large number of serving robots. In this example we have:Number of Serving Robot≥(AECA)×(P cP p)×(ECA×∆) Since in our case ECA is the whole network(A N=ECA),we obtain the following numbers for our testbed.Number of Serving Robots≥(36.4/120)×15=4.55This result shows the importance of the consumption pattern. Note that this number of serving robots is necessary for worst traf-fic condition and guaranteeing the network longevity.In practice the number of serving robots may reduce depending on expected reliability.5.3Overhead of the AlgorithmThe previous two experiments give an estimate of the robots ter-ritory and the actual number of needed robots for maintenance of the network longevity.However,we have not yet considered the overhead of running the algorithm.There are two type of overhead associated with our algorithm.Thefirst one is the communication overhead on the network for energy queries and the replies.The second one is the movement overhead(due to paths taken by the robot which are longer than optimal).We ran a series of experiments to estimate the overhead associ-ated with the suboptimal paths the robot takes.We programmed the robot to go from different initial points to destination points and logged the path they actually navigated.Figure4(c)shows one example of such navigation.In thisfigure it is clear that the robot starts from location3,1to reach location3,7.The actual path taken is shown in thefigure.The average path has30%overhead (sampled on100paths)compared to the straight-line path.If we neglect communication overhead,and also neglect the overhead of sink to source movement due to our small testbed size,and only consider the movement overhead this corrects the requirement for the maximum number of robots to be:Number of Serving Robot≥4.55×1.3=5.9Ping Rare Number of packets received in30minutes10Packets/Sec9723Packets1Packet/Sec16700Packets1Packet/10Sec1504Packets1Packet/60Sec303Packets1Packet/300Sec65PacketsTable1:The table shows the rate of packets transmitted from the ping source and the throughput of the network in30minute experiment time.The graph shows the calculated per node throughput traffic vs.the source ping rate.6.FUTURE WORKCurrent work is a preliminary analysis of the applicability of such type of systems.Clearly,we see that the results are promis-ing.We leave a more comprehensive study with probabilistic ap-proaches for later study.We also develop the actual energy equal-ization algorithm to run on such a network.Current testbed has apparently a limited scope of operation.There are several limiting factors in the current system such as:•Low Number of robots•Low Number of static nodes•Limited geographic scopeWhile this is true,on the other hand our current testbed pro-vides a very reliable and controlled test environment for prelimi-nary algorithmic experiments.While we are doing more experi-ments with our current system but also we are developing a more reliable testbed.Our newer testbed will target in the long run:•50Active robots•More than50static nodes•Multiple optical docking station•Building size network distribution•More efficient routing•Nodes sleep in idle timeState EnergyMove(2”/Sec)-420mWTransmit-60mWReceive-36mWSleep-240µWCharge120mWTable2:Measured energy consumption or production of the robot in different states.The new robots will have enhanced features such very lower power consumption in movement state,higher speed,excellent odome-ter feedback system for reduced movement overhead and very pre-cise object avoidance system for in building navigation.We target larger geographic scopes and lower number of robots to static nodes ratio in future.7.CONCLUSIONIn this paper we introduced a new paradigm of energy manage-ment and equalization using mobility.This approach can increase the network longevity.If the net consumption rate is lower than the possible harvesting rate,enough mobile nodes can provide a self-sustaining system.We defined the robots territory and the number of active serving robots needed in those territory areas.We also described the development of a testbed forfinding the practical balances for such high longevity network.We found thatin our network of15static nodes with maximum possible traffic we need about6robots to guarantee network longevity.This is a about 0.40%number of robots to number of static nodes ratio for guar-anteeing network lifetime.In practice the actual number of robots can be less based on the degree of expected network availability. The results clearly demonstrate the applicability of our approach to energy maintenance.We showed that an unmodified network would partition in less than a day can be made sustainable with the addi-tion of40%mobile nodes.This result demonstrates that the addi-tion of a few,relatively inexpensive robots(less than twice the cost of static nodes)can make sensor nets self-sustaining.8.ACKNOWLEDGMENTSThis work is supported in part by“Center for Embedded Net-worked Sensing”,by NSF grants ANI-9979457and by NSF grant EIA-0121141.9.REFERENCES[1]Deborah Estrin,“Embedded Everywhere”,National Academy ofEngineering,2001.[2]R.Ramanathan and R.Hain,“Topology Control of Multi-hop RadioNetworks using Transmit Power Adjustment”,Proc.IEEE InfocomTel Aviv,Israel,Mar2000[3]Wei Ye,John Heidemann and Deborah Estrin,“An Energy-EfficientMAC Protocol for Wireless Sensor Networks”,In Proceedings of the21st International Annual Joint Conference of the IEEE Computerand Communications Societies(INFOCOM2002),New York,NY,USA,June,2002[4]Alberto Cerpa and Deborah Estrin,“ASCENT:AdaptiveSelf-Configuring Sensor Networks Topologies”,In Proceedings ofthe Twenty First International Annual Joint Conference of the IEEEComputer and Communications Societies,NewYork,NY,USA,June,23-27,2002.[5]Yan Yu,Ramesh Govindan and Deborah Estrin,“Geographical andEnergy Aware Routing:A Recursive Data’Dissemination Protocolfor Wireless Sensor Networks”,UCLA Computer ScienceDepartment Technical Report UCLA/CSD-TR-01-0023,May,2001. [6]S.Madden,R.Szewczyk,M.Franklin and D.Culler,“SupportingAggregate Queries over Ad-hoc Wireless Sensor Network”Workshop on Mobile Computing and Systems Applications,2002[7]Chalermek Intanagonwiwat,Ramesh Govindan and Deborah Estrin,“Directed Diffusion:A Scalable and Robust CommunicationParadigm for Sensor Networks”,In Proceedings of the Sixth AnnualInternational Conference on Mobile Computing and Networks(MobiCOM2000),August2000,Boston,[8]Jan M.Rabaey,M.Josie Ammer,Julio L.da Silva Jr.,Danny Patel,and Shad Roundy,“PicoRadio Supports Ad Hoc Ultra Low-PowerWireless Networking”,IEEE Computer,vol.33,(no.7),IEEEComput.Soc,July2000.p.42-8.[9] Marca,D.Koizumi,M.Lease,S.Sigurdsson,G.Borriello,W.Brunette,K.Sikorski,D.Fox,“PlantCare:An Investigation inPractical Ubiquitous Systems”,Intel Research,IRS-TR-02-007,Jul.23,2002[10]Maxim A.Batalin,Gaurav S.Sukhatme,“Efficient Explorationwithout Localization”,To appear in International Conference onRobotics and Automation,ICRA2003[11]Nirupama Bulusu and John Heidemann and Deborah Estrin andTommy Tran,“Self-Configuring Localization Systems:Design andExperimental Evaluation”,To appear On ACM Transactions onComputer Systems,September2002[12]Nirupama Bulusu and Deborah Estrin and Lewis Girod and JohnHeidemann,“Scalable Coordination for Wireless Sensor Networks: Self-Configuring Localization”,In Proceedings of the6th IEEEInternational Symposium on Communication Theory and Application [13]Test bed webpage:/projects/robomote/testbed/[14]Mohammed Rahimi,Rohit Mediratta,Karthik Dantu,and Gaurav S.Sukhatme,“A test bed for experiments with Sensor Actuatornetworks”,/robomote/testbed/techreport.pdf [15]David Culler,“A Network-Centric Approach to Embedded Softwarefor Tiny Devices”,DARPA workshop on Embedded Software. [16]Gabriel T.Sibley,Mohammad H.Rahimi and Gaurav S.Sukhatme,“Robomote:A Tiny Mobile Robot Platform for Large-Scale Sensor Networks”,Proceedings of the IEEE International Conference onRobotics and Automation(ICRA2002),2002[17]N.Bulusu,J.Heidemann and D.Estrin,“GPS-less Low-CostOutdoor Localization for Very Small Devices”,IEEE PersonalCommunications Magazine,Special Issue on Smart Spaces andEnvironments,October2000.[18]Mezzanine,An Overhead Visual Object Tracker/mezzanine/mezzanine.html。
Optimal design of multi-channel microreactor for uniform residence time distribution
TECHINCAL PAPEROptimal design of multi-channel microreactor for uniform residence time distributionCyril Renault •Ste´phane Colin •Ste ´phane Orieux •Patrick Cognet •The´o Tze ´dakis Received:7April 2011/Accepted:26July 2011/Published online:21August 2011ÓSpringer-Verlag 2011Abstract Multi-channel microreactors can be used for various applications that require chemical or electro-chemical reactions in either liquid,gaseous or multi phase.For an optimal control of the chemical reactions,one key parameter for the design of such microreactors is the res-idence time distribution of the fluid,which should be as uniform as possible in the series of microchannels that make up the core of the reactor.Based on simplifying assumptions,an analytical model is proposed for optimiz-ing the design of the collecting and distributing channels which supply the series of rectangular microchannels of the reactor,in the case of liquid flows.The accuracy of this analytical approach is discussed after comparison with CFD simulations and hybrid analytical-CFD calculations that allow an improved refinement of the meshing in the most complex zones of the flow.The analytical model is then extended to the case of microchannels with othercross-sections (trapezoidal or circular segment)and togaseous flows,in the continuum and slip flow regimes.In the latter case,the model is based on second-order slip flow boundary conditions,and takes into account the com-pressibility as well as the rarefaction of the gas flow.1IntroductionApplications of microfluidic systems are very varied and have been developed for more than two decades (Gravesen et al.1993;Shoji and Esashi 1994).They are now used for example in aerospace,automotive,military,food and also in a number of medical applications.The recent develop-ment of MEMS and microfluidic technologies applied to chemical engineering is due to several advantages amongwhich the increased surface area to volume ratio (Lo¨we and Ehrfeld 1999)which greatly improves the mass or energy transfer.Thanks to their small dimensions,microreactors exhibit fast response times,which is an advantage for the process control and permits the use of highly reactive and dangerous products (Vlachos 1998).In comparison with macroscopic reactors,microreactors allow a better man-agement of effective heat,facilitating isothermal operation or coupling of endothermic and exothermic reactions (Peterson 1999).All these features associated to original designs can allow reducing the number of reaction steps.In addition,miniaturization should help to optimize selectivity,reduce energy consumption and lower produc-tion costs.Thus,the current trend is to develop smaller,cheaper and more efficient devices by optimizing safety and reaction control while minimizing the environmental impact (Commenge et al.2005).There are some counterparts,however,to the use of microreactors.Reduction of dimensions may lead toC.Renault ÁS.Colin (&)ÁS.OrieuxUniversite´de Toulouse;INSA,UPS,Mines Albi,ISAE;ICA (Institut Cle´ment Ader),135,avenue de Rangueil,31077Toulouse,Francee-mail:stephane.colin@insa-toulouse.fr C.Renaulte-mail:renault.cyril@hotmail.fr S.Orieuxe-mail:stephane.orieux@insa-toulouse.frC.Renault ÁP.Cognet ÁT.Tze´dakis Laboratoire de Ge´nie Chimique,Universite ´de Toulouse;INPT,UPS,118Route de Narbonne,F-31062Toulouse,France e-mail:patrick.cognet@ensiacet.fr T.Tze´dakis e-mail:tzedakis@chimie.ups-tlse.frC.Renault ÁP.Cognet ÁT.Tze´dakis Laboratoire de Ge´nie Chimique,CNRS,31062Toulouse,France Microsyst Technol (2012)18:209–223DOI 10.1007/s00542-011-1334-7significant modifications of both flow hydrodynamics and convective heat transfer,especially in the case of gases,for which rarefaction of the flow induces local thermodynamic disequilibrium.Another restriction is due to the small volumes that limit the possibilities of industrial production.To overcome this drawback,the use of several parallel microchannels is necessary but this solution could exhibit a poor uniformity in the fluid distribution between them.Recently,Ziogas et al.(2009)have summarized the advantages and disadvantages of the use of microreac-tors compared with conventional reactors applied to electrochemistry.An example of typical microreactor made of a series of parallel microchannels is shown in Fig.1.The three main parts of this basic cell are the distributing channel,the net-work of parallel reaction microchannels and the collecting channel.The arrows point out the possible locations of the flow inlet and outlet.Applications for such a microreactor design are varied.A simple microreactor can for example be used in synthesis of organic compounds and in catalytic treatment of gaseous effluents contaminated by volatile organic compounds (VOCs).An electrochemical microre-actor can be designed for electro synthesis of ‘‘probes’’molecules devoted to medical imaging for tumor detection,using two similar gold-or platinum-coated units facing each other and separated by an ion-exchanger membrane.In such a microreactor,the control of the residence time of the fluid is crucial for an efficient operation.The objec-tive is to obtain a uniform distribution of the flowrate between all microchannels.A complete CFD simulation of complex microreactor geometries generally requires,how-ever,important computational resources (Commenge et al.2002;Saber et al.2009).Delsman et al.(2004)proposed an optimization of the flow distribution in a microstructured plate composed with 19rectangular microchannels,considering nine plate designs with different distributing and collecting channels and locations of the inlet and outlet sections,under various flowrate conditions.The authors used a three-dimensional CFD model with an artificial viscosity in the channel region which reduced the compu-tational time by a factor 7.The simulations showed that doubling the cross-sectional area of the distributing and collecting channels improved the even distribution of the flow.The same trend was observed by doubling the length of the microchannels.Finally,the best geometry found consisted in inlets and outlets sections parallel to the mi-crochannels cross-sections,with asymmetrical distributing and collecting channels.With this optimal design,the rel-ative standard deviation of the flow distribution was reduced from 19to 3%.Jang et al.(2010)developed an original program combining a simplified conjugate-gradient method called SCGM (Chen and Cheng 2002)with CFD calculation by the CFD-ACE ?commercial code in order to optimize the width of distributing and collecting chan-nels.Their goal was to increase the methanol conversion for hydrogen production in fuel cells.They compared three different geometries with different inlet and outlet locations and found an optimized solution for these three cases which required an increase in the pressure drop.They also showed that this optimization led to an improvement of the meth-anol conversion ratio from 72.7to 99.9%.In order to rapidly optimize the design of multi-channel microreactors,it is necessary to develop calculation meth-ods less expensive in terms of memory and computation menge et al.(2002)proposed an approximate model in order to evaluate the pressure drop and flow dis-tribution through microchannels,focusing on the influence of the length and width of the reaction microchannels and the angle of the tapered distributing and collecting channels.CFD calculations were used to validate this analytical model.Saber et al.(2009)analyzed the hydrodynamics of multi-scale channel networks under isothermal and laminar conditions,using a linear pressure drop model.The main technique used to improve the uniformity of the residence time distribution was to decrease the pressure drop through the distributing and collecting channels in comparison to the pressure drop through the microchannels.This objective was achieved increasing the distributing over microchannel hydraulic diameter ratio and decreasing the distributing over microchannel length ratio.More recently,Saber et al.(2010)investigated the performances of their previous multi-scale channel network,focusing on the selectivity of consecutive catalytic reactions.The authors demonstrated the large influence of the flow maldistribution on the selectivity,which reduces the efficiency of the considered reaction.Cho et al.(2010)studied the thermal and hydro-dynamic behaviour of microchannel heat sinks with a design similar to the one shown in Fig.1.TheyinvestigatedFig.1Example of microreactor based on a network of parallel microchannelsthe effect of non-uniform heatflux for three different non-uniform heatflux conditions,using a tri-diagonal matrix algorithm(TDMA)for the calculation of massflow distri-bution.The influence of the shape of the distributing and collecting channels on the two-phaseflow distribution in the rectangular microchannels was also analysed.It was con-cluded that a more evenflow distribution is achieved when the cross-sectional area of the distributing and collecting channel are larger,compared with the cross-sectional area of the microchannels.Whatever its design,the optimisation of a microreactor requires an accurate modelling of the hydrodynamics in the distributing and collecting channels,as well as in the mi-crochannels,which have the smallest hydraulic diameters of the reactor.Due to the low values of the Reynolds numbers,the analytical modelling of liquidflows in long microchannels is easy.For fully developed laminar and isothermalflows of Newtonian liquids,the Poiseuille number is a constant that only depends on the shape of the channel cross-section.The early experimental studies published twenty years ago,however,showed deviations from the theory and pointed out contradictory results (Morini2004),but the most recent papers have shown that these deviations were mainly due to experimental uncer-tainties,particularly the uncertainties on the dimensions of the channel cross-section.Thus,conventional theory has now proved to be accurate for predicting liquidflowrates in microchannels,as soon as the hydraulic diameter is of the order of one micrometer or higher.On the other hand,reducing dimensions or decreasing pressure leads to rarefaction effects,in addition to com-pressibility effects,forflows of gases in microchannels (Colin2005).These rarefaction effects appear as soon as the mean free path of the molecules is no longer negligible compared with the hydraulic diameter of the microchannel. For such rarefiedflows,the classic Poiseuille model is no longer valid,and other models should be used,according to the rarefaction level,which is quantified by the Knudsen number Kn¼k=L,ratio of the mean free path of the molecules k over a characteristic length L such as the hydraulic diameter.In microsystems,it is frequent that 10À3Kn10À1;in that case,the regime is the so-called slipflow regime and the Navier–Stokes equations remain applicable,provided a velocity slip and a temperature jump at the walls,due to a local thermodynamic disequilibrium, are taken into account.Semi-analytical models are still available in this regime,even when3-D effects should be taken into account,as it is the case for rectangular cross-sections(Aubert and Colin2001).The objective of the present paper is to propose a simple model for the rapid optimisation of a multi-channel mic-roreactor,in terms of uniform residence time distribution.Following the description of a multi-channel microreactor in Sect.2,an analytical model is developed in Sect.3, which allows a rapid calculation of theflow distribution of liquids in the rectangular microchannels as a function of the geometrical parameters.The efficiency of this approximate model is validated by CFD simulations and a hybrid model combining these two approaches is proposed. This intermediate approach allows the utilisation of simple analytical equations in the long straight microchannels, combined with accurate CFD calculations for the other parts of the microreactor which require more complex3D simulations.The analytical model is extended in Sect.4to the case of microchannels with various cross sections.In silicon substrates,rectangular cross-sections can be etched by deep reactive ion etching(DRIE)whereas trapezoidal cross-sections are obtained by wet chemical etching.In metallic substrates,circular segment sections can be obtained by precision micro milling,embossing,isotropic wet chemical etching(Madou2002)or micro electro dis-charge machining(l EDM)techniques.Finally,it is shown that the analytical model can also be extended to the case of gasflows in the continuum and slipflow regimes.From these models,it is demonstrated that the microreactor can exhibit a uniform residence time distribution in the mi-crochannels,by simply modifying one geometrical parameter of the distributing and collecting channels.2Microreactor geometry2.1Overall viewA schematic view of a microreactor,here designed for electrochemical synthesis of molecules,is shown in Fig.2. It is composed of two symmetrical units facing each other.A copper heat exchanger(E)insures the extraction of heat generated by the chemical reaction,the coolantfluid flowing through the cavity with internal zigzag from inlet (E I)to outlet(E O).A thick plate of platinum(P),or a silicon plate with a platinum deposit,is welded to the heat exchanger side.Microchannels are etched in this plate which plays the role of an electrode.The chemical solution flows through these microchannels.Two platinum tubes are inserted through the copper block and the bottom face of the platinum layer allowing the entrance(T I)and exit(T O) of the chemicalfluid.Both electrodes(anode and cathode) are separated by an ion-exchanger membrane(M).2.2ElectrodesFigure3details the initial geometry of the electrodes,with the distributing channel,the reaction microchannels and thecollecting channel.Table 1provides typical values of the various parameters defining non-optimized electrode geometry,in the case of 10parallel microchannels with rectangular cross-sections.These values are used in Sect.3.4for illustrating the calculation of an optimal design.2.3Optimisation strategyThe aim of the optimization is to design the distributing and collecting channels in order to obtain the most possible uniform residence time distribution among all microchan-nels.For simplifying the fabrication process,the depth of the distributing and collecting channels,as well as thedepth of the reactor microchannels is kept uniform and constant (d ¼50l m).It is demonstrated in Sects.3and 4that a uniform residence time distribution can be achieved optimizing a single parameter:the angle h of the tapered distributing and collecting channels,initially equal to zero (see Fig.4).3Optimisation for liquid flows and rectangular microchannels3.1Analytical modelThe following analytical model is very simple to implement and provides flowrate and pressure drop distributions in a few seconds,assuming a laminar regime in the entire mic-roreactor.The distributing and collecting channels are considered as a series of short segments with rectangular cross-sections (Fig.5).The unknowns are the inlet and outlet pressures P in and P out as well as the volume flow ratesQ j —or the mass flowrate _Mj —for each microchannel j .Figure 5illustrates the simplifying assumptions in the case of 10reaction microchannels.The pressures calcu-lated at the downstream section of each segment of the distributing channel are reported to the inlet of the corre-sponding microchannel,while the pressures at the outlet of each microchannel are reported to the upstream sectionofFig.2Schematic view of the electrochemicalmicroreactorFig.3Diagram of the initial electrode before optimizationTable 1Example ofgeometrical parameters of the initial electrode before optimizationReaction microchannelsDistributing/collecting channels Number of channels 101/1Length L c =5mm L d =5mm Depth d =50l m d =50l m Widthw c =250l m w d =1mmWidth of walls between channelsw s =250l mthe corresponding segment in the collecting channel.The pressure drop D P through any segment of the collecting and distributing channels or through the reaction micro-channels is related to the corresponding volume flowrate Qor mass flowrate _M with the Poiseuille number Po ¼SD 2h 2l Q D P L ¼q SD 2h 2l M :D P L;ð1Þwhose value only depends on the aspect ratio of therectangular cross-section.In Eq.1,S is the cross-section area,D h is the hydraulic diameter,l and q are the dynamic viscosity and the density of the fluid,respectively,and L is the length of the considered channel segment or microchannel.The Poiseuille number for a rectangular cross-section can be calculated from the polynomial (Shah and London 1978)Po R ¼241À1:3553r ÃR þ1:9467r Ã2R À1:7012r Ã3Rhþ0:9564r Ã4R À0:2537r Ã5R ið2Þwhere 0\r ÃR 1is the aspect ratio of the rectangularsection defined by r ÃR¼d =w c .In the general case of a microreactor with n microchannels,the problem reduces toa set of 3n ?2equations of type (1),n ?1for the distributing channel,n for the reaction microchannels and n ?1for the collecting channel (see Figs.3and 5).The3n ?2unknowns are P j and P 0j with j 20;n ½ and _Mi with i 21;n ½ .Each quantity R h ;a Àb ¼2l LPo q SD 2hð3Þrepresents the hydraulic resistance of the consideredchannel segment or microchannel,with an inlet pressure P a and an outlet pressure P b ,and it is calculated with Eq.2which gives the value of Po .The system of equations is then written in the matrix form C ¼AB ;ð4Þwhere B contains the unknowns,C is function of the inlet pressure P in and outlet pressure P out of the microreactor and A is function of the various hydraulic resistances (3)previously calculated.For example,in the simple case of a microreactor with only two microchannels,Eq.4reads:Fig.4Initial (a )and optimal (b )geometries of theelectrodeFig.5Diagram of an electrode and its associated network of microchannels (case of 10reaction microchannels)The solution of the problem is given by B ¼A À1C ;ð5Þwhere A À1is the inverse of A ,which can be easily cal-culated using Matlab software.3.2CFD simulationThe accuracy of the analytical model is analyzed bycomparison with numerical simulations obtained with the commercial CFD code Fluent.Different meshes are gen-erated and tested.Only one half of the real domain is meshed,as the plane located at the half-depth of the channels is a plane of symmetry.For the first mesh (A-1),the whole simulated domain (distributing and collecting channels as well as and reaction microchannels)is meshed with a uniform meshing density and 22,400cells in the main plane (Fig.6).The second mesh (A-2)is uniformly refined in both directions of the main plane,leading to a number of cells 4times higher than in the previous case.The third mesh (A-3)is based on the first one with a refinement in the regions of the mi-crochannels inlets and outlets (see Fig.6).For these three meshes,the influence of the number of cells in the direction of the depth is also checked:5,10and 20cells in the half-depth of the microreactor are tested (see Table 2).As an example,the case A-2-10corresponds to the mesh A-2in the main plane with 10cells in the half-depth,and the total number of cells for this case is 896,000.All simulations are performed assuming a laminar incompressible and isothermal flow.A pressure-based solver is used with a second-order discretization scheme.3.3Hybrid approachThe idea is here to combine the two previous analytical and numerical approaches.As the Poiseuille number in long microchannels is accurately modeled in laminar regime by Eq.1,it is possible to make CFD simulations only in the collecting and distributing channels and to use analytical modeling for the flow in the microchannels.Equation 1is implemented via User Defined Functions that allow to link for each microchannel j the inlet pressure P j ,the outlet pressure P 0j and the flowrate Q j .As the microchannels are not meshed,it is possible to increase the number of cells for the meshing of the distributing and collecting channels,with the same computational effort.Three differentmeshesFig.6Detail of the mesh (A-1)in the main plane.The red rectangle shows the region refined in mesh A-3Table 2Total number of cells in all cases simulated by CFD Number of cells in depth 51020Mesh in the main plane A-1112,000224,000448,000A-2448,000896,0001,792,000A-3186,970373,940747,880B-172,000144,000288,000B-284,000168,000336,000B-3336,000672,0001,344,000P in 000000P out¼þ100000R h ;in À0R h ;in À0À1þ10000R h ;0À1R h ;0À10À1þ10000R h ;1À20À10þ100R h ;1À10000À10þ100R h ;2À20000À1þ10R h ;10À2000000À1þ1R h ;20À30R h ;20À3000000þ1ÀR h ;30ÀoutÀR h ;30Àout ÂP 0P 1P 2P 01P 02P 03_M 1_M2:are used for this hybrid approach (Fig.7).In case B-1,only the distributing and collecting channels are meshed and in cases B-2and B-3,the entrance and exit of the micro-channels are also meshed to take into account extremity effects,where the flow is not fully developed.Figure 7shows the mesh in the main plane and Table 2provides the number of cells for each case.For the three cases,the influence of the number of cells in the depth has also been tested.3.4Results and discussion 3.4.1Analytical optimisationThe analytical model is used to optimise the distributing and collecting channels angle h in order to obtain the same flowrate in each microchannel.The solution is illustrated for the case of a microreactor with 10parallel micro-channels,the dimensions of which are given in Table 1.The inlet pressure is 10kPa higher than the outlet pressure.The Reynolds numbers in the microchannels are then in the range 10À1À101.The initial configuration with straight distributing and collecting channels (h =0)leads to high deviations between flowrates in the different microchan-nels:the flowrate in microchannels 1and 10is about 50%higher than in the central microchannels 5and 6(Fig.8).After optimisation,the deviation is found negligible for an angle h R =10.46°.3.4.2CFD simulationsTable 3shows the results of the numerical simulation for meshes A-1,A-2and A-3.The mass flowrate deviation between the data obtained by CFD simulation and the results of the analytical model are provided.The average deviation between analytical and numerical calculations is of the order of 5%,but larger deviations are experienced for side microchannels 1and 10,and lower for central microchannels 3–8.By construction of the model,the analytical solution exhibits symmetry:the flowrate in microchannel j is the same as in microchannel n -j .On the other hand,the local hydrodynamics at the bifurcations is properly taken into account by the CFD simulation and consequently the symmetry is no longer observed for the numerical solution,but the deviation to this symmetry remains moderate.It should also be noted that an increase in mesh refinement leads to a decrease in the deviation between analytical and numerical data.Due to too many cells in the most refined meshes A-2-10,A-2-20and A-3-20,numerical simulations in these cases experienced con-vergence issues.For this reason,the hybrid approach is an interesting alternative:as the microchannels arenotFig.7Meshes of the distributing channel for the hybrid approach;a mesh B-1;b mesh B-2and c meshB-3Fig.8Flowrate distribution in a 10-microchannel microreactor for the initial layout (h =0)and the optimal layout (h =10.46°).Flow of water;analytical calculationsmeshed,it is possible to use more refined meshes in the distributing and collecting channels with the same com-putational effort.3.4.3Hybrid calculationsThe results obtained by the hybrid model are summarised in Table4.Similar deviations are observed between hybrid simu-lation and analytical data,than between CFD and analytical data.It is assumed that the simulations for cases B-2-20 and B-3-10are the most accurate,as they correspond to the most refined meshes.In these cases,the average deviation with the analytical results is less than4%,but as previ-ously,larger deviations,between9and13%,are observed in side microchannels1and10.This phenomenon can be explained by a more detailed analysis of theflow in the distributing and collecting channels.In the analytical model,it is assumed that pressure is uniform in both inlet and outlet sections of each segment of distributing and collecting channels.Moreover,it is also assumed that these pressures are the same as the pressures at the inlet or outlet sections of the corresponding microchannels(see Fig.5).As illustrated in Fig.9,which shows the actual contours of pressure obtained by the hybrid simulation with mesh B-3-5,this simplifying assumption is rather accurate at the inlet of thefirst microchannels and at the outlet of the last microchannels(see for example the zoom on pressure contours P1and P07).On the other hand,it is less accurate when the distributing and collecting channels are more narrow(see the zoom on pressure contours P9and P02). Figure9also underlines the necessity to simulate by CFD the entrance and exit regions of the microchannels,up to a section in which the pressure is uniform.This is necessary for the validity of Eq.1involved in the User Defined Functions.Table3Deviation(%)on massflowrate obtained for each microchannel between CFD simulations and analytical model,for various meshes Channel numberjAnalytical solution Deviation between analytical and hybrid data(%)_M kg sÀ1ÀÁA-1-5A-1-10A-1-20A-2-5A-3-5A-3-101 1.01910–611.849.989.5411.4111.810.22 1.01910–6 6.81 5.01 4.6 6.25 6.73 5.093 1.01910–6 5.03 3.25 2.85 4.36 4.93 3.264 1.01910–6 4.27 2.5 2.11 3.53 4.16 2.475 1.01910–6 4.04 2.26 1.88 3.25 3.91 2.26 1.01910–6 4.17 2.39 2.01 3.37 4.03 2.327 1.01910–6 4.71 2.92 2.53 3.9 4.56 2.848 1.01910–6 5.84 4.03 3.63 5.05 5.67 3.969 1.01910–68.2 6.34 5.937.438 6.2910 1.01910–614.0912.1511.7113.313.8512.14 Average deviation(%) 6.9 5.08 4.68 6.19 6.76 5.08Table4Deviation(%)on massflowrate obtained for each microchannel between hybrid simulations and analytical model,for various meshes Channel numberjAnalytical solution Deviation between analytical and hybrid data(%)_M kg sÀ1ÀÁB-1-5B-1-10B-1-30B-2-5B-2-10B-2-20B-3-5B-3-101 1.01910–611.6210.269.9310.48.988.6310.49.022 1.01910–6 6.05 4.67 4.34 5.62 4.19 3.85 5.43 4.033 1.01910–6 3.99 2.6 2.29 3.92 2.48 2.16 3.61 2.24 1.01910–6 3.11 1.72 1.41 3.2 1.75 1.43 2.82 1.45 1.01910–6 2.82 1.41 1.1 2.97 1.52 1.2 2.55 1.126 1.01910–6 2.94 1.53 1.22 3.11 1.64 1.33 2.66 1.227 1.01910–6 3.5 2.07 1.76 3.54 2.15 1.83 3.11 1.738 1.01910–6 4.69 3.24 2.93 4.64 3.21 2.89 4.23 2.829 1.01910–67.16 5.67 5.35 6.87 5.43 5.09 6.5 5.0910 1.01910–612.6911.6211.312.7711.3210.9712.5611.14 Average deviation(%) 5.86 4.48 4.16 5.7 4.27 3.94 5.39 3.983.5Optimization with respect to the numberof microchannelsThe previous optimization has been done for 10reactionmicrochannels with an aspect ratio r ÃR ¼0:2.In this section,the influence of the number n of microchannels on the value of the optimal angle h is investigated for different aspect ratios ranging from 0.1to 1.The optimization is based on the previous analytical model;the depth d of the etching is kept equal to 50l m and the microchannels width w c is varied in order to analyze various aspect ratios.The length L c of the microchannels is equal to the length L d of the distributing and collecting channels,and the width w s of the walls between two consecutives microchannels is equal to the microchannels width:L c ¼L d ¼2nw s ¼2nw c .Fig-ure 10gives the values of the optimal angle h R as a functionof n for various aspect ratios r ÃR in a logarithmic scale.It is observed that whatever the aspect ratio,h R n ðÞexhibits a quasi linear behavior in a logarithmic scale.These curves can be accurately fitted by the general equationh R ¼A R n ÀB Rð6Þwere coefficients A R and B R can be expressed as:A R ¼6:4309þ575:55r ÃR À258:71r Ã2Rð7ÞFig.9Iso-contours of pressure obtained by the hybridsimulation with meshB-3-5Fig.10Optimal angle h R versus number n of microchannels forvarious aspect ratios r ÃRin the case of microchannels with rectangular cross-section。
Energy System Flexibility Options
Energy System Flexibility Optionsrefer to a range of strategies and technologies that can help operators and consumers better manage energy supply and demand in order to ensure a reliable and cost-effective energy system. In this article, we will discuss some of the key flexibility options available for energy systems.One important flexibility option is demand response, which involves adjusting energy consumption in response to changes in energy supply or price. Demand response programs can incentivize consumers to reduce electricity usage during times of peak demand or high prices, helping to alleviate strain on the grid and reduce overall energy costs. By shifting energy consumption to off-peak hours or using energy more efficiently, demand response can help balance supply and demand and support the integration of renewable energy sources.Another key flexibility option is energy storage, which involves storing excess energy for use at a later time. Energy storage technologies such as batteries, pumped hydro storage, and thermal energy storage can help smooth out fluctuations in renewable energy generation, improve grid stability, and provide backup power during outages. Energy storage also enables consumers to store energy when electricity prices are low and use it when prices are high, helping to reduce energy costs and optimize energy use.In addition to demand response and energy storage, grid modernization and smart grid technologies are essential flexibility options for energy systems. Grid modernization involves upgrading and digitizing the electrical grid to enable greater integration of renewable energy sources, improve system reliability, and enhance energy efficiency. Smart grid technologies such as advanced metering infrastructure, distribution automation, and grid-connected devices allow for real-time monitoring and control of energy flow, enabling more efficient energy management and response to changing grid conditions.Furthermore, flexible generation technologies such as combined heat and power (CHP), gas turbines, and flexible biomass power plants play a crucial role in enhancingenergy system flexibility. These technologies can quickly ramp up or down in response to changes in energy demand or supply, providing grid operators with the flexibility to manage system variability and ensure reliable energy delivery. By complementing intermittent renewable energy sources with flexible generation, energy systems can achieve greater stability and resilience.In conclusion, energy system flexibility options encompass a wide range of strategies and technologies that can help enhance the reliability, efficiency, and sustainability of energy systems. From demand response and energy storage to grid modernization and flexible generation technologies, there are numerous options available to optimize energy supply and demand, integrate renewable energy sources, and improve grid stability. By leveraging these flexibility options, energy operators and consumers can create a more resilient and cost-effective energy system capable of meeting the challenges of a rapidly evolving energy landscape.。
Applied Energy
逄秀锋,等:我国建筑调适发展现状与前景指南与标准、建立激励机制以及政策法规、走向市场化产业化。
我国建筑调适的发展目前也遵循了这样一条发展路径,不同的是,我们的目标是用更短的时间完成西方国家四十多年走过的道路。
参考文献:[1]Mills E.Commissioning Capturing the Potential[J].ASHRAE Journal,2011,53(2):1-2.[2]逄秀锋,刘珊,曹勇.建筑设备与系统调适[M].北京:中国建筑工业出版社,2015:1-2.[3]Legris C,Choiniere D,Milesi Ferretti.Annex47Report1:Commissioning Overview[R].Paris:International Energy Agency,2010.[4]The U.S.Department of Energy.New DOE Research Strengthens Business Case for Building Commissioning[EB/OL].(2019-05-02)[2020-01-02].https://www.energy.gov/eere/buildings/articles/ new-doe-research-strengthens-business-case-building-commissioning.作者简介:逄秀锋(1976),男,辽宁人,毕业于美国内布拉斯加大学林肯分校,暖通空调专业,博士,研究员,研究方向:建筑调适技术、建筑系统能耗模拟、暖通空调系统故障诊断与优化控制、智慧建筑(xpang113@163.com)。
Energy and Buildingshttps://www.sciencedirect.com/journal/energy-and-buildings/vol/224/suppl/CVolume224,1October2020(1)A new analytical model for short-time analysis of energypiles and its application,by Jian Lan,Fei Lei,Pingfang Hu,Na Zhu,Article110221Abstract:An energy pile is a special form of vertical ground heatexchanger that couples the roles of structural support and heat trans-fer.Modeling the transient heat transfer process inside an energy pilehas importance;however,available analytical models either have in-sufficient calculation accuracy or are computationally demanding.Based on three existing models,this paper proposes a novel short-term hybrid composite-medium line-source(HCMLS)model,whichis not only efficient in computation but also more accurate than mosttraditional analytical models.The model is suitable for ground heatexchangers of various radii.Comparisons between the hybrid analyti-cal model and a numerical model are made for energy pile cases withdifferent parameters,including the thermal properties,borehole radii,relative positions of tubes,and number of tubes.In general,the hy-brid composite-medium line-source model gives credible predictionafter100min.The new model is further validated by the infinitecomposite-medium line-source(ICMLS)model,which is currentlythe most theoretically complete short-term model.Moreover,the newmodel is applied to thermal response tests(TRTs).The least dimen-sionless test duration for interpretations based on the modified hybridcomposite-medium line-source(C-HCMLS)solution is Fo>1.7.This study renders the application of in situ TRTs to energy pileswith large diameters feasible.Keywords:Ground heat exchanger;Energy pile;Short time re-sponse;Thermal response testing(2)Charging performance of latent thermal energy storage sys-tem with microencapsulated phase-change material for domestichot water,by Y.Fang,Z.G.Qu,J.F.Zhang,H.T.Xu,G.L.Qi,Arti-cle110237(3)Thermographic2D U-value map for quantifying thermalbridges in building fa ades,by Blanca Tejedor,Eva Barreira,Ricardo M.S.F.Almeida,Miquel Casals,Article110176(4)Urban morphology and building heating energy con-sumption:Evidence from Harbin,a severe cold region city,by Hong Leng,Xi Chen,Yanhong Ma,Nyuk Hien Wong,Tingzhen Ming,Article110143(5)UK Passivhaus and the energy performance gap,by Ra-chel Mitchell,Sukumar Natarajan,Article110240Building and Environmenthttps://www.sciencedirect.com/journal/building-and-environ-ment/vol/183/suppl/CVolume183,October2020(1)Residential buildings airtightness frameworks:A reviewon the main databases and setups in Europe and NorthAmerica,by Irene Poza-Casado,Vitor E.M.Cardoso,Ricar-do M.S.F.Almeida,et al,Article107221Abstract:The airtightness of buildings has gained relevance in thelast decade.The spread of the regulatory frameworks,the demand ofstricter requirements,schemes for testing and quality control,the cre-ation of airtightness databases and its analysis,is proof of this real-ity.The present review encompasses schemes developed in Europeand North America with regard to these aspects for national residen-tial sectors.A normative framework on requirements and recommen-dations at the national level is compiled.Whole building airtightnessdatabases are compared based on their structures and measurementdata acquisition protocols.Gathered complementary information notdirectly related to testing is analysed and airtightness influencing fac-tors importance and relationships are discussed.Weaknesses andstrengths in the different aspects of the existing database setups areidentified.Also,neglected or not entirely undertaken topics are pin-pointed together with the suggestion of possible opportunities forfuture works and changes.Amongst other relevant remarks and dis-cussions,it is concluded that the lack of uniformization in methodbetween countries,the need for a minimum data setup,the lack ofdata analysis on relating the energy impact with the advancement inrequirements of airtightness performance and the implemented setupsare some of the main issues to address in the near future.Keywords:Review paper;Airtightness;Regulation policy(2)A simulation framework for predicting occupant thermalsensation in perimeter zones of buildings considering directsolar radiation and ankle draft,by Shengbo Zhang,Jamie P.Fine,Marianne F.Touchie,William O’Brien,Article107096(3)Comparative review of occupant-related energy aspectsof the National Building Code of Canada,by Ahmed Abdeen,William O’Brien,Burak Gunay,Guy Newsham,HeatherKnudsen,Article107136Applied Energyhttps://www.sciencedirect.com/journal/applied-energy/vol/275/suppl/CVolume275,1October2020(1)Performance characteristics of variable conductance loopthermosyphon for energy-efficient building thermal control,byJingyu Cao,Xiaoqiang Hong,Zhanying Zheng,et al,Article115337Abstract:Variable conductance loop thermosyphon(VCLT)manip-ulates natural phase-change cycle to regulate the heat transfer.Its pri-mary advantages include high sustainability,simple design and lowcost.One of the potential applications of variable conductance loopthermosyphon is thermal control in buildings for achieving highenergy efficiency.In this study,a distributed steady-state model wasimplemented to determine the heat transfer control characteristics ofvariable conductance loop thermosyphon for the first time and evalu-ate its effectiveness on precise air-conditioning for buildings.The in-ternal flow resistance rises from0.002K/W to0.305K/W and theheat transfer rate decreases from468.5W to71.9W when the rela-tive opening degree of the regulating valve reduces from1.00to0.17under normal boundary conditions.The thermodynamic analysesshow that the regulating valve of the variable conductance loop ther-mosyphon can enable effective thermal control over a wide range ofheat transfer rate to accomplish indoor thermal comfort.The studyalso reveals that variable conductance loop thermosyphon can be ef-fectively adopted with various working fluids and over wide rangesof heat source and heat sink temperatures.Keywords:Air-conditioning;Energy-efficient building;Loop ther-mosyphon;Numerical study(2)Increasing the energy flexibility of existing district heatingnetworks through flow rate variations,by Jacopo Vivian,Dav-ide Quaggiotto,Angelo Zarrella,Article115411(3)A framework for uncertainty quantification in buildingheat demand simulations using reduced-order grey-box en-ergy models,by Mohammad Haris Shamsi,Usman Ali,EleniMangina,James O’Donnell,Article115141(2020-10-10《建筑节能》杂志社侯恩哲摘录)7。
综合能源系统冷热电负荷预测模型的构建方法
综合能源系统冷热电负荷预测模型的构建方法Building a comprehensive energy system load forecasting model is crucial for maximizing the efficiency and sustainability of energy production and utilization. 建立一种综合能源系统负荷预测模型对于最大化能源生产和利用的效率和可持续性至关重要。
By accurately predicting the cold, heat, and electricity demands of a system, operators can optimize the distribution of resources and improve overall performance. 通过准确预测系统的冷、热、电需求,运营商可以优化资源分配,提高整体性能。
However, developing such a forecasting model involves combining various data sources, understanding complex energy interactions, and utilizing advanced analytical techniques. 然而,开发这样的预测模型涉及整合各种数据源,理解复杂的能量相互作用,并利用先进的分析技术。
One approach to constructing a comprehensive energy system load forecasting model is to utilize historical data and statistical methods to identify trends and patterns. 建立综合能源系统负荷预测模型的一种方法是利用历史数据和统计方法来识别趋势和模式。
对自己未来的职业生涯期待英语作文
In the grand tapestry of life, one's career is an intricate thread that weaves through the fabric, lending it both color and texture. As I gaze into the future, my vision for my professional career is not merely about a job title or financial success; rather, it is an ambitious blend of personal growth, meaningful contribution, continuous learning, and a profound sense of fulfillment.Firstly, I envision a career that aligns with my passions and strengths. Having developed a keen interest in technology and its transformative power over society, I aspire to become a leading expert in the field of Artificial Intelligence (AI). I see myself working as a Senior AI Engineer or possibly a Data Scientist, where I can leverage my analytical skills, problem-solving abilities, and creativity to develop groundbreaking solutions. This role will allow me to contribute to the advancement of AI technologies that can revolutionize various sectors from healthcare to education, thereby impacting millions of lives positively.Moreover, I anticipate a career that offers ample opportunities for personal and professional development. I believe that true success lies in constant evolution and adaptation. Therefore, I expect my future career path to be steeped in challenges that push me out of my comfort zone, helping me grow both technically and interpersonally. I yearn for a workplace culture that encourages innovation, collaboration, and lifelong learning. I am excited about attending workshops, seminars, conferences, and pursuing higher studies to stay abreast of the latest advancements in my field.Career satisfaction also entails making a significant impact on society.I desire to engage in projects that have a broader societal purpose, aiming at solving real-world problems using AI and data-driven insights. Whether it's predicting disease outbreaks or optimizing energy consumption, I want my work to contribute towards sustainable development goals and social good.Furthermore, leadership roles hold a special place in my future aspirations. As I progress in my career, I hope to lead teams and mentor young professionals, inspiring them to harness their potential and foster a collaborative workenvironment. The prospect of shaping organizational strategies, influencing industry trends, and driving change excites me greatly.Lastly, striking a harmonious work-life balance is crucial to my career expectations. While I am committed to achieving excellence in my professional endeavors, I equally value time for family, friends, hobbies, and self-care.A flexible work environment that respects individual needs and promotes well-being is integral to my envisioned career trajectory.In conclusion, my ideal future career journey is multifaceted – a rich blend of passion, expertise, growth, impact, leadership, and balance. It’s a path where I would continuously evolve, create value, inspire others, and find meaning in every step of the way. As I embark on this exciting voyage, I am fully aware that perseverance, adaptability, and resilience will be my guiding stars. With dedication and hard work, I look forward to crafting a fulfilling career that transcends beyond just earning a livelihood but contributes significantly to my personal growth and societal progress.This vision serves as a beacon, illuminating my path towards a future that is professionally enriching, socially responsible, and personally satisfying. In the ever-evolving landscape of modern careers, I am ready to embrace the challenges and seize the opportunities that lie ahead, driven by the unwavering belief in my capabilities and fueled by an unquenchable thirst for knowledge and growth.。
基于交通时空特征的车辆全局路径规划算法
ISSN 1674-8484汽车安全与节能学报, 第12卷第1期, 2021年J Automotive Safety and Energy, Vol. 12 No. 1, 2021基于交通时空特征的车辆全局路径规划算法杜 茂,杨 林*,金 悦,涂家毓(上海交通大学机械与动力工程学院,上海 200240,中国)摘要:为降低混合动力汽车(HEV)的出行时间和出行能耗,提出了一种基于时空动态交通信息的路径规划算法。
分析了影响车辆通行时间和全程最低能耗的因素。
一种基于广义回归网络(GRNN)模型,拟合计算了道路通行时间以及整体路径的全程能耗。
构建了基于并行A*算法的车辆路径规划算法,为确定起终点位置后的车辆,规划了一条耗时更短、更加节能的路径。
进行了仿真对比试验。
结果表明:相比于依据平均车速与道路功率的计算方法,该算法能够获得更优的出行路径,可降低车辆能耗11%以上,缩短行车时间13%以上。
因而,该算法可为车辆规划更优的路径。
关键词:混合动力汽车(HEV);城市交通;路径规划;时空搜索中图分类号: U 469.7 文献标识码: A DOI: 10.3969/j.issn.1674-8484.2021.01.005 Vehicle global path planning algorithm based on spatio-temporal characteristics of trafficDU Mao, YANG Lin*, JIN Yue, TU Jiayu(School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China) Abstract: A path planning algorithm based on spatiotemporal dynamic traffic information was proposed toreduce the travel time and energy consumption of hybrid electric vehicles (HEV). The factors that affect thevehicle travel time and the minimum energy consumption in the whole path were analyzed. The travel timeand the path energy consumption are calculated based on the generalized regression network (GRNN) model.A vehicle path planning algorithm based on parallel A * algorithm was constructed to plan a shorter time-consuming or more energy-saving path for vehicles after determining the starting and ending positions. Thevirtual simulation test was implemented. The results show that the proposed algorithm can obtain a better travel path and reduce vehicle energy consumption by more than 11% or driving time by more than 13% comparedwith the calculation methods through average speed or power parameters. Therefore, the proposed algorithm can plan a better route for vehicles.Key words: hybrid electric vehicle (HEV); urban traffic; path planning; space-time searching收稿日期 / Received :2020-11-11。
电工词汇中英文对照
[选取日期][键入文档副标题] | 微软用户微软中国 [键入文档标题]电工词汇中英文对照a. c .balance indicator, 平衡指示器a. c. bridge ,交流电桥a. c. current calibrator,交流电流校准器a. c. current distortion,交流电流失真a. c. induced polarization instrument,交流激电仪a. c. potentiometer,交流电位差计a. c. resistance box,交流电阻箱a. c. standard resistor,交流标准电阻器a. c. voltage distortion,交流电压校准器a. c. voltage distortion,交流电压失真Abbe comparator,阿贝比长仪aberration,象差ability of anti prereduced component,抗先还原物质能力ablative thickness transducer [sensor],烧蚀厚度传感器abrasion testing machine,磨损试验机absolute calibration,绝对法校准absolute coil,独立线圈absolute error,绝对误差(absolute)error of measurement,测量的(绝对)误差absolute gravimeter,绝对重力仪absolute gravity survey,绝对重力测量absolute humidity,绝对湿度absolute method,绝对法absolute moisture of the soil,土壤(绝对)湿度absolute pressure,绝对压力absolute(pressure transducer,绝对压力表absolute pressure transducer[sensor],绝对压力传感器absolute read-out,单独读出absolute resolution,绝对分辨率absolute salinity,绝对盐度absolute stability,绝对稳定性absolute stability of a linear system,线性系统的绝对稳定性absolute static pressure of the fluid,流体绝对静压absolute temperature scale,绝对温标absorbance,吸光度absorbed current image,吸收电流象absorptance,吸收比absorptiometer,吸收光度计absorption cell,吸收池absorption coefficient,吸收系数absorption correction,吸收修正absorption edges,吸收边absorption factor,吸收系数absorption hygrometer,吸收温度表absorption spectrum,吸收光谱absorption X-ray spectrometry,吸收X射线谱法absorptivity,吸收率absorptivity of an absorbing,吸引材料的吸收率abstract system,抽象系统abundance sensityivity,丰度灵敏度AC-ACLVDT displacement transducer,交流差动变压器式位移传感器accelerated test,加速试验accelerationg voltage,加速电压acceleration,加速度acceleration error coefficient,加速度误差系数acceleration of gravity,重力加速度acceleration simulator,加速度仿真器acceleration transducer[sensor],加速度传感器accelerometer,加速度计acceptance of the mass filter,滤质器的接收容限acceptance test,验[交]收检验access,存取 access time,存取时间accessibility,可及性accessories of testing machine,试验机附件accessory(for a measuring instrument),(测量仪表的)附件accessory hardware,附属硬件accessory of limited interchangeability,有限互换附件accumulated error,积累误差accumulated time difference,累积时差accumulative raingauge,累积雨量器accumulator,累加器accuracy,精[准]确度accuracy class,精[准]确度等级accuracy limit factor(of a protective current transformer), (保护用电流互感器的)精确度极限因数accuracy of measurement,测量精[准]确度accuracy of the wavelength,波长精确度accuracy rating,精确度限acetylene(pressure)gauge,乙炔压力表acetylene regulator,乙炔减压器acoustic amplitude logger,声波幅度测井仪acoustic beacon,水声信标acoustic current meter,声学海流计acoustic element,声学元件acoustic emission,声发射acoustic emission amplitude,声发射振幅acoustic emission analysis system,声发射分析系统acoustic emission detection system,声发射检测系统acoustic emission detector,声发射检测仪acoustic emission energy,声发射能量acoustic emission event,声发射事件acoustic emission preamplifier,声发射前置放大器acoustic emission pulser,声发射脉冲发生器acoustic emission rate,声发射率acoustic emission signal processor[conditioner],声发射信号处理器acoustic emission rate,声发射信号acoustic emission source location and analysis system,声发射源定位及分析系统acoustic emission source location system,声发射源定位系统acoustic emission source,声发射源acoustic emission spectrum,声发射频谱acoustic emission technique,声发射技术acoustic emission transducer[sensor],声发射换能器acoustic fatigue,声疲劳acoustic impedance,声阻抗acoustic logging instrument,声波测井仪acoustic malfunction,声失效acoustic matching layer,声匹配层acoustic(quantity)transducer[sensor],声(学量)传感器acoustic ratio,声比acoustic releaser,声释放器acoustic resistance,声阻acoustic thermometer,声学温度计;声波温度表acoustic tide gauge,回声验潮仪acoustic transponder,声应答器acoustical frequency electric,声频大地电场仪acoustical hologram,声全息图acoustical holography,声全息acoustical holography by electron-beam scanning,电子束扫描声全息acoustical holography by laser scanning,激光束扫描声全息acoustical holography by mechanical scanning,机械扫查声全息acoustical imaging by Bragg diffraction,布拉格衍射声成像acoustical impedance method,声阻法acoustical lens,声透镜acoustically transparent pressure vessel,透声压力容器acquisition time,取数据时间actinometer,光能计;直接日射强度表;日射表(active)energy meter,(有功)电度表active gauge length,有效基长active gauge width,有效基宽active metal indicated electrode,活性金属指示电极active remote sensing,主动遥感active transducer[sensor],有源传感器activity,活度 activity coefficient,活度系数actual material calibration,实物校准actual time of observation,实际观测时间actual transformation ratio of voltage transformer,电压互感器的实际变化actral transformation ratio of current transformer,电流互感器的实际变化actual value,实际值actual voltage ratio,实际电压比actuator,执行机构;驱动器actuator bellows,执行机构波纹管actuator load,执行机构负载actuator power unit,执行机构动力部件actuator sensor interface(ASI),执行器传感器接口actuator shaft,执行机构输出轴actuator spring,执行机构弹簧actuator stem,执行机构输出杆actuator stem force,执行机构刚度actuator travel characteristic,执行机构行程特性adaptation layer,适应层adaptive control,(自)适应控制adaptive control system,适应控制系统adaptive controller,适应控制器adaptive prediction,适应预报adaptive telemetering system,适应遥测系统adder,加法器addition method,叠加法additional correction,补充修正additivity of mass spectra,质谱的可迭加性address,地址 adiabatic calorimeter,绝热式热量计adjust buffer total ion strength,总离子强度调节缓冲剂adjustable cistern barometer,动槽水银气压表adjustable relative humidity range,相对湿度可调范围adjustable temperature range,温度可调范围adjusted retention time,调整保留时间adjusted retention volume,调整保留体积adjuster,调整机构;调节器adjustment,调整adjustment bellows,调节波纹管adjustment device,调整装置adjusting pin,校正针adsorbent,吸附剂adsorption chromatography,吸附色谱法aerial camera,航空照相机aerial remote sensing,航空遥感aerial surveying camera,航摄仪aerodynamic balance,空气动力学天平aerodynamic noise,气体动力噪声aerograph,高空气象计aerogravity survey,航空重力测量aerometeorograph,高空气象计aerosol,县浮微料;气溶胶aging of column,柱老化agitator,搅拌器agricultural analyzer,农用分析仪air-borne gravimeter,航空重力仪air capacitor,空气电容器air consumption,耗气量air damper,空气阻尼器air-deployable buoy,空投式极地浮标air-drop automatic station,空投自动气象站air duct,风道air gun,空气枪air inlet,进风口air lock,气锁阀air-lock device,锁气装置air outlet,回风口air pressrue balance,空气压力天平air pressure test,空气压力试验air sleeve,风(向)袋air temperature,气温air-tight instrument,气密式仪器仪表air to close,气关air to open,气开airborne electromagnetic system;AEM system,航空电磁系统airborne flux-gate magnetometer,航空磁通门磁力仪airborne gamma radiometer,航空伽玛辐射仪airborne gamma spectrometer,航空伽玛能谱仪airborne infrared spectroradiometer,机载红外光谱辐射计airborne optical pumping magnetometer,航空光泵磁力仪airborne proton magnetometer,航空甚低频电磁系统airborne XBT,机载投弃式深温计airgun controller,气控制器airmeter,气流表alarm summery panel,报警汇总画面alarm unit,报警单元albedograph,反射计alcohol thermometer,酒精温度表algorithm,算法 algorithmic language,算法语言alidade,照准仪alignment instrument,准线仪alkali flame ionization detector(AFID),碱焰离子化检测器alkaline error,碱误差alkalinity of seawater,海水碱度all-sky camera,全天空照相机all-weather wind vane and anemometer,全天候风向风速计allocation problem,配置问题;分配问题allowable load impedance,允许的负载阻抗allowable pressure differential,允许压差allowable unbalance,许用不平衡量alpha spectrometer,α粒子能谱仪alternating[exchange]load,交变负荷alternating-current linear variable differentialtransformer(AC-ACLVDT), 交流极谱仪 alternating temperature humidity test chamber,交变湿热试验箱altimeter,高度计altitude angle,高度角altitude meter,测高仪ambient humidity range,环境湿度范围ambient pressure,环境压力ambient pressure error,环境压力误差ambient temperature,环境ambient temperature range,环境温度范围ambient vibration,环境振动ambiguity error,模糊误差ammeter,电流表ammonia(pressure)gauge,氨压力表amount of precipitation,雨量amount of unbalance,不平衡量amount of unbalance indicatior,不平衡量指示器ampere-hour meter,安时计amplitude,幅值amplitude detector module,振幅检测组件amplitude error,振幅误差amplitude modulation(AM),幅度调制;调幅amplitude-phase error,幅相误差amplitude ratio-phase difference instrument,振幅比—相位差仪amplitude response,幅值响应analog computer,模拟计算机analog control,模拟控制analog data,模拟数据analog deep-level seismograhp,模拟深层地震仪analog input,模拟输入analog magnetic tape record type strong-motion instrument,模拟磁带记录强震仪analog model,模拟模型analog output,模拟输出analog seismograph tape recorder,模拟磁带地震记录仪analog simulation,模拟仿真analog stereopotter,模拟型立体测图仪analog superconduction magnetometer,模拟式超导磁力仪analog system,模拟系统analog telemetering system,模拟遥测系统analog-to-digital conversion accuracy,模-数转换精确度analog-to-digital conversion rate,模-数转换速度analog transducer[sensor],模拟传感器analogue computer,模拟计算单元analogue date,模拟数据analogue measuring instrument,模拟式测量仪器仪表analogue representation of a physical quantity,物理量的模拟表示analogue signal,模拟试验analogue-digital converter;A/D converter,模-数转换器;A/D转换器analogue-to-digital conversion,模/数转[变]换analysis of simulation experiment,仿真实验分析analytical balance,分析天平analytical electron microscope,分析型电子显微镜analytical gap,分析间隙analytical instrument,分析仪器analytical line,分析线analytical plotter,解析测图仪analyzer tube,分析管anechoic chamber,消声室;电波暗室anechoic tank,消声水池anemograph,风速计anemometer,风速表anemometer meast,测风杆anemometer tower,测风塔aneroid barograph,空盒气压计aneroid barometer,空盒气压表;空盒气压计aneroidograph,空盒气压计angle,角度angle beam technique,斜角法angle beam testing,斜角法angle form,角型angle of attach,冲角angle of field of view,视场角angle of incidence,入射角angle of refraction,折射角angle of spread,指向角;半扩散角angle of view of telescope,望远镜视场角angle of X-ray projiction,X射线辐射圆锥角angle probe,斜探头angle resolved electron spectroscopy(ARES),角分辨电子谱法angle strain,角应变angle transducer[sensor],角度传感器anglg-attack transducer[sensor],迎角传感器angle valve,角形阀angular acceleration,角加速度angular acceleration transducer[sensor],角加速度传感器angular displacement,角加速度传感器angular displacement,角位移angular displacement grationg,角位移光栅angular encoder,角编码器angular sensitivity,角灵敏度angular velocity transducer[sensor],角速度传感器annular coil clearance,环形线圈间隙annular space,环形间隙annunciator,信号源anode,阳极answering,应答anti-cavitation valve,防空化阀anti-contamination device,防污染装置anti-coupling bi-frequency induced polarization instrument,抗耦双频激电仪anti-magnetized varistor,消磁电压敏电阻器antiresonance,反共振antiresonance frequency,反共振频率anti-stockes line,反斯托克线aperiodic dampong,非周期阻尼;过阻尼aperiodic vibration,非周期振动aperture,光阑aperture of pressure difference,压差光阑aperture photographic method,针孔摄影法aperture stop,孔径光栏aperture time,空隙时间apparatus for measuring d.c.magnetic characteristic with ballistic galvanometer, 冲击法直流磁特性测量装置apparent temperature,表观温度appearance potentical,出现电位appearance potential spectrometer,出现电热谱仪appearance potential spectrometer(APS),出现电热谱法application layer(AL),应用层application layer protocol specification,应用层协议规范application layer service definition,应用室服务定义application software,应用软件approval,批准approximate absolute temperature scale,近似绝对温标aqueous vapour,水汽arc suppressing varstor,消弧电压敏电阻器arctic buoy,极地浮标area effect,面积影响area location,区域定位area of cross section of the main air flow,主送风方向横截面积argon-ion gun,氩离子枪annular chamber,环室argon ionization detector,氩离子化检测器arithmetic logic unit(ALU),算术逻辑运算单元arithmetic mean,算术平均值arithmetic weighted mean,算术加权平均值arithmetical mean deviation of the(foughness)profile,(粗糙度)轮廓的算术平均偏差arm error,不等臂误差armature,动铁芯array,阵,阵列array configuration,阵排列arrester varistor,防雷用电压敏电阻器articulated robot,关节型机器人artificial defect,人工缺陷artificial environment,人工环境artificial field method instrument,人工电场法仪器artificial intelligence,人工智能artificial seawater,人工海水ash fusion point determination meter,异步通信接口适配器asynchronous input,异步输入asynchronous transmission,异步传输atmidometer,蒸发仪,蒸发表atmometer,蒸发仪;蒸发表atmoradiograph,天电强度计atmosphere,气氛atmospheric counter radiation,天气向下辐射atmospheric electricity,大气电atmospheric opacity,大气不透明度atmospheric pressure,气压atmospheric pressure altimeter,气压高度计atmospheric pressure ionization(API),大气压电离atmospherics,天电;远程雷电atom force microscope,原子力显微镜atomic absorption spectrometry,原子吸收光谱法atomic fluorescence spectrophotometer,原子荧光光度计atomic fluorescence spectrometry,原子荧光光谱法atomic mass unit,原子质量单位atomic number correction,原子序数修正atomin spectrum,原子光谱atomic-absorption spectrophotometer,原子吸收分光光度计atomization,原子化atomizer,原子化器attenuation,衰减attenuation coefficient,衰减系数attenuation length,衰减长度attenuator,衰减器attitude,姿态attitude transducer[sensor],姿态传感器audio monitor,监听器audio-frequency spectrometer,声频频谱仪audit,审核Auger electron energy spectrometer(AEES),俄歇电子能谱仪Auger electron image,俄歇电子象Auger electron spectrometer,俄歇电子能谱仪Auger electron spectroscopy(AES),俄歇电子能谱法aurora,极光auto-compensation logging instrument,电子自动测井仪auto-compound current transformer,自耦式混合绕组电流互感器auto-polarization compensator,自动极化补偿器autocorrelation function,自相关函数automatic a.c.,d.c.B-H curve tracer,交、直流磁特性自动记录装置automatic balancing machine,自动平衡机automatic control,自动控制automatic control souce of vacuum,真空自动控制电源automatic control system,自动控制系统automatic data processing,自动数据处理automatic exposure device,自动曝光装置automatic feeder for brine,盐水溶液自动补给器automatic focus and stigmator,自动调焦和消象散装置automatic level,自动安平水准仪automatic levelling compensator,视轴安平补偿器automatic/manual station;A/M station,自动/手动操作器automatic programming,自动程度设计automatic radio wind wane and anemometer,无线电自动风向风速仪automatic railway weigh bridge,电子轨道衡automatic scanning,自动扫查automatic spring pipette,自动弹簧式吸液管automatic testing machine,自动试验机automatic titrator,自动滴定仪automatic tracking,自动跟踪automatic vertical index,竖直度盘指标补偿器automatic weather station,自动气象站automation,自动化automaton,自动机auxiliary attachment,辅件auxiliary controller bus(ACB),辅助控制器总线auxiliary crate controller,辅助机箱控制器auxiliary devices,辅助装置auxiliary equipment(of potentiometer),(电位差计的)辅助设备auxiliary gas,辅助气体auxiliary output signal,辅助输出信号auxiliary storage,辅助存储器auxiliary terminal,辅助端auxiliary type gravimeter,助动型重力仪availability,可用性available time,可用时间average,平均值average availability,平均可用度average nominal characteristic,平均名义特性average sound level,平均声级average value of contarmination,污染的平均值average wind speed,平均风速axial clearance,轴向间隙axial current flow method,轴向通电法axial load,轴向载荷axial sensitivity,轴向灵敏度axial vibration,轴向振动axis of rotation,摆轴;旋转轴axix of strain gauge,应变计[片]轴线。
Energy Efficiency in Manufacturing
Energy Efficiency in Manufacturing Energy efficiency in manufacturing is a crucial aspect of sustainable production practices. It not only helps reduce costs for companies but also minimizes the environmental impact of their operations. By optimizing energy usage, manufacturers can lower their carbon footprint and contribute to a greener future. However, achieving energy efficiency in manufacturing is not always easy and requires a concerted effort from all stakeholders involved. One of the key challenges in improving energy efficiency in manufacturing is the initial investment required to upgrade equipment and processes. Many companies may be hesitant to invest in energy-efficient technologies due to the high upfront costs involved. However, it is important to recognize that these investments can lead to long-term savings through reduced energy consumption and lower operating costs. Incentives and subsidies from governments and other organizations can help offset some of these initial expenses and encourage more companies to invest in energy efficiency. Another obstacle to energy efficiency in manufacturing is the lack of awareness and knowledge about available technologies and best practices. Many manufacturers may not be aware of the latest energy-efficient solutions or how to implement them in their operations. This highlights the need for more educationand training programs to help companies understand the benefits of energyefficiency and how to achieve it. Collaboration with industry associations, research institutions, and government agencies can also help disseminate information and promote best practices in energy efficiency. Furthermore, organizational culture and mindset play a significant role in determining the success of energy efficiency initiatives in manufacturing. Companies need tofoster a culture of sustainability and environmental responsibility to prioritize energy efficiency in their operations. This may require a shift in mindset and a commitment from top management to support and prioritize energy-saving initiatives. Employee engagement and involvement are also crucial in driving energy efficiency efforts, as frontline workers often have valuable insights and ideas for improving energy efficiency on the shop floor. In addition to internal factors, external factors such as regulatory requirements and market demands can also influence the level of energy efficiency in manufacturing. Stricter environmental regulationsand customer expectations for sustainable products are pushing manufacturers to adopt more energy-efficient practices. Companies that fail to meet these requirements may face penalties or lose out on business opportunities. Therefore, staying ahead of regulatory trends and aligning with market demands is essential for manufacturers looking to improve their energy efficiency performance. Collaboration and partnerships with suppliers, customers, and other stakeholders can also help manufacturers enhance their energy efficiency efforts. By working together with supply chain partners, companies can identify opportunities for energy savings and implement joint initiatives to reduce energy consumption across the entire value chain. This collaborative approach not only improves energy efficiency but also fosters stronger relationships with stakeholders and enhances the overall sustainability of the manufacturing industry. In conclusion, energy efficiency in manufacturing is a multifaceted issue that requires a holistic approach to address. By investing in energy-efficient technologies, raising awareness about best practices, fostering a culture of sustainability, and collaborating with stakeholders, manufacturers can make significant strides towards improving their energy efficiency performance. Ultimately, prioritizing energy efficiency not only benefits the bottom line but also helps protect the environment and create a more sustainable future for generations to come.。
核心和非核心结构【外文翻译】
外文翻译原文Core vs.Non-Core FrameworkMaterialSource:/articles/2009/01/core-vs-noncore-fra mework,2004.02 Author: Neil Mac Allister, Richard Evans, and Katherine WallaceAcross the pharmaceutical industry,dramatic and durable changes to the operating environment are calling for modifications to companies’strategies and structures. The industry is facing a period of eroding pricing power, falling growth in the consumption of branded drugs, and tighter regulatory standards.As a consequence, companies are finding that revenue growth is becoming both slower and more volatile, and that returns on R&D spending are pushing below the cost of capital. We recommend changes to the current business model that include smaller, more efficient and more flexible cost structures, as well as the increase of efforts to mitigate revenue volatility.Our aim in this article is to apply an analytical framework for how to think about an evolving business model for pharmaceutical companies.The pharmaceutical industry is operating within an increasingly unfavorable political,economic,and regulatory environment,largely as a result of negative public opinion,rising healthcare costs,and increasing involvement from governments in the purchase,reimbursement and market approval of pharmaceuticals.These pressures are being brought to bear on an industry whose structures reflect past rather than present and future conditions,particularly with costs that are both too high and too inflexible.To estimate profitability over long time cycles,we compared year one R&D spending to year 10 net income, a rate of return that has been falling for as long as we can measure. Apparent returns are no longer higher than the industry’s cost of capital.Profits must exceed costs of capital for a business to remain viable.Inpharma, this gap can be widened by either increasing the revenue return generated by each dollar spent on R&D or by reducing the cost of commercializing the industry’s innovationsWe also see revenue growth slowing and becoming more volatile, consisting of interspersed periods of growth and contraction. As real pricing power and per-capita branded volume effects fade,revenue growth slows.Historically,real pricing power and per-capita volume growth made steady, predictable contributions to total revenue growth and more pricing power could be applied when needed to stabilize growth;as they fade,revenue growth defaults to —or at least toward —the remaining variables: population growth and product mix. Population growth is too small to matter,leaving mix as the dominant variable.Product mix is extremely volatile from period to period, consisting of significant gains (i.e. new products) and significant losses (i.e. patent expiry) interspersed at uneven intervals. Absent the buffers of real pricing and per-capita volume gains,it follows that future revenue patterns contain both ups and downs; unless cost structures become more flexible, periods of revenue contraction will result in outright earnings losses.Competitive differentiation must consider whether ownership or control of the activity is important for competitive and/or strategic reasons,and whether or not the company is able to perform the activities at such a level that it provides them with a point of differentiation against their competitors. The availability of sourcing options needs to examine whether or not there are ample vendors performing the activity that can deliver world-class quality at a cost-effective price.Core activities•Enable the overarching business strategy•Are key components of the company’s value proposition•Are a major source of durable competitive advantage (e.g.intellectual capital)•Protect intellectual property•Have internal capabilities that cannot be matched or exceeded by outside vendors or other partnersNon-core activities•Can be pushed outside of pharma to improve flexibility within cost structure •Are general “supportive” activities to the pharma value proposition•Can be conducted by third parties and match or exceed internal quality/economicsDetermining core and non-core activities for your company will depend upon the benefits of outsourcing or partnerships versus keeping the function in-house and the strategic importance of the function relative to your company. The importance and value of these two dimensions will differ depending upon the product stage. For example, elements of screening in discovery that have low risk of IP exposure may be considered non-core,while aspects of lead optimization in development involving high risks of IP exposure are likely to be considered core activities.We have broken R&D down into four phases:basic research,discovery, preclinical, and development. By examining the key activities within each of these phases, a core or non-core determination can be made based on the strategic context underlying each element.In some cases the activity may be “on the fence”–this simply means that the determination will vary for each company depending on their internal capabilities, capacity, and strategic direction.Basic research functions are core only if they provide a point of differentiation for the company. For example, in therapeutic areas in which very few companies are working on a limited number of mechanisms, target identification and validation activities may be a point of differentiation. Beyond this, the activities are thought to be non-core. In therapeutic areas with multiple mechanisms and multiple companies competing,it is likely in the best interest for the company to change their orientation to search for and evaluate targets rather than to generate IP.Basic research around different mechanisms may be found within academia,but increasingly commercial organizations are developing platforms necessary to conduct these activities – providing a variety of capable vendors and partners.Discovery efforts should be considered for outsourcing because they are easily systematized and — in some instances — are automated. For companies that have already built discovery capabilities in-house — e.g. high through-put screening —the cost effectiveness and quality standards must be evaluated against outsourcing options.Preclinical activities are increasingly being outsourced in order to take advantage of specialist modeling capabilities. Assembly of the information gathered in preclinical development will remain in-house,while the actual generation of perspectives will move to outsourcing. The vendor environment for preclinical work,particularly in specialty areas,is maturing quickly,allowing pharmaceutical companies to tap into new efficiencies by outsourcing in these areas.In clinical development,strategy,development plans, and management functions should always be kept in-house. In all development activities, consideration should be given to two rules: the protection of critical relationships and the active management of the outsourcing.Another factor that influences outsourcing in development is the philosophy of the company.This should be considered when examining “on the fence” activities; some companies may view certain activities as generic skills where others see a core strategic advantage.Once the core vs.non-core determination has been made,companies must assess the optimal outsourcing approach for non-core activities.We have divided outsourcing approaches into two categories, functional and integrated, based on the degree of integration necessary between the sponsor and the vendor. As a general rule, if a core activity must be outsourced for some reason, integration with vendors is essential. For non-core activities, a functional approach is usually best.Integrated outsourcing arrangements would focus on accelerated decision making and minimizing time to proof of concept.Vendors would work with integrated workflows and pre-established standard operating procedures (SOPs), with IT decision support. In many cases,integrated outsourcing calls for a component of risk sharing in the compound’s success,and an economic model should be established in which there is an emphasis on quality, not quantity, and it is in the interest of the vendor to “kill” compounds as soon as they begin showing unfavorable results.Clinical data management has been one area of development activity that is routinely outsourced and has been increasingly off-shored. As a very basic example of the application of the core/non-core decision process, we have used the example of the partnership between Accenture and Wyeth to illustrate the use of our framework.To begin,from the company perspective,data management is not a differentiating activity. It does not provide a point of competitive advantage over a competitor, and it is unlikely that any one company possesses a leadership position in this area.As a result,in outsourcing there is very little executional or IP risk associated with outsourcing data management.In terms of capacity,many companies have been moving away from holding data management capabilities in-house over the last decade,so many may not even have internal resources.Additionally,there are no key relationships associated with the data management function.From a company situation standpoint,all variables point toward data management being a non-core activity.The vendor situation for data management points toward the same conclusion. Sophisticated vendor environments already exist for conducting data management, particularly in off-shore communities. Because of the ample supply of vendors, quality standards are high and generally well trusted, and competition has lowered prices to a very attractive level.With both situations pointing toward outsourcing, there is very little by way of company situation that would compel a pharmaceutical company to keep data management activities in-house. Below we have profiled an example of Wyeth’s shifting of data management to an outsource partner and the impact the relationship has had.Wyeth and Accenture formed a deal in which Wyeth gave its entire data management operation to Accenture over a 10-year contact.In doing so,half of Wyeth’s 300 data management positions were eliminated,and the remaining employees were transferred to Accenture for employment. The more mundane data management tasks, such as data entry, would be sent offshore to India to make use of Accenture’s specialized facilities. In order to get the contract, Accenture had to accept a risk-sharing arrangement and meet highly specific performance criteria all while cutting Wyeth’s data management costs by 50%.The result was a deal that provides significant cost savings for Wyeth, shifting fixed costs to variable costs while also tapping into new capabilities through Accenture’s service delivery center in India. New efficiencies can also be reached; functions that would have taken Wyeth more than 100 days must be reduced to about 20 days under the deal, or Accenture will have to pay Wyeth.In contrast to the data management example, program management activities can be examined as an extreme illustration of an activity that is core and must be kept in-house.As a result,we do not have a case studied to apply to this framework because we are not aware of outsourced program management arrangements.While specific company situations will vary,all companies will find a very high executional and IP risk associated with outsourcing the program management function. Program management is one of the remaining areas where companies can hold a significant leadership advantage over competitors and continue to buildinternal know-how (intellectual capital).Additionally, it is critical that internal program management teams develop and leverage key relationships along the value chain.The vendor situation further reinforces that program management remains in-house.While program management functions may be a portion of outsourcing specific activities, vendors solely dedicated to program management do not readily exist. Even if an extreme company situation dictated that program management activity be outsourced, a fully integrated outsourcing approach would be necessary. This arrangement would require oversight from the pharma company and would therefore result in significant overlap of responsibilities.Across the industry’s R&D functions,significant un-tapped degrees of freedom exist for making the cost base more variable. Opportunities for flexibility gains have to be balanced against IP and organizational know-how risks; in general the economic value of flexibility gains increases toward the sell end of the continuum while IP concerns lessen. Means for reducing revenue volatility clearly exist, including co-development of products and/or sharing of commercial rights/returns with commercial partners, and expansion of effective portfolio size in partnership with passive investors.Whether such volatility gains are worth the associated costs can and should be analyzed.Determining a company's degrees of freedom and how they may be leveraged is a two-step process. First, this general framework of core vs. non-core needs to be fine-tuned by persons having greater proximity to each component of the value chain (in general),and to the company’s business circumstances and organizational status (in particular).Second,available degrees of freedom (i.e. owned non-core functions)should be prioritized according to likely gains, associated risks,and the extent to which well-developed external platforms for performing these functions exist.译文核心和非核心结构资料来源:/articles/2009/01/ core-vs-noncore-framework作者:尼尔·麦克利斯特,理查德·埃文斯,凯瑟琳·沃利斯在整个制药行业,戏剧性和持续性的对于经营环境的改变,正在呼吁对公司的战略和结构的修改。
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An Analytical Energy Consumption Model for Packet Transfer over Wireless LinksJavad Vazifehdan,R.Venkatesha Prasad,Martin Jacobsson,and Ignas NiemegeersAbstract—We provide a detailed analytical model for esti-mating the total energy consumed to exchange a packet over a wireless link.Our model improves many of the current models by considering details such as consumed energy by processing elements of transceivers,packet retransmission,reliability of links,size of data packets and acknowledgments,and also the data rate of wireless links.To develop the model,we use experimental results based on IEEE802.15.4devices to show that consumed energy for receiving erroneous packets is comparable to the consumed energy for receiving error-free packets.Index Terms—Energy consumption,analytical modeling,wire-less networks.I.I NTRODUCTIOND ATA communication with high energy efficiency is animportant requirement in wireless networks.To address this requirement,we need accurate models of energy consump-tion.Without the use of such models,any designed mechanism may not be optimal and any analysis may result in poor approximation.In this letter,we develop a mathematical model for energy consumption of nodes for packet exchange over wireless links. Inclusion of details such as consumed energy by processing elements of transceivers,reliability of wireless links,packet retransmissions at the MAC(medium access control)layer, size of data and acknowledgment packets,and data rate of wireless links makes our model very detailed compared to the other models[1]–[3].Studies such as[1],[2],only model consumed energy during a single transmission and reception of a packet.They do not take into account the effect of packet retransmission at the MAC layer on the energy consumed for packet exchange. By taking into account the effect of packet retransmission, our model brings into the picture the effect of reliability of wireless links on the energy consumption of nodes.Our model also enhances the proposed model in[3]by limiting the number of times that a lost packet is allowed to be retrans-mitted.It is assumed in[3]that there is no limitation on the number of transmission attempts.Furthermore,[3]assumes, without any verification,that the same amount of energy is consumed for receiving lost and error-free packets.We used 2.4GHz IEEE802.15.4devices to verify this assumption by showing that a high percentage of lost packets over a wireless link are discarded after being completely detected by the receiver.These packets are discarded due to CRC(cyclic Manuscript received April3,2011.The associate editor coordinating the review of this letter and approving it for publication was S.Gupta.The authors are with Delft University of Technology,Mekelweg4,2628 CD,Delft,The Netherlands(e-mail:{J.Vazifehdan,R.R.Venkateshaprasad, M.Jacobsson,I.Niemegeers}@tudelft.nl).Digital Object Identifier10.1109/LCOMM.2011.111611.110729redundancy check)failure,which is performed on detected and reconstructed packets at the receiver.This implies thatthe consumed energy for receiving lost packets is comparable with the consumed energy for receiving packets successfully. The rest of this letter is structured as follows:We present the energy consumption model in Section II.In Section III,wedetermine the expected number of transmission attempts of a data packet and its acknowledgment.We present experimentaland simulation results in Section IV.We conclude in SectionV.II.E NERGY C ONSUMPTION M ODEL FOR P ACKETE XCHANGE OVER W IRELESS L INKS Consumed energy by nodes during packet transmissioncould be abstracted into two distinct parts[1],[2].Thefirst part represents the energy consumed by the processing circuitof the transmitter(baseband processing).The second part represents the energy consumed by the power amplifier ofthe transmitter to generate the required output power fordata transmission over the air.On the other hand,the energy consumed by a node to receive a packet could be abstracted byonly one part,which is the energy consumed by the receivingcircuit including the low noise amplifier(LNA)of the receiver. Let(u,v)denotes the wireless link between a sender u and a receiver v.Let r be the rate at which u transmits data to v over the physical link(u,v),P t be the power required to run the processing circuit of the transmitter,P r be the power required to run the receiving circuit,P u,v be the transmissionpower from u to v,andκbe the efficiency of the poweramplifier.The energy consumed by u to transmit a packet of length x bits over(u,v)isεu,v(x,r)=(P t+P u,vκ)T=(P t+P u,vκ)xr(1) in which T is the time required to transmit x bits with the rate r bps.The energy consumed by v to receive the packet from u isωu,v(x,r)=P rrx.(2) The transmission power P u,v could be the maximum transmis-sion power of nodes P max.Alternatively,it could be the power adjusted due to a transmission power control scheme[4].In accordance with wireless technologies IEEE802.11and IEEE802.15.4,we assume that the receiver transmits an acknowledgment to the sender for each correctly received packet.If the sender does not receive an acknowledgment, it will retransmit the packet.This may happen because ei-ther the packet or its acknowledgment is lost.The sender retransmits the packet until it receives an acknowledgment,1089-7798/12$31.00c⃝2012IEEEor the maximum number of transmission attempts M is reached.Therefore,a packet or its acknowledgment might be transmitted m≤M times.This means,the actual energyconsumed to exchange a packet over a wireless link must include the energy consumed during retransmissions as well. Let X∈{1,2,...,M}be the number of times that a packet is transmitted,including thefirst transmission,and Y∈{0,1,2,...,M}be the number of acknowledgments trans-mitted for the packet.For the time being,let us assume that the energy consumed by the receiver for receiving and decodinga corrupted packet is the same as the energy consumed for receiving an error-free packet.In Section IV,we shall verify this assumption.Let L d(bits)denote the size of the data packet transmitted from u to v,L a(bits)denote the size of the acknowledgment,R d denote the data rate with which the data packet is transmitted by u,and R a denote the data rate by which the acknowledgment is transmitted by v.The total consumed energy by u to deliver the packet to v ise t(u,v)=Xεu,v(L d,R d)+Yωv,u(L a,R a).(3)Similarly,the total energy consumed by v to receive the packet ise r(u,v)=Xωu,v(L d,R d)+Yεv,u(L a,R a)=X P r L dR d+Y(P t+P v,uκ)L aR a.(4)The value of X and Y depends on reliability of the forwardlink(u,v)for data packets and reliability of the reverse link (v,u)for acknowledgments.Thus,the total consumed energy by nodes to exchange a packet over a wireless link dependson reliability of the links as well.It may even happen that the receiver consumes more energy compared to the sender.This of course depends on various parameters in(3)and(4).In the next section,we determine the probability densityfunction(PDF)of X and Y.This allows us to determine the expected number of times that a data packet is trans-mitted E[X]as well as the expected number of times that an acknowledgment is transmitted for the data packet E[Y]. They are needed to calculate the expected amount of energy consumed by the sender and the receiver to exchange a data packet,which are as follows:⎧⎨⎩E[e t(u,v)]=E[X](P t+P u,vκ)L dR d+E[Y]P r L aR aE[e r(u,v)]=E[X]P r L dR d+E[Y](P t+P v,uκ)L aR a.(5)III.E XPECTED N UMBER OF T RANSMISSION A TTEMPTS OFD ATA AND A CKNOWLEDGMENT P ACKETSA packet will be transmitted m times if the packet itself or its acknowledgments is lost in the last m−1transmission attempts.Therefore,Pr{X=m}={(1−pq)m−1pq,m=1,...,M−1, (1−pq)M−1,m=M,(6)where p is the delivery probability of a data packet of size L d bits transmitted over(u,v),and q is the delivery probability of an acknowledgment of size L a bits transmitted over(v,u). From(6),we haveE[X]=M(1−pq)M−1+M−1∑m=1mpq(1−pq)m−1=1−(1−pq)Mpq.(7) To derive(7),we used the identity∑nm=1z m=z1−z n(1−z)2−nz n+11−zconsidering z=1−pq and n=M−1.Note that for sufficiently large M,(1−pq)M→0.Thus,we can writethat E[X]→1pq.This means,for any limited value of M,wehave E[X]≤1pq.Next,we determine Pr{Y=m},∀m∈{0,1,...,M}.If a data packet is lost during all possible transmission attempts, no acknowledgment will be transmitted for it.Hence,Pr{Y= 0}=(1−p)M.On the other hand,an acknowledgment will be transmitted M times for a data packet,if the data packet is received correctly in every transmission attempt,but all M−1 acknowledgments transmitted for it are lost.Thus,Pr{Y= M}=p M(1−q)M−1.Let us calculate the probability of transmitting0<m<M acknowledgments for a data packet.An acknowledgment is transmitted only when the data packet is correctly received. Of course,a packet might be received correctly after a number of transmission attempts.If the transmitted acknowledgment for the packet is lost,the sender will retransmit the packet. Hence,another acknowledgment will be transmitted,if the data packet is again received correctly after a number of attempts.We should also notice that the maximum number of transmission attempts of a data packet is limited,which adds to the complexity of the analysis.To determine Pr{Y=m,0<m<M,we consider two possible cases.In thefirst case,the M tℎtransmission of the data packet never happens,because the sender receives an acknowledgment for the packet before reaching the maximum transmission attempts M.In such a case,m−1out of the first n−1,∀n∈{m,m+1,m+2,...,M−1},transmission attempts of the data packet could be successful,but all m−1acknowledgments transmitted for it should be lost.The m tℎtransmission of the data packet must be successful,and its acknowledgment must also be received successfully.The probability of this event isE1=M−1∑n=m(n−1m−1)p m−1(1−q)m−1(1−p)n−1−(m−1)pq.In the second case,the sender transmits the data packet M times,because it has not received an acknowledgment after M−1attempts.Here,we face two subcases.In the first subcase,m−1out of thefirst M−1transmission attempts of the packet are successful,but all its m−1 acknowledgments are lost.The M tℎtransmission attempt of the packet is also successful,which triggers transmission of the m tℎacknowledgment.The probability of this event isE2=(M−1m−1)p m−1(1−p)M−1−(m−1)(1−q)m−1p.In the second subcase,m out of the first M −1transmission attempts of the packet are successful,but all m acknowledg-ments transmitted for it are lost.The M tℎtransmission attempt of the packet fails,which prevents transmission of another acknowledgment.The probability of this event isE 3=(M −1m)p m(1−p )M −1−m (1−q )m (1−p ).The probability of transmitting 1≤m ≤M −1acknowledg-ments for the packet is then E 1+E 2+E 3.In summary,Pr {Y =m }=⎧⎨⎩(1−p )M ,m =0;E 1+E 2+E 3m =1..M −1;p M (1−q )M −1,m =M.(8)Given p and q ,we can compute E [Y ]for any value of M usingits PDF given by (8).Unfortunately,no closed-form expression could be found for E [Y ]when M is finite.However,if there is no limitation on the number of transmission attempts of a packet,M →∞,we can find a closed-form expression.In such a case,a packet can be retransmitted as many times as required until the receiver receives the packet successfully.Therefore,the expected number of times that an acknowledg-ment is transmitted for a packet is simply E [Y ]=1q .As aresult,for any limited value of M we have E [Y ]≤1q .Using (5)and considering the two inequalities,E [X ]≤1pq and E [Y ]≤1q ,the expected energy consumed by sender and a receiver to exchange a packet over the wireless link is upper-bounded as⎧⎨⎩E [e t (u,v )]≤(P t +P u,v κ)L d pqR d +P r L a qR a E [e r (u,v )]≤P r L d pqR d +(P t +P v,u κ)L aqR a (9)where the equality happens if M is unlimited.IV.E XPERIMENTAL AND S IMULATION R ESULTS According to IEEE 802.11and IEEE 802.15.4standards,there are several possibilities in which a packet could be lost.The packet may not be detected by the physical layer (PHY)at all due to low received signal to noise ratio or due to corrupted preamble.Even if a packet is detected completely,it may contain erroneous bits.Some errors might be corrected by FEC (Forward Error Correction)techniques.Nevertheless,some bits may still remain erroneous after FEC.Then,CRC is performed to ensure the reception of error-free packets to higher layers.If a packet fails to pass CRC,it will be discarded (lost).We used T-mote devices based on the CC2420chipset (IEEE 802.15.4)to show that a high percentage of packets lost over a wireless link are CRC-failed packets.Note that CRC is performed on packets which have been detected completely (like error-free packets).Hence,we may conclude that most of the time nodes consume the same amount of energy to detect a lost packet as that of receiving the packet correctly.To verify this,we modi fied T-mote devices to report CRC-failed packets too.Only packets which have not been detected at all were not reported to higher layers.The receiver was placed at different distances from the sender to have different signal strengths.At each location,100000packets were transmitted by the sender.The receiver counted both error-free received packets and CRC-failed packets.As Fig.1shows,even if only−Mean RSSI [dBm]P e r c e n t a g e o f R e c e i v e d /D e t e c t e d P a c k e t s(a)Packet size =10Byte−Mean RSSI [dBm]P e r c e n t a g e o f R e c e i v e d /D e t e c t e d P a c k e t s(b)Packet size =100ByteFig.1.Percentage of received and detected packets as a function of the mean received signal strength indicator (RSSI).(a)Our model(b)Comparison with other modelsFig.2.Total energy consumed by a sender and a receiver to exchange a packet over a physical link.We assumed δ=1×10−4,P t =P r =100mW,P u,v =200mW,κ=1,R d =R a =250kb/s,M =4,L a =30B.Size of the preamble is one octet.10%of the packets are received error-free,around 55%(65%)of them have been detected (correct reception or erroneous)for 10Byte (100Byte)packet sizes.That is,when practically there is no link between the two nodes due to high packet drop rate,many of the transmitted packets have been detected completely.When quality of the link improves,the percentage of detected packets gets closer to 100%.For instance,in Fig.1(b),when 55%of transmitted packets are received error free,92%of them have been detected.Only 8%have not been detected at all.Thus,if there is a link between two nodes with an acceptable quality,the consumed energy for reception of lost packets is almost equal (on an average)to the consumed energy for reception of error-free packets.We also present simulation results to verify the accuracy of our energy consumption model.In our simulatons,100000data packets are transmitted over a physical link between a sender and a receiver to measure the average amount of energy consumed to exchange a packet.εu,v (x,r )and ωu,v (x,r )are consumed for each transmitted and received packet,respec-tively.Nevertheless,if an erroneous bit is detected at the preamble of a packet,no energy is consumed for reception of that packet 1.We also compute theoretical values and their upper bounds using (5)and (9),respectively.To this end,1Weassume bit errors in a packet occur independently from each other.we compute the delivery probability of a packet of size l∈{L d,L a}bits as(1−δ)l,whereδis the bit error rate of the link.As Fig.2(a)shows,the analytical model can accurately predict the total energy consumed to exchange a packet over a link.The upper bound tends to be very tight when packet size decreases.Moreover,we observe in Fig.2(b)that neglecting the impact of MAC retransmissions and energy consumption of processing elements of transceivers by existing models can result in substantial inaccuracy in estimating the total energy required for packet exchange over wireless links.In Fig.2(b), PAMAS,used in[5],refers to a model which neglects the impact of MAC retransmissions as well as energy consumption by processing elements of transceivers.BAMER,used in[6], refers to a model which considers the impact of MAC re-transmissions but neglects energy consumption by processing elements of transceivers.MTTPR,used in[7],is similar to PAMAS with the difference that MTTPR considers the energy consumed by processing elements of the receiver.We observe that existing models are not very accurate compared to our model especially when the packet length is higher.V.CONCLUSIONWe provided an analytical model for energy consumed to exchange a packet over a wireless link.The accuracy of the model was verified using experimental and simulation results. 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