Design and optimisation of the lining of a tunnel in the presence of expansive clay levels
“背山面水”理念在岭南山地建筑中的运用——华为荔枝园项目中的小气候营造
于建筑通风,春夏季较热的空气吹过水面,降低空气温度的同时带动水汽上行促成降雨,雨水通过阳坡顺畅排走汇入河流或池塘,形成一种良性循环。
有研究表明,坡向作为重要地形因子,通过改变太阳辐射和水分分布,能够引起水热因子的系列变化,影响地上植物的发育,从而对植物群落类型等产生重要影响[1];建筑背靠山体,可以抵御秋冬季冷空气流,以上元素综合构成一个良好的小气候环境。
2岭南建筑“岭南”广义上理解为五岭(大庾岭、骑田岭、越城岭、萌渚岭、都庞岭)以南,以广东、广西与湖南、江西四省边境处的五岭山脉为界,与内陆相隔[2]。
关于岭南地区的气候属性,一说属亚热带季风气候,夏热冬暖;也有学者根据1959年版《中国综合自然区划》将广州、南宁、闽南均称为“南亚热带”,与岭北亚热带地区在气候上有较大不同,建筑具有典型的回应热带气候的技术特征[3]。
为适应其地域性所造成的炎热多雨、湿度高的气候特点,对室内空间起到自然通风的作用,在空间布局上有高度的要求[4]。
传统的岭南民居利用建筑的总体布局和空间组织来达到通风的目的,以尽快排出室内潮湿闷热的空气[5]。
村落在选址上往往遵循“背山面水”的理念,坐北朝南,摘要 “背山面水”是中国传统理念中理想的择居基本模式之一,其背后蕴含着丰富的理念与实践基础。
以岭南山地建筑——华为荔枝园项目为例,展示了在“背山面水”理念蕴含的环境观念引导下,采取主动介入策略整理地形、布置建筑、建立山地标高体系,营造优质小气候环境。
体现出传统营造理念在现代的延续与应用。
关键词 背山面水;岭南山地建筑;小气候营造中图分类号 TU241文献标识码 ADOI 10.19892/ki.csjz.2023.07.46Abstract The concept of “backing the mountain and facing the water” is one of the basic models for the ideal choice of residence in traditional Chinese traditional theories, which have a rich conceptual and practical basis. Huawei Lizhiyuan project, a mountainous building in Lingnan, is used as an example to demonstrate how to follow the environmental concepts embedded in the concept of “backing the mountain and facing the water”, and adopt proactive strategies to organize the topography, to arrange buildings and to establish a mountain height system, thereby creating a qualified microclimate. It reflects the utilization of the traditional concept in modern continuation and applicati on.Key words backing the mountain and facing the water; Lingnan mountain architecture; microclimate creati on1中国传统理念中的“背山面水”“背山面水”是中国传统理念中选择理想居住地的基本模式之一,其看似简单的模式背后蕴含着丰富的理念与传承数千年的实践基础。
Engineering Optimization and Design
Engineering Optimization and DesignEngineering optimization and design are crucial aspects of the engineering process, as they involve finding the best possible solution to a problem withinthe given constraints. This can include maximizing efficiency, minimizing cost, reducing waste, or improving performance. However, achieving optimization and design excellence often presents challenges and requires a deep understanding of the problem at hand, as well as the ability to think critically and creatively. In this discussion, we will explore the complexities of engineering optimization and design, considering various perspectives and potential solutions. One of the primary challenges in engineering optimization and design is the need to balance competing objectives. For example, in the design of a new aircraft, engineers may need to optimize for factors such as fuel efficiency, speed, and passenger comfort. However, these objectives are often interconnected, and improving one may come at the expense of another. This requires a careful and nuanced approach to optimization, considering trade-offs and compromises to find the best overall solution. This can be a difficult and time-consuming process, as it often involves iterative design and analysis to refine the solution. Another challenge in engineering optimization and design is the need to account for uncertainty and variability. In many engineering applications, the operating environment and conditions are not fully known or predictable. This can make it difficult to optimize a design for all possible scenarios, as there may be trade-offs between robustness and performance. Additionally, there may be variability in material properties, manufacturing processes, or other factors that can impact the performance of a design. Accounting for these uncertainties requires aprobabilistic approach to optimization, considering the likelihood of different outcomes and designing for resilience. In addition to technical challenges, there are also human and organizational factors that can impact engineering optimization and design. For example, engineers may face pressure to meet tight deadlines or cost targets, which can limit the time and resources available for optimization. There may also be conflicting priorities within an organization, with different stakeholders advocating for their own objectives. This can make it difficult to achieve consensus on the best approach to optimization and design, and may requirecompromise and negotiation to move forward. Additionally, there may be resistance to change or new ideas, particularly if they challenge established practices or beliefs. Despite these challenges, there are a variety of tools and techniques available to engineers to support optimization and design. For example, computer-aided design (CAD) software allows engineers to create and analyze virtual prototypes, enabling rapid iteration and exploration of design alternatives. Simulation and modeling tools can be used to predict the performance of a design under different conditions, helping to identify potential issues and opportunities for improvement. Additionally, optimization algorithms and techniques, such as genetic algorithms or simulated annealing, can be used to search for the best solution within a complex design space. Ultimately, achieving excellence in engineering optimization and design requires a combination of technical expertise, creativity, and collaboration. Engineers must have a deep understanding of the problem they are trying to solve, as well as the constraints and objectives that define the design space. They must also be willing to think critically and challenge assumptions, exploring new ideas and approaches to find innovative solutions. Collaboration with colleagues and stakeholders can also be valuable, bringing diverse perspectives and expertise to the table. By embracing these principles and leveraging the available tools and techniques, engineers can overcome the challenges of optimization and design, ultimately delivering better solutions for the world.。
核电专用轮槽数控铣床动态设计和结构优化
第21卷 第4期2006年12月北京机械工业学院学报Journa l of Be ijing Institute o fM ach i neryV o.l 21N o .4D ec .2006文章编号:1008-1658(2006)04-0070-03核电专用轮槽数控铣床动态设计和结构优化刘芳,杨庆东,刘国庆,龚国庆(北京机械工业学院 机械工程系,北京100085)摘 要:针对大型专用铣床CX056串联式的结构和它运动部件质量大的特点,用有限元法分析了该机床的动态特性。
将模态分析和谐响应分析的结果作为该大型铣床动态设计和结构优化的依据,通过设计不同的结构和预置不同系列的参数,以整机固有频率的提高为目标函数,应用经验法和比较法提出了机床大件和整机的优化设计方案。
关 键 词:动态设计;专用机床;动态特性;优化中图分类号:TH 122;TG 502 文献标识码:AD yna m ic design and structure opti m ization of the nuclear electri cityNC m illi ng m ach i ne for processi ng wheel troughLI U Fang ,YANG Q i n g dong ,LI U Guo q i n g ,GONG Guo qing(Depart m en t ofM echan ical Engi n eeri ng ,Beiji ng In stit u te ofM ach i nery ,Beiji ng 100085,Ch i na)Abstract :The dyna m ic pr operties o f the large scale specialm illing m ach i n e CX056is analyzed ai m ing at its tande m structure and heavy m ov i n g parts .The data calcu lated by m odal ana l y sis and har m on ic re sponse is the support for dyna m ic desi g n and str ucture opti m izati o n o f the m illi n g m achine .B ased on ex perience and co m parative m ethod ,d ifferent structures and para m eters in seri e s are a lso g iven and the opti m ized desi g n sche m e is advanced w ith frequency as the objective function .Key words :dyna m i c design ;specia lm achi n e too;l dyna m ic property ;opti m izati o n 核电专用双轴轴向轮槽数控铣床CX056是加工核反应堆内构件的非标超大专用设备。
3Optimization for finite element applications
Optimization for Finite Element Applications
Illustration of Finite Element
Finite elements of a lathe spindle
Optimization for Finite Element Applications
3 Optimization for Finite Element Applications
The power of an optimization program depends on the available preprocessing and analysis capabilities. Applications for 2-D and 3-D need both automatic and parametric meshing capabilities. Error estimate and adaptive control must be included because the problem’s geometry and mesh might change during the optimization loops.
The figures are expressed in terms of a percentage.
3 Optimization for Finite Element Applications
As engineers work with increasingly complex structures, they need rational, reliable, fast, and economical design tools. Over the past two decades, finite element analysis has proven to be the most frequently used method of identifying and solving the problems associated with these complicated designs.
“城村架构”中的一个砖墙原型——林君翰先生访谈
“城村架构”中的一个砖墙原型——林君翰先生访谈范路【期刊名称】《世界建筑》【年(卷),期】2014(000)007【总页数】4页(P22-25)【作者】范路【作者单位】清华大学建筑学院【正文语种】中文林君翰先生是香港大学建筑系的副教授和“城村架构”(RUF)工作团队的创始人。
“城村架构”成立于2005年,是一个非盈利机构。
它以建筑项目、学术研究、展览和写作等方式,积极介入中国大陆的乡村向城市转变的过程。
到目前为止,该机构团队已经在中国大陆完成了至少15个项目。
这些项目位于大陆不同地区的乡村,包括学校、社区中心、医院、乡村住宅、桥梁及增量规划策略等(图1、2)。
在这些项目中,坐落于陕西省石家村的“四季”住宅(2009-2012)格外引人注目。
它获得了2014年维纳博艮砖筑奖、2012年WA中国建筑奖优胜奖和2012年《建筑评论》杂志住宅奖。
林君翰先生通过设计这座乡村砖墙住宅,尝试发展一种关于中国传统合院住宅的可持续发展的现代原型。
他还试图通过使用当地已有建筑材料和工序,来沟通传统和现代。
本访谈旨在呈现石家村住宅中所运用的创新性设计策略,以及建筑师对项目背后社会问题的批判性思考。
范路:您为何想要于2005年成立“城村架构”?该组织的建筑学目标又是什么?林君翰:“城村架构”是我和约书亚·鲍乔弗(Joshua Bolchover)一起成立的,目的是探究中国的城市化问题。
我们认为,城市化会导致积极或消极的后果,而城市化的过程对于决定结果非常重要。
我们通过合作,试图找到将学术研究与实际项目相结合的方式。
我们的一个方法就是在项目进行过程中开展研究。
这不同于纯粹的分析性研究,还是对建筑项目所产生影响的检验。
我们常常会想,对一个单独的项目来说,它能否在乡村或更大的尺度上解决更多的问题?范路:在您看来,中国乡村向城市转变过程中的主要社会问题是什么?建筑又能以何种方式来应对这些问题?林君翰:在这一过程中有许多问题,也有许多可能。
【工程学科英语(整合第二稿)】 参考答案
Unit OneTask 1⑩④⑧③⑥⑦②⑤①⑨Task 2① be consistent with他说,未来的改革必须符合自由贸易和开放投资的原则。
② specialize in启动成本较低,因为每个企业都可以只专门从事一个很窄的领域。
③ d erive from以上这些能力都源自一种叫机器学习的东西,它在许多现代人工智能应用中都处于核心地位。
④ A range of创业公司和成熟品牌推出的一系列穿戴式产品让人们欢欣鼓舞,跃跃欲试。
⑤ date back to置身硅谷的我们时常淹没在各种"新新"方式之中,我们常常忘记了,我们只是在重新发现一些可追溯至涉及商业根本的朴素教训。
Task 3T F F T FTask 4The most common viewThe principle task of engineering: To take into account the customers ‘ needs and to find the appropriate technical means to accommodate these needs.Commonly accepted claims:Technology tries to find appropriate means for given ends or desires;Technology is applied science;Technology is the aggregate of all technological artifacts;Technology is the total of all actions and institutions required to create artefacts or products and the total of all actions which make use of these artefacts or products.The author’s opinion: it is a viewpoint with flaws.Arguments: It must of course be taken for granted that the given simplified view of engineers with regard to technology has taken a turn within the last few decades. Observable changes: In many technical universities, the inter‐disciplinary courses arealready inherent parts of the curriculum.Task 5① 工程师对于自己的职业行为最常见的观点是:他们是通过应用科学结论来计划、开发、设计和推出技术产品的。
优化设计 英文专著
优化设计英文专著Optimization Design: A Comprehensive GuideIntroduction:Chapter 1: Fundamentals of Optimization Design- Definition and importance of optimization design- Basic principles and goals of optimization design- Various approaches and methodologies used in optimization designChapter 2: Mathematical Modeling Techniques for Optimization Design- Overview of mathematical modeling in optimization design- Commonly used mathematical models and algorithms- Optimization techniques for linear and nonlinear problems Chapter 3: Multi-objective Optimization Design- Introduction to multi-objective optimization design- Pareto optimality and Pareto frontiers- Multi-objective optimization algorithms and applications Chapter 4: Genetic Algorithms in Optimization Design- Basics of genetic algorithms and their relevance to optimization design- Chromosomes, genes, and fitness functions in genetic algorithms - Applications of genetic algorithms in optimization design Chapter 5: Particle Swarm Optimization in Optimization Design - Overview of particle swarm optimization techniques- Swarm intelligence and social behavior in particle swarmoptimization- Implementing particle swarm optimization in optimization design Chapter 6: Artificial Neural Networks in Optimization Design- Introduction to artificial neural networks and their role in optimization design- Structure, training, and application of artificial neural networks in optimization- Case studies and examples of artificial neural networks in optimization designChapter 7: Real-Life Applications of Optimization Design- Optimization design in engineering and manufacturing- Optimization design in transportation and logistics- Optimization design in finance and investmentChapter 8: Future Trends in Optimization Design- Emerging technologies and methodologies in optimization design - Challenges and opportunities for optimization design in the digital age- Potential applications and impact of optimization design in various fieldsConclusion:- Recap of key concepts and techniques covered in the book- Final thoughts on the significance of optimization design in improving efficiency and effectiveness in various domains Appendix:- Glossary of key terms and definitions- List of references for further reading- Index for easy navigation and reference。
materials and design分区
materials and design分区Materials and design can be categorized into several areas including:1. Material selection: This involves choosing the appropriate materials based on their properties, such as strength, durability, conductivity, and aesthetic appeal. Factors such as cost, availability, and environmental impact also play a role in material selection.2. Material testing and analysis: Once materials are chosen, they need to be tested to ensure they meet the required standards and specifications. This includes mechanical testing, chemical analysis, and non-destructive testing methods.3. Manufacturing processes: Designing and optimizing the manufacturing processes to efficiently and accurately produce the desired product. This covers various methods such as casting, molding, machining, and additive manufacturing (3D printing).4. Product design and development: This involves the conceptualization and creation of products while considering factors like functionality, ergonomics, aesthetics, and market demand. CAD (Computer-Aided Design) software is commonly used to create detailed product designs.5. Material characterization: This includes studying the properties and behavior of materials at different scales, such as microscopic analysis, imaging techniques, and determining material properties like hardness, tensile strength, and thermal conductivity.6. Sustainable design: Considering the environmental impact of materials and products throughout their lifecycle, with a focus on reducing waste, minimizing resource consumption, and optimizing energy efficiency.These are just a few examples of how materials and design can be categorized. Each area is interconnected and requires expertise in various disciplines like materials science, mechanical engineering, industrial design, and manufacturing engineering.。
Optimization of InjectionWithdrawal Schedules for Natural Gas Storage Facilities
Optimization ofInjection/Withdrawal Schedules for Natural Gas Storage Facilities∗Alan HollandCork Constraint Computation Centre,Department of Computer Science,University College Cork,Cork,IrelandAbstractControl decisions for gas storage facilities are made in the face of ex-treme uncertainty over future natural gas prices on world markets.Weexamine the problem faced by owners of storage contracts of how to man-age the injection/withdrawal schedule of gas,given past price behaviorand a predictive model of future prices.Real options theory providesa framework for making such decisions.We describe the theory behindour model and a software application that seeks to optimize the expectedvalue of the storage facility,given capacity and deliverability constraints,via Monte-Carlo simulation.Our approach also allows us to determine anupper bound on the expected valuation of the remaining storage facilitycontract and the gas stored therein.The software application has beensuccessfully deployed in the energy trading division of a gas utility.1IntroductionThis work focuses on gas storage facilities that consist of partially depleted gas fields such as the Roughfield in the Southern North Sea,18miles from the east coast of Yorkshire.There is a shortage of gas storage facilities worldwide that has contributed to an increase in their value[9].There is,therefore,a greater incentive for optimizing its utilization given the rising costs in storage contracts. Many of these storage facilities were originally developed to produce natural gas.Fields can be converted to storage facilities,enabling gas to be stored within the reservoir,thousands of feet underground or under the seabed and withdrawn to meet peaks in demand.These facilities do not supply gas directly to domestic and industrial end users.Instead,they act as a storage facility for gas shippers and suppliers,allowing gas to be fed into a transmission system at times of peak demand(e.g.winter)or withdrawn from the grid and re-injected into the reservoir at times of low demand(e.g.summer).The movement of gas either into or out of the reservoir is based on“nominations”made by gas shippers as a result of demands placed on them by their end customers.These facilities have various deliverability rates depending on their size and physical ∗The author is very grateful to the energy trading team at Bord G´a is for their excellent advice and feedback.1attributes.The Roughfield in the North Sea has a deliverability of455GWh (1.5billion cubic feet)of gas per day and a total storage capacity of30TWh (100billion cubic feet)of natural gas at pressures of over200bar.It is currently the largest gas storage facility in the UK,able to meet approximately10%of current UK peak day demand.We examine the problem faced by owners of gas storage contracts of how to inject and withdraw natural gas in an optimal manner so that gas is injected when prices are lowest and withdrawn when prices are high.Storage contracts are typically of twelve months duration and the storage operators must be in-formed at the outset of each day whether they should inject or withdraw gas on that day.Gas prices exhibit a noticeable seasonality each year.We fo-cus upon the northern European market where prices drop in the summer as consumption for heating purposes decreases and rise in the winter as temper-atures drop.We model gas prices using a stochastic process and determine the expected-profit maximizing injection/withdrawal for an energy trader who wishes to decide whether to inject or withdraw gas for that day[5].The theory of real options is based on the realization that many business decisions have properties similar to those of derivative contracts used infi-nancial markets[11].A natural gas well can be thought of as a series of call options on the price of natural gas,where the strike or exercise price is the total operating and opportunity costs of producing gas[10],if we ignore operating characteristics.By operating a gas storage facility in the way that maximizes the expected cashflow with respect to the market’s view of future uncertainties and its risk tolerances for those uncertainties,one can subsequently maximize the market value of the facility itself.This paper is structured as follows:Section2presents a stochastic model for gas prices and optimization of the storage facility.It also discusses the depen-dencies of the model and the numerous input parameters that contribute to the complexity of the problem.Section3presents the optimization model required to solve the injection/withdrawal schedule for each Monte-Carlo simulation.A software application for energy traders that facilitates model configuration, solving and presentation of results in a graphical manner is also presented.We also discuss possible extensions in Section4before concluding.2Gas Storage ModelIn this section we outline the relevant inputs for the problem,present an equa-tion for determining inventory levels and describe the stochastic model we use to represent gas price movements.Difficulties arise when operating characteristics and extreme pricefluctua-tions are included in a pricing model[1].The exotic nature of storage facilities and gas prices requires the development of complex methodologies both from the theoretical as well as the numerical perspective.The operating character-istics of actual storage facilities pose a challenge due to the non-trivial nature of the opportunity cost structure.When gas is withdrawn from storage the gascannot be released again.Also,when gas is released the deliverability of the remaining gas in storage decreases because of the drop in pressure.Similarly, when gas is injected into storage both the amount and the rate of future gas injections are decreased.The opportunity costs and thus the exercise price varies nonlinearly with the amount of gas in the reservoir[7].These facts,coupled with the complicated nature of gas prices,have serious implications for numerical valuation and control.There are three common numerical techniques that could be used when determining the value of a real-option to store or withdraw gas on a given day:•Monte-Carlo simulation,•binomial/trinomial trees,•numerical partial differential equation(PDE)techniques.Monte-Carlo simulation is the mostflexible approach because it can handle a wide range of underlying uncertainties.However,it is not ideal for han-dling problems for which an optimal exercise strategy needs to be determined exactly,and in particular when that strategy may be non-trivial.Although im-perfect,because of the inaccuracies,this approach is very popular because it is computationally tractable and accuracy can be improved by allowing more sim-ulations.Price spikes should be an integral part of any gas market model and Monte-Carlo simulations are the most robust means of replicating such price behavior.Closed form solutions cannot in general cater for such spikes because the techniques are based on calculus and require continuous and differentiable functions representing price movements.For these reasons we investigate the use of Monte-Carlo simulation and present a model for optimizing the injec-tion/withdrawal schedule for storage users.2.1The input parametersLet us begin by defining the seven relevant variables and parameters.Figure1 presents a schematic diagram that illustrates how these variables affect the gas store.Let•P=the price per unit of natural gas.•I=the amount of working gas inventory.•c=the control variable representing the amount of gas currently being released(c>0)or injected(c<0).•I max=the maximum storage capacity of the facility.•c max(I)=the maximum deliverability rate(as a function of inventory levels).•c min(I)=the maximum injection rate(c min(I)<0)as a function of inventory levels.Figure1:The parameters for measuring the performance of a gas storage facility.•a(I,c)=the amount of gas lost given c units are being released/injected.The objective is to maximize the expected overall cashflow.The cashflow at any timeτin the future is(c−a(I,c))P,i.e.the amount of gas bought or sold times the price P.This cashflow in the future is worth e−ρτ(c−a(I,c))P now,whereρis the current interest rate. The sum of all cashflows ismax c(P,I,t)ETe−ρτ(c−a(I,c))Pδτ,(1)subject to c min(I)≤c≤c max(I).2.2Equations for I and PWe can easily deduce that the change in I depends on c and a(I,c):dI=−(c+a(I,c))dt.The decrease in inventory is just the sum of gas being extracted,c,and lost through pumping/leakage,a(I,c).Natural gas prices can exhibit extreme price fluctuations unlike those of virtually all other commodities,partially due to imperfections in the storage market.Prices may jump orders of magnitude in a short period of time and then return to normal levels just as quickly.Figure2 illustrates some of the extreme price movements that are not unusual either in American or European gas markets.A normal level varies depending upon the time of year.No generally agreed upon stochastic model exists for natural gas prices(some non-price-spike models can be found in[8]).Hull also gives anFigure 2:Natural gas prices.overview of stochastic processes for natural gas [6].A general valuation and control algorithm must be flexible enough to deal with a wide range of potential spot price models while remaining computationally tractable.We use P to denote the gas prices and changes in the price,dP are as follows:dP =µ(P,t )dt +σ(P,t )dX 1+N k =1γk (P,t,J k )dq k ,(2)where,•µ,σand the γk ’s (all N of them)can be any arbitrary functions of price and/or time.•dX 1is the standard Brownian motion increment.•The J k ’s are drawn from some other arbitrary distributions Q k (J ).•dq k ’s are Poisson processes with the properties:dq k = 0with probability 1− k (P,t )dt 1with probability k (P,t )dt ,In the above mean-reverting model we let the mean,µ1(P,t )dt =α(A +βA ∗Cos (2π(t 365−t A 365))+βSA ×cos (4π(t −t SA 365))−S t ),(3)so that we model gas prices that incorporate annual and semi-annual peaks.Via calibration of historical natural gas prices in the UK over eight years1,we found that A=29.2269,βA=9.8169,t A=−28.4464,βS A=−4.2561,t SA=47.0376. We found thatσ≈0.4.We defined upward jumps as>0.40and downward jumps as<0.20and these are modelled separately to diffusion.The relative frequencies of jumps up and down for the months January to December are as follows:•{4,2,2,2,1,1,0,3,3,6,3,7},•{6,5,2,2,0,0,3,3,2,4,0,8}.The mean jump sizes are0.725966106and-0.400104082and the standard de-viation for these jumps are0.297140631and0.098653958,respectively.The Poisson processes simulate price spikes,or sudden large jumps,that often occur in gas prices because of interruptions in supply or sudden peaks in demand.Multiple(N)Poisson processes can simulate different types of ran-dom events that may cause such jumps,itary conflict,extreme weather conditions,supply failures etc.With probability k(P,t)dt,dq k=1,and P increases by an amountγk(P,t,J k),where J k is drawn from some distribu-tion Q k(J).The addition of more Poisson processes has the benefit of not substantially increasing the computational complexity.There are a large number of parameters required as input into the model.A liquid secondary derivatives market is very useful in parameter estimation and spot model validation.Additional random factors may be added,that include stochastic volatility,stochastic mean reversion,path dependent models (price dynamics can depend on the current total amount in storage)Given representations for the dynamic processes,governing inventory,I,and price, P,we can set out to derive equations for the corresponding optimal strategy c(P,I,t)and the corresponding optimal value V(P,I,t).This can be achieved using different choices of stochastic models for gas prices.3Injection/Withdrawal Scheduling Problem Given a single Monte-Carlo price simulation,we inspect the generated prices and optimize the injection/withdrawal sequences retrospectively.We have an-ticipated gas prices for a given number of days in the future up until the end date of the storage contract.So,for example,at the beginning of a one-year contract there would be365simulated prices for the forthcoming year.2There remains the problem of deciding the optimal injection/withdrawal schedule for this simulated price movement.The trader has a choice of three actions for each day of the year,inject,withdraw or do nothing.In the northern hemisphere the critical decision times during the year occur in September-November and January-March.At other times of the year the price is usually so low or so high that injection and withdrawal decisions can be made easily.1(Prices were taken from31/01/98to31/01/06.2Storage contracts are usually of one year duration and begin on the1st of April.Energy traders in gas supply companies make decisions on a daily basis. They must also bear in mind the duration of their storage contract in this analysis.Storage operators adopt a“use it or lose it”policy with regard to gas that remains in storage after the expiry of the contract.It is therefore ideal to deplete the store at the end of a contract.The following integer linear program formulation represents the profit maximisation problem:maxNi=1p i(dW i W i−dI i I i),(4)where p i is the price on day i,W i is the maximum withdrawal amount on day i,I i is the maximium injection amount on day i,dW i is the decision variable on whether to inject or not,dI i is the decision variable on whether to withdraw on day i or not.Injection and withdrawal are mutually exclusive decisions,therefore,dI k+dW k≤1,∀k=1...N.(5) Also,there cannot be a negative amount of gas in storage on any given day in the future,j,so the following capacity constraints apply:INV+jk=1dI k I k−dW k W k≥0,∀j=1...N,(6)where INV is the amount stored in the facility at the start of the contract. In many cases this is0,because contracts usually involve a“use it or lose it”policy.Similarly,we cannot exceed our maximum capacity on any day,j:INV+jk=1dI k I k−dW k W k≤MAXCAP,∀j=1...N,(7)where MAXCAP is the maximum storage capacity as agreed in the contract. In this model there are2N variables and2N constraints,where N is the number of days remaining in the contract.The decisions on injection or withdrawal are made at the beginning of each day and cannot be reversed,thus imposing integrality constraints on the decision variables.This problem is N P-hard.Using the lp solve ILP solver[2],we attempted to solve instances of this problem.Unfortunately,some individual instances can take in the order of several minutes to solve optimally using branch and bound search3.Recall that we are adopting a Monte-Carlo simulation approach to determine the optimal strategy over a set of many possible instances.We repeat the price simulations many times so that we can gain confidence in our withdrawal or injection decisions.In terms of usability,the energy traders also require a response from the system within at mostfive minutes because decisions on withdrawal or injection are made early in the morning of each day and there is a tight deadline on decision times.3These experiments were conducted using a1.8GHz Intel Pentium III CPU processor.3.1Solution TechniqueTo aid computability,we relax the integrality constraints on the injection and withdrawal decision variables.In operational terms,this means that we ignore the fact that decisions can only be made at the start of the day.The linear relaxation assumes that a single withdrawal or injection decision can be made at any time during the day.This alteration allows us to solve the model in polynomial time and speeds up the computation by two orders of magnitude. Wefind that,on average,less than2%of the decision variable solution values are fractional.This provides strong evidence that our approximate solution technique does not seriously affect our results.The main reason for the low number of fractional values is that our price simulations do not provide intra-day price movements and only generate a single opening price for each day in the future.This level of granularity is deemed sufficient by energy traders.The linear relaxation of each optimization problem,involving a single price simulation for the remainder of the contract,is solved optimally in turn.The results of dW i and dI i are then averaged in order to determine what the best decision for day i is likely to be.Given that gas suppliers can decide daily on their injection policy,only the decision for today is required to know what needs to be done for that day.Nevertheless,energy traders can see the probability of certain strategies being optimal for future days given the status quo.This helps with budgeting and planning for the gas supplier.We conducted experiments to determine the scalability of this approach.In a worst case situation,where N=365,over300simulations can be solved in just over4minutes.Energy traders informed us that this does not pose a problem.In practice,the software tool is most useful in autumn and spring.This is because storage contracts begin in April and,therefore,the decision to inject dominates in thefirst3-4 months of the contract.It is principally used when there are less than220 days remaining in the contract,in which case the problems can be solved much faster.In experiments we found that the total runtime,t,is proportional to the square of the the number of days remaining in the contract,t∝N2.3.2Software ApplicationIt is important that the complexities of mathematical model and Monte Carlo simulation technique are abstracted away from the end-user.We designed a user interface that permits the entry of all necessary variables and illustrates the output in a graphical manner that is easy to understand and requires little or no training for the energy traders.Figure3presents a snapshot of the gas storage optimisation application[4]. The user can choose to set the following parameters on the Settings Menu:•Expected DIAF(Daily Injection AdjustmentFactors):The expected DIAFs for dates in the future are issued by the storage operator can be viewed/updated by selecting any day onFigure3:User Interface.the calendar4.These values indicate how rapidly one may inject or withdraw gas from the facility and are a function of the pressure within the underground cavern.They are thus a function of the behaviour of all other gas companies with storage contracts.Thesefigures depend upon the pressure within the storage facility.Figure4shows the interface for updating DIAFs.•Contract Details:The size of the storage facility can be updated here.The contract start and end-dates can also be modified.•Simulation Preferences:This window displays the form of the stochas-tic process used for simulations.The number of desired simulations can also be updated here.More simulations imply greater accuracy in the predictions.Once all the parameters in the dropdown menus are chosen,the current price of gas and the inventory in storage can be selected on the main screen. The“Launch Simulation”button can then be clicked to simulate the price pro-cesses.The status bar,at the bottom of the page,initially indicates that theses simulated prices are being generated.Then,the optimisation software deter-mines optimal solutions for each simulation.This is computationally intensive 4These values can be set to zero during downtime or a force majeure that precludes injection/withdrawal until this time.Figure4:DIAF updates.and the application absorbs most of the CPU’s processing capabilities during this procedure.The runtime grows as a square of of the remaining days to the contract end-date.Obviously,it grows linearly in the number of simulations requested.The graph entitled“Injection/Withdrawal”plan is updated in real-time as more simulated problems are solved.This dynamic behaviour allows energy traders to visually assess whether the number of simulations provided results in a stable expected schedule.If,nearing the end of the run,the graph is still changing significantly between simulations,this means that more simula-tions are ually after approximately150simulations the expected schedule and profitability graphs begins to settle and smoothen.The graph itself can be interpreted as follows.The abscissa indicates the dates,from today on the far left to the end date of the contract on the far right.The ordinate data represents the probability that a certain policy will be the optimal decision given possible future events.Red indicates injection, blue is withdrawal and yellow means do nothing.In Figure3we see that100 simulations were performed on the6th/May2006.The schedule for the coming days indicates yellow with100%probability.This was because the facility was closed for several weeks for repairs.The DIAFs were set to zero to indicate this event.Upon re-opening,the likely optimal strategy is to inject with82% probability.Injection is likely to continue until early October.The graph entitled“Max-Profit Probability Distribution”indicates the prob-ability distribution of profits that could be made from the store and its inven-tory,given perfect foresight about future prices.This,therefore,reflects a distribution over the upper bounds on valuations for the storage contract and the gas in storage.The series of graphs entitled“Price Distribution(Day X)”demonstrate the anticipated price movements for gas given the current price and date.The probability distribution over future days can be viewed by selecting the“Next Day”button.Gas suppliers can decide daily on their injection policy,so onlydI1and dW1(see Section2)are required to inform the user of the optimal expected policy for that day.This information is given in the results box with“Today’s Recommended Action”.The confidence rating indicates the probability that this decision is optimal over the given number of simulations.3.3Results and Feedback from Energy TradersOur results and subsequent discussions with energy traders were extremely positive.They found the interface easy to use and the outputs can be clearly interpreted.But most importantly,the software application is performing ex-tremely favourably when compared to human decision making.It is used on a daily basis be energy traders as a decision support system.The software was crucial in pointing out some anomalies in human behaviour.Traders were not compensating sufficiently for the adjustments in the DIAF,the amount of gas that can be injected or withdrawn on a daily basis.Instead,the focus remained toofirmly on the the gas price.The optimisation model showed that it is better to withdraw earlier in the season whilst other competitors are still injecting to avail of the higher pressure.It also highlights the game-theoretic aspects of the storage market and how the sub-optimal behaviour of competitors in the market can be explotied.We also discovered that the do-nothing policy is overlooked too often by traders and should be adopted more frequently.One possible explanation is that traders who choose to not to inject or withdraw on a given day may be sub-ject to criticism from those who perceive the the facility as being under-utilised, when in fact either injecting or withdrawing can harm expected profitability. This tool helps to illustrate how in certain circumstances,a policy of inaction is best.For example,consider a scenario when prices are rising,it is near the end of the contract and there is little gas in storage.It may be best to wait and withdraw tomorrow when prices will be higher and you can maintain higher deliverability also.Figure5demonstrates the aggregate runtimes for300simulated scheduling problems using the linear relaxation technique.It is approximately quadratic in the number of days remaining in the contract.Energy traders indicated that this is perfectly acceptable for their needs.Another worthwhile result of this endeavour was the increased interest in the potential potential applications of Artificial Intelligence within the natural gas sector.Bord G´a is is now the official sponsor a taught Masters programme in Intelligent Systems and students will conduct research projects in conjunction with this company in future years.4Possible ExtensionsSome of the following additional features could also enhance the system so that accuracy and performace may be improved:1.Incorporate forward/future prices to determine expected volatility,1 1010010000 50 100 150 200 250300 350 400T i m e (s e c s )Days remaining in the storage contract Figure 5:Scalability of LP approach.2.Incorporate risk aversion into the optimisation model,3.Incorporate interest rates to model discounting of future income and ex-penditure.4.LP model can be improved through removal of implicit constraints.Thereis no need for maximum capacity constraints at the beginning of thestorage contract.5.Determination of optimal control policy given multiple storage facilities.We are also examining another related problem that presents computational problems for the operator of the storage facility.Recently,there have been op-erational difficulties with storage facility that caused a prolonged downtime [3].This was caused be a fire and has increased awareness of safety issues.Main-tenance and the scheduling of downtime is gaining greater priority.We plan to use a constraint programming model that incorporates global constraints that enforce a minimum number of do-nothing events whose scheduling on consec-utive days facilitates cost-minimising maintenance.Another interesting line of research may involve the game theoretic study of equilibrium behaviour in this market.Given that the DIAFs are determined by the pressure within the storage facility,competing gas utilities with storage contracts directly affect the rate at which others can inject or withdraw gas.5ConclusionStochastic optimisation of gas storage facilities enables gas suppliers to schedule injection and withdrawal over the duration of a storage contract in a manner that maximises expected profitability.We presented a mean-reverting price model that incorporates diffusion and jump components.We then presented an ILP formulation of the injection/withdrawal scheduling problem.We found that it was necessary to adopt the linear relaxation of this model so that we cansolve hundreds of simulated price movements over the remainder of the storage contract in a timely manner.We showed that in practice fractional solutions have a very small impact on solution accuracy.This solution has been deployed very successfully and is used regularly by energy traders.This project has been so well received that the company are now in the process of introducing various AI techniques to assist in other areas of the business.References[1]Hyungsok Ahn,Albina Danilova,and Glen Swindle.Storing arb.Wilmott,1,2002.[2]Michael Berkelaar,Kjell Eikland,and Peter Notebaert.lp solve version5.0.10.0./group/lp_solve/.[3]Centrica.Force majeure update12th may2006.http://www.centrica-/Storage/MediaPress/Incident20060216q.html,May2006.[4]Alan Holland.Stochastic optimization for a gas storage facility.Demon-stration Session,Principles and Practices of Constraint Programming(CP-2006),September2006.[5]Alan Holland.Injection/withdrawal scheduling for natural gas storagefacilities.In Proceedings of the ACM Symposium on Applied Computing (ACM-EC2007),2007.[6]John C.Hull.Options,Futures and Other Derivatives.Prentice-Hall,2003.[7]Mike Ludkovski.Optimal switching with application to energy tolling agree-ments.PhD thesis,Princeton University,2005.[8]Dragana Pilipovi´c.Energy Risk:Valuing and Managing Energy Deriva-tives.McGraw-Hill,1998.[9]Ken Silverstein.More storage may be key to managing natural gas prices.PowerMarketers Industry Publications,October2004.[10]Matt Thompson,Matt Davison,and Henning Rasmussen.Natural gasstorage valuation and optimization:A real options application.preprint.[11]Paul Wilmott.Paul Wilmott introduces Quantitative Finance.Wiley,2001.。
基于遗传算法的滚动轴承的多目标优化设计翻译.
Multi-objective design optimisation of rolling bearings using genetic algorithms 基于遗传算法的滚动轴承的多目标优化设计Shantanu Guptaa Rajiv Tiwari b, and Shivashankar B. Nair a,aDepartment of Computer Science and Engineering计算机科学与工程系Indian Institute of Technology Guwahati, 印度理工学院GuwahatiGuwahati 781039, Assam, India 印度阿萨姆邦,781039,GuwahatibDepartment of Mechanical Engineering 机械工程系Indian Institute of Technology Guwahati, 印度理工学院GuwahatiGuwahati 781039, Assam, India 印度阿萨姆邦,781039,GuwahatiReceived 8 March 2006; revised 6 September 2006; accepted 2 October 2006. Available online 28 December 2006.Abstract 摘要The design of rolling bearings has to satisfy various constraints, e.g. the geometrical,kinematics and the strength, while delivering excellent performance, long life and high reliability.This invokes the need of an optimal design methodology to achieve these objectives collectively, i.e. the multi-objective optimisation.In this paper, three primary objectives fora rolling bearing, namely, the dynamic capacity (C d), the static capacity (C s) and the elastohydrodynamic minimum film thickness (H min) have been optimized separately, pair-wise and simultaneously using an advanced multi-objective optimisation algorithm: NSGA II(non-dominated sorting based genetic algorithm). These multiple objectives are performance measures of a rolling bearing, compete among themselves giving us a trade-off region where theydesign become “simultaneously optimal”, i.e. Pareto optimal. A sensitivity analysis of various parameters has been performed, to see changes in bearing performance parameters, and results show that, except the inner groove curvature radius, no other design parameters have adverse affect on performance parameters.滚动轴承的设计,要满足各种制约因素,如几何、运动学和强度,同时还要提供优异的性能、长寿命和高可靠性。
第十二届全国周培源大学生力学竞赛“理论设计与操作”团体赛“自停滚轮”项目设计方案
第42卷第1期力学与实践2020年2月§全国周培源大学生§力学竞赛 \3C5C«C53GaC«C3aC53G3第十二届全国周培源大学生力学竞赛“理论设计与 操作”团体赛“自停滚轮”项目设计方案宋向荣U李想杜立成赵哲(江苏科技大学船舶与海洋工程学院,江苏镇江212003)摘要本文介绍了江苏科技大学代表队在第十二届全国周培源大学生力学竞赛“理论设计与操作”团体赛“自停滚轮”项目中的设计方案和制作过程,给关心和热爱该比赛的力学爱好者提供参考。
关键词力学竞赛,自停滚轮,设计方案中图分类号:031,0341 文献标识码:A doi:10.6052/1000-0879-19-352D ESIG N SCH EME OF SE L F-STO PPING C Y LIN D ER IN TH E“T H E O R ETIC A L D ESIG N AND O PE R A T IO N” C O M PE T IT IO N OF TH E 12TH NATIONAL ZHOU PE IY U A N C O M PE T IT IO N ON M EC H A N IC S FO RCOLLEG E STU D EN TS OF C H IN ASONG Xiangrong1^LI Xiang DU Licheng ZHAO Zhe(School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology,Zhenjiang 212003, Jiangsu, China)A bstract This paper presents the design scheme and the operational process of the competition team from Jiangsu University of Science and Technology in the“Theoretical Design and Operation”competition of the 12th Zhou Peiyuan Mechanics Competition for College Students of China.K e y words mechanics competition,self-stopping cylinder,design scheme第十二届全国周培源大学生力学竞赛“理论设 计与操作”团体赛于2019年8月6—9日在清华大学 举行w。
玉山县玉山一中城北分校规划设计
环境与建筑科学科技创新导报 Science and Technology Innovation Herald91①作者简介:房晶辉(1980—),男,本科,高级工程师,研究方向为建筑设计。
DOI:10.16660/ki.1674-098X.2010-5640-1835玉山县玉山一中城北分校规划设计①房晶辉 阎明 董雅楠(天津大学建筑设计规划研究总院有限公司 天津 300073)摘 要:“一座书香溢谷的现代书院”这种规划理念,将玉山的传统文化、玉山一中老校区的符号特征融入新校区的规划之中,带来了新校区的两大主要特点:自由的院落布局,形成富有特色的双园林核心空间;升华的情景再现,将新老校区的文化基因有效传承。
通过努力把校区建成经得起历史检验的精品工程。
关键词:校园规划 特色 文化 设计中图分类号:TU985文献标识码:A 文章编号:1674-098X(2021)01(b)-0091-03Planning and Design of North Branch of Yushan No.1 MiddleSchool in Yushan CountyFANG Jinghui YAN Ming DONG Yanan(Architectural Design and Planning Research Institute Co., Ltd., Tianjin University, Tianjin, 300073 China)Abstract: The planning concept of "a modern academy of scholars overf lowing the valley", integrates the traditional culture of Yushan and the symbolic features of the old campus of Yushan No. 1 Middle School into the planning of the new campus, it brings two main features of the new campus: Free courtyard layout, form the core space of double gardens with rich characteristics; the sublimation of the scene reappearance, the cultural gene of the new and old campus will be effectively inherited. So that the campus can be built into a high-quality project that can stand the test of history.Key Words: Campus planning; Characteristics; Culture; Design本项目规划设计指导思想是:以科学发展观为统领,坚持以人为本,可持续发展;坚持“智能、节能、环保”的设计理念;坚持“生态型、园林式”的设计要求,融校园文化、建筑文化、景观文化于一体,注重生态节能与人文的和谐统一,贯彻环境育人的宗旨。
传统园林中适应碳中和的景观设计手法分析——以拙政园为例
Construction & Decoration建筑与装饰2023年9月下 7传统园林中适应碳中和的景观设计手法分析——以拙政园为例卢麒远河北工业大学建筑与艺术设计学院 天津 300131摘 要 传统园林在中国园林发展历史上占据着重要的地位。
在“双碳”目标下,分析传统园林生态思想和设计手法,对现代园林景观设计具有重要的指导和借鉴作用。
本文以苏州拙政园为例,从适应气候的建筑设计、建筑与植物、生态构筑物、水文连通性、生态材料和植物材料选择等6个方面进行分析,了解其蕴含的生态思想,提出对“双碳”目标下现代园林景观设计的启示,为园林绿化增强对碳中和的贡献提供参考。
关键词 碳中和;传统园林;景观设计;拙政园Analysis of Landscape Design Techniques Adapting to Carbon Neutrality in Traditional Gardens —— Example of Humble Administrator’s GardenLu Qi-yuanSchool of Architecture and Art Design, Hebei University of Technology, Tianjin 300131, ChinaAbstract Traditional gardens occupy an important position in the history of Chinese garden development. Under the goal of “dual carbon”, the analysis of traditional garden ecological thinking and design techniques has an important guiding and reference role for modern garden landscape design. Taking the Humble Administrator’s Garden in Suzhou as an example, this paper analyzes six aspects: climate-adapted architectural design, buildings and plants, ecological structures, hydrological connectivity, ecological materials and plant materials selection, understands the ecological ideas in them, and puts forward enlightenment for modern garden landscape design under the “dual carbon” goal, so as to provide reference for the contribution of landscaping enhancement to carbon neutrality.Key words carbon neutrality; traditional garden; landscape design; Humble Administrator’s Garden引言2020年9月,中国明确提出了“碳达峰、碳中和”的“双碳”目标倡导,并提出力争2030年前CO 2排放达到高峰,努力力争2060年实现碳中和。
Optimization and Control in Engineering
Optimization and Control in Engineering As an engineer, the concept of optimization and control plays a crucial rolein my work. It involves finding the best solution to a problem while considering various constraints and objectives. This process requires a deep understanding of the system being analyzed, as well as the ability to use mathematical models and algorithms to find the optimal solution. Optimization and control are essential in a wide range of engineering disciplines, including mechanical, electrical, andcivil engineering. One of the main challenges in optimization and control is balancing conflicting objectives. For example, in designing a mechanical system, engineers may need to optimize for both performance and cost. Finding the right balance between these objectives can be difficult, as improving one aspect of the system may come at the expense of another. This is where optimization techniques such as multi-objective optimization come into play, allowing engineers to find solutions that trade off between different objectives. Another important aspectof optimization and control is dealing with uncertainty. In real-world engineering systems, there are often uncertainties in the parameters or variables that can affect the system's performance. Engineers need to account for these uncertainties when designing and optimizing systems to ensure that they are robust and reliable. Techniques such as robust optimization and stochastic control are used to address these uncertainties and ensure that the system performs well under varying conditions. In addition to optimizing the performance of a system, control isalso essential for ensuring that the system operates as intended. Control systems use feedback mechanisms to regulate the system's behavior and maintain desired performance levels. These systems can be simple, such as a thermostat controlling the temperature of a room, or complex, such as the autopilot system of an aircraft. Control theory provides engineers with the tools and techniques to design and implement these systems effectively. Optimization and control also play a crucial role in the field of process engineering. In industries such as chemical and manufacturing, engineers need to optimize processes to improve efficiency, reduce costs, and ensure product quality. Control systems are used to regulate these processes and maintain optimal operating conditions. By applying optimization and control techniques, engineers can maximize the productivity and profitability ofthese processes while minimizing waste and environmental impact. Overall, optimization and control are essential tools for engineers in designing, analyzing, and improving systems and processes. By using mathematical models, algorithms, and feedback mechanisms, engineers can find the best solutions to complex problems and ensure that systems operate efficiently and reliably. The challenges of balancing conflicting objectives, dealing with uncertainties, and designing effectivecontrol systems require a deep understanding of engineering principles and a creative approach to problem-solving. As an engineer, I find the field of optimization and control both challenging and rewarding, as it allows me to apply my skills and knowledge to make a real impact in the world.。
1. Objective
Project Description:Optimal design and operation of natural gas processing plants 1. ObjectiveThe objective of this project is to increase the value of natural gas produced offshore by more efficient operations and improved design of processing plants for natural gas. This will be achieved by:-Improving production planning by advanced optimization and scheduling.-Incorporate market information into production planning and thereby achieving market focus on process control-Provide decision support tools to link process control and business operation for use in the new emerging control centers-Provide a framework within the Gas Technology Centre at NTNU/SINTEF for exchange and reuse of Process System Technology2. Measurable Goals1Develop an application that demonstrates the integration of optimization algorithms, process models and process control for a selected main case 2Supervise 1-2 PhDs3Publish a tutorial paper that summarize the main contribution of new methodology4Develop a core software library for a generic model / optimizer interface 5Acquire an industrial project based on this technology3. BackgroundOptimal operation of processing plants is a challenging area where new dynamic markets for gas components lead to a need for stronger analytical capabilities of decision support tools. Also the supply situation becomes more variable, as the gas companies responds to market opportunities.The result is a more rapidly changing environment and as a consequense processing plants needs to be reconfigured more often.. There is a need for better understanding on how to make plantwide production plans and implement these through process management.Decision support tools have to combine both optimization and simulation capabilities in order to analyse the consequence of different scenarios. This is an area where little research has been done, and we wish to focus on developing new methodology for combining simulation and optimization.Advanced modeling and optimization is needed in order to address the new challenges in plant control and optimization systems:-Many of the processing plants are short of capacity. It is more important than before to maximize flow through the plants or the profit from final products.-New opportunities exist because of recent advances in modern control technology. E.g. Model Predictive control lifts the level of automation andgives better opportunities for process optimization systems at higher levels.-Integration of tasks and systems in operation support centers. E-fields give a new perspective on operation of the oil and gas production, and this mode ofoperation must be prepared for in the gas processing plants. (1) -The MTO-perspective. (Man-Technology-Organisation). Provide relevant information for the personnel involved. (10)-The use of models for planning and process control plays a central role.-Modeling the dynamics of the markets and incorporate it in contingent plans.The plans should reflect capacities and production possibilities of the plant.This again depends on the design and operation of the plant control system -Structuring of the information flow between layers and systems in the process in the process control hierarchy, the real time optimization level and theplanning and scheduling levels.-Self regulating and robust systems. Optimizing the other parameters-Limitations imposed by process design. How do the above issues have impact on the optimal design of gas processing plants.3.1 Industrial relevanceIn the upcoming decade there will be large investments in gas production, and in facilities for transportation and processing. Optimal utilization of all these facilities is vital in order to maximise the value from produced natural gas.Advanced process control and operation has raised the level of automation in the process industry significantly in the last decades. For example in the refinery industry methods like model based predictive control (MPC) and real time optimisation (RTO) have become widely used. (2) The focus on these technologies has given large benefits to the industry in form of increased throughput and more robust operation. The improvements obtained by the use of better control and decision support tools ends up directly on the bottom line for the operating companies. The substance of these tools is in fact software realization of process knowledge, control and optimization methods. MPC and RTO are now more or less off the shelf products, although for complex processes the adoption of this technology requires specialist competence.However, there are significant potentials for further improvements in this area. One challenge for the gas processing plants is being able to quickly adapt the plant operation to dynamic changes in the markets, thus the plant flexibility and ability to perform rapid production changes becomes more important. It is also required toknow accurately the plant production capability, both on very short term (today-tomorrow) and on weekly, monthly and even longer horizons. And this requires use of advanced optimisation tools and efficient process calculations. It is important to consider these issues related to dynamic operation also in the design of new processing plants, and for modification projects. Investments in this type of project have typically short payback time . (3)Combination of individual units into an integrated plant gives a large scale control problem that is more than just the sum of the units. Cross-connections, bypasses and recycling of streams give more flexibility, but at the same time, the operation becomes significantly more complicated, and it is almost impossible to utilize the full potential of a complex plant without computer based decision support tools.Thus there is a need for development of new decision support tools that combines optimization technology, realize process calculation models at a suitable level of speed and accuracy, and structure the information flow, both from the process measurements and deduced variables, and from the support tool down to the manipulated variables in the control system. It is also needed to develop further the methodology related to Plantwide control (4) in this context.3.2 The Technology Integration ChallengeThis requires contribution and integration of several technical disciplines: •Control engineering – process control, control structuring, model interfacing •Industrial Economy - Description of economic mechanisms and environment, planning and scheduling•Process engineering – Process knowledge, models, process design•Applied mathematics –Optimization methods, efficient numerical methods, basic supplier to the above disciplines•System integration – Framework/interface standards for system componentsThere is an obvious potential in combining the multidisciplinary knowledge into a working solution in form of a decision support tool. However, different disciplines have normally different focus on their research, and develop their own terminology and tools, so bridging the gap and to join forces towards a common goal is a challenge that must not be underestimated.Thus for every research group there must be an incentive to adapt their approaches to fit into a wider context. For example, the compressor experts normally have their focus on making better compressors, but in a plant operation context the focus is on providing models of compressor behavior in a format and at a complexity level that are suited for the overall system. Nobody else than the compressor experts are better qualified to do this right but it requires a system focus in stead of a unit focus. If the compressor expert does not contribute, who can, and how do we integrate the required technology then? Thus, the vital question for an expert within every discipline is: How can I best provide my knowledge and tools into a common system for optimization of natural gas processing plants.It is not an issue whether the Gas technology Centre at NTNU/SINTEF has resources within each of the required disciplines, but how the knowledge and available methods can be integrated into a working system. This requires development of new methodology.4. Scientific ApproachOptimal operation of gas processing plants is a challenging multidisciplinary task. Large-scale process optimization is challenging in itself. Thus, when we also will consider dynamic conditions in the market and on the supply side the operation will most certainly run into problems that must be solved. Some will arise from the size of the problem, some from complex process behavior and from requirements to solution of complex optimization problems.The starting point in this project is the need for decision support as seen from the personnel in a plant operating company. This defines a set of tasks that requires optimization calculations, process calculations and measurement data handling.The personnel in question can be plant operators, production planners, sales personnel, maintenance planners, process engineers, managements, etc. Experience from other applications like gas transportation will be utilized (5).The inclusion of market factors, capacity planning and scheduling shall be focused, as this sets new requirements to vertical integration of process control and the optimization layers. We may in fact have several such layers, where for example the classical real time optimization is just one element. In planning and capacity assessment, the RTO-layer may be accessed by superior layers in order to compute the optimal process targets over a certain horizon.The requirements to process calculations for each type of task shall be classified. This may result in a set of optimization problems with different properties and requirements to solution approach and to the underlying process calculations and data handling. E.g. one approach from the planning side is to start with empty or extremely simple process models, and to refine the models based on the requirements to the planning.Segmentation into suitable process sections and control hierarchies are central issues. Here we can apply methods from Plantwide control (4) in order to structure the control of the plant units in a way so that the influence from unknown disturbances and model uncertainties are minimized. A very important output in the first phase is to define high level targets for the process control. The next important issue is to develop methods to select the variables that should be exchanged between the optimization and the process control layers. This is a control structure design task where the focus is on selecting the variables that are best suited for setpoint control in order to fulfill the process optimization targets in presence of unknown disturbances and model parameters and measurement errors. Segmentation of the control into suitable sections and layers is also a part of this task.Recent advances in process control technology also give a new perspective. For example, with an active MPC controller, information about active and inactive process constraints is high level information that can be exchanged with the optimization layer, in stead of representing the constraint equations at the optimization layer.Efficient use of models is a so wide area that this issue can be subject to extended research in separate programs. For example, in process design it is industrial practice to use quite detailed process models, including rigorous thermodynamics and representation of detailed phenomena within each process unit. In operator training simulators, detailed dynamic models are used, but these are rarely the same models as used in design, and the built in process knowledge in form of model configurations and parameters is usually not interchangeable because of different modeling approaches and different model data representation. The models used in MPCs are normally captured from experiments on the process itself, and are not connected to the other two types of models. For Real time optimization (RTO), steady state models are normally used, and in some cases model tools with rigorous models are used there too. For capacity assessment, correct representation of potential bottlenecks is important. In this project the focus shall be on model representation at an accuracy level and format that is required from the optimization level for the different optimization tasks. In a system realization, the system architecture and formats for interchange of data between must be unified. As far as possible, available standards should be applied. This is a central issue within the Gas Technology centre in order to be able to combine newly developed models and optimization procedures for analysis and performance evaluation.5. Activities5.1 Project ManagementCoordination activities by the project manager, and disipline leaders.Refer to project organization.5.2 Developing new methodology5.2.1 Modelling for optimization purposesThe focus in this activity is to address any special requirements for process calculations that arise from a set of relevant optimization problems. The requirements may be different dependant on the type of the requested decision support information that is needed. E.g. the assessment of spare capacity over a planning horizon may require more rapid, but less accurate process calculations than solution of the current real time optimum.-Model requirements for various optimization problems-Representation and extraction of process knowledge-Use of commercial process simulation tools-Standards for interchange of model data-Model development methods-Handling of uncertainties: model, measurements, prices, etc.-Use of steady-state and dynamic models-Fitting models to measurement data-Representation of constraints-Representation of the control system-Relation to Control Structure design-Analysis of the process system regularity5.2.2 Applied optimization technologiesThe resulting optimization problems are highly complex, and no general effective global methods exist for such problems. There are a large number of problem areas where different solution approaches are required. A set of problem areas shall be selected from the relevant cases. Issues to be addressed are:-Classify optimization problems-Non-convex problems-Large Scale problems- Search algorithms tailored to the properties of the underlying cases-Use of Real Time Optimization based on steady state models in a time-varying dynamic plant-Analysis of requirements to the underlying process computations-Updating process constraints and other parameters from measurement data5.2.3 Plantwide ControlExplore the issues related to control structure design and structuring of the process control hierarchy in the perspective of market focus on process operation.5.3 System integration of a demonstrator systemA versatile decision support tool will consist of a several complex functional modules. The integration of simulators, optimizing functions, real time data, planning input data, economic data etc. requires that system integration issues must not be underestimated. Thus, the selection of methods/standards for communication interface, computing platforms, system architecture and other system development issues are vital for realization of an integrated system. Interface to commercial packages bothfor optimization and simulation.Phases:1.Requirement specifications2.System functional analysis3.System architecture4.Interface specifications5.Production6.Demonstrator testing5.4 Adaptation and testing on relevant cases5.4.1 Case: Natural gas processing plant optimizationA typical area of use would be decision support tools for the plantwide optimization of a processing plant like the Kårstø gas processing facility:-Optimization of export gas specification: For example, to meet delivery contract requirements as well as possible, under the circumstances of limitedfeed-streams or technical problems within the plant-Capacity assessment: Optimization of available capacity for a given set of conditions. Examples of this function include:-Optimization based on constraints on gas specification, liquids production or feed-streams-Optimization based on constraints on individual components within the plant -Optimization based on shutdown of equipment items or trains-Maximization of liquids recovery: For example, to take advantage of a short-term increase in specific product prices.-Optimization of liquids specification: For example, during reduced effectiveness of a related piece of equipment-Minimization of energy use: For example, due to limited steam generation capacity-Minimization of emissions (or financial impact)-Minimization of operating cost.Gassco, who is the operator at Kårstø, is probably going to start a project in this area during 2004 and this project fits together. A supporting letter from Gassco is enclosed with this proposal.5.5 PhD subjectsSeveral issues are also suited for PhD studies and three particular areas are outlined below.There are needs for better understanding and new methodology in modeling for optimization purposes. The properties of the model will influence the nature of the optimization problem, and it is important to understand how model properties fit together with optimization solving techniques. The numerical properties and solution times are strongly affected and it is important to develop good methods that can make more efficient solutions. For example, the tradeoff between the level of accuracy of a model with respect to the accuracy of the final solution, representation of physical limitations in the model, or as constraint equations, etc.Plantwide Control with market focus on process control. Vertical integration between business systems, planning systems and process control systems sets new requirements to how the process control best should be configured.Operational analysis and study of the optimization problems that will appear in the context of market focus on process controlAllocation of PhD subjects shall be coordinated with the NTNU partners and the other ongoing activities within the Gas Technology Centre.The main priority is in this project the interaction between the economic- and technical perspectives on plant operation.5.6 Dissemination and exploitationThe results form the basic methodology studies through the Ph.D studies will be published at conferences and appropriate scientific journals.SINTEF is in an ideal position to distribute results to industry. Gassco, Statoil and Norsk Hydro are the main end users in Norway. SINTEF also carry out development projects for system industry. Relevant companies are Kongsberg Simrad, Cybernetica, Predictor, FMC.6. Project organization6.1 Organization of the projectThe project contractual partner is SINTEF ICT. The Project Manager is Senior Scientist Ivar J. Halvorsen Dr.ing.We will establish a steering group. This group will be appointed by the board of The Gas Technology centre and will consist of 1-2 representatives from each of NTNU and SINTEF and also from one of the industrial companies that supply case data (Gassco or Statoil).For each of the research activities we willestablish a work group and an activity leaderresponsible for the deliverables of the activity.The activity leaders are responsible for thedaily operation of the activities, the operationalmanagement of these and their budget. Workgroups will consist of NTNU professors,SINTEF researchers, PhD students andindustry people (when the industry finds itadequate). This kind of co-operation ischallenging and we will therefore establish amanagement group consisting of the projectmanager, the activity leaders and seniorpersons from SINTEF and NTNU,representing different disciplines. The main purpose of this group is to co-ordinate the activities among all the partners in the project and to make operational decisions for the project guided by the priorities of the steering group. The project manager is responsible for the overall project budget, the deliverables of the project and the co-ordination of the research activities. He reports to the steering group and leads the management group.6.2 Partners and rolesThe partners in this project already cooperate within the Gas Technology Centre at NTNU-SINTEF. (See web page at http://www.ntnu.no/gass/).SINTEF NTNU Industrial Management Industrial Economics and TechnologyManagementICT, Applied Cybernetics, AppliedEngineering CyberneticsMathematicsEnergy Research Chemical EngineeringSINTEFSINTEF Industrial management (IM): Has competence in optimization, mathematical modeling and energy markets. Previous experience within optimization models for the natural gas value chain includes:-VENOGA: NFR project finishing in 2004. Modeling the value chain for natural gas including production, transportation, processing and contractmanagement (in cooperation with Statoil and NTNU).-GassOptTKL: industrial project in co-operation with Statoil and Gassco modeling transport optimization using steady-state models (ongoing since1997).-Omega: EU-project finished in 2000. Supply chain optimization models for European energy companies (NTNU, Iberdrola and Enel main partners).-SARA: Optimization tool for portfolio investment analysis. Developed together with NTNU in the 80’s for Norwegian petroleum Directorate andseveral oil companies. Statoil is still using a modified version.Key personnel: Senior scientist dr. ing. Asgeir Tomasgard, Senior Scientist FrodeRømo, Dr. Matthias Nowak.SINTEF Energy research (SEfAS): Has competence in modeling energy processes and knowledge of new technology development. Related projects are: -NFR project finished in Dec. 2003. Ship based transport of CO2.-CCP: NFR project finished in Dec. 2003. CO2 conditioning and transportation (In cooperation with industrial partner)-NFR project started in 2003 (going for 4 years) Next generation LNG heat exchangers-Industry project finished in 2003, LNG technology status-ACMAR, NFR-project ended in 2003. Multiphase flow modeling-Coil, Modeling of spiral wound heat exchangers (in cooperation with industrial partner)-Mini-LNG, Development of small scale LNG production technology-General development of in-house tools for dynamic pipeline transport, including thermodynamic properties for CO2 and hydrocarbon mixtures,within various industrial projectsKey personnel: Dr.ing. Maria Barrio, Dr.ing Hanne KvamsdalSINTEF Information and communication technology (ICT):Applied Cybernetics (Former department Automatic Control). Process control and system integration.-Intelligent wells (SINTEF Strategic Institute Project) focused on some related issues concerning enhanced oil and production based on active use of modelsand control technology. (11)-CORD – Optimal operation of offshore plants.2002-4 (10). Joint project betweek SINTEF, MARINTEK and IFE.-REPP – “Reaktorteknologi i petrokjemi- og plastindustrien”. Development of nonlinear modle based control for polymer reactors.-Kybernetisk Lab- REPP-Kyb-lab is the base for the establishment of Cybernetica AS. (www.cybernetica.no)-HOPE – Heidrun Operational Experience. Development of a training simulator and procedure handling system for Heidrun (92-95) -MIP – Methodology program In the Process industry (88-92). Development of methodology within process control and instrumentation. Basis for Statoils inhouse MPC technology-CADAS – Computer Aided Design Analysis and Simulations – (88-92) Life Cycle process simulator development. The design is now adopted in ASSETT from Kongsberg Simrad.Key personnel: Senior scientist,Dr.ing. Ivar Halvorsen, Dr ing Ingrid Schølberg, M.Sc Mary Ann Lundteigen Applied Mathematics: Contribution in optimization methodologyKey personnel: Research Manager Trond KvamsdalThe Norwegian University of science and technology (NTNU )The main roles of the NTNU departments listed here will be in PhD student supervision and advising SINTEF.NTNU Department of Industrial economics and technology management (IE): Has competence in energy economics and optimization, and is co-operating with SINTEF and advise SINTEF in the same areas. PhD student in optimization.Key personnel: Associate professor Asgeir TomasgardNTNU Department of engineering cybernetics (EC): This department will advise SINTEF in the same areas. Will supervise PhD students in Modeling for optimisation and will advise SINTEF in the modeling.Key personnel: Professor Bjarne Foss, Professor Morten HovdNTNU Department of chemical engineering (EP): Will supervise PhD students in Plantwide control and will advise SINTEF in this field.Key personnel: Professor Sigurd Skogestad7. Progress plan – Milestones2005Kickoff JanuaryMethodologyDefinition document for Methodology studies June 2005Recruitment of PhD* students June 2005Methodology studies (PhD basic research, SINTEF application) June 2005-June 20086monthsarticles EachScientificDemonstrato rDemonstrator requirement document October 2005Demonstrator system analysis document April 2006Demonstrator interface specification June 2006Demonstrator productionand testing November 2006Updated interface standard/library November 2007CaseAugust20051CaseofSelectionDemonstrator on Case 1 April 2008DisseminationAdoption of new results from the methodologyWithin2008demonstrator Junethe2008JanuarypaperTutorial*PhD studies June 2005 – June 20088. Strategic relevance for NTNU-SINTEFJoining forces in gas technology research is one of the backgrounds for establishment of the Gas Technology Centre at NTNU/SINTEF. Thus, this project fits right into that strategy. Some of the issues are also generic and demanded for other types of process industry than natural gas processing.Note that this initiative shall not replace detailed research within each discipline. Such research is required for maintaining the deep knowledge within each discipline that is the base for contribution to a common system.This project shall enhance SINTEFs position as provider of technology both directly to the operators of the gas production, processing and transportation systems and to vendors and engineering companies that deliver products and services to the industry. Only in Norway, there are great challenges in optimal design and operation of gas processing plants e.g. at Kårstø, Kollsnes, Averøya and Melkeøya. Recent newspaper articles about some of these projects exceeding their budgets call for improvements in engineering and planning.SINTEF, being an independent research foundation can play an active role in reducing the uncertainties related to new technology in gas processing plants, and thereby improve both design and operations. Both vendors and operators can benefit from this, and just by the size of the processing plants, optimal operation and design is also of National importance.Thus, it is of strategic important that SINTEF develops and maintains knowledge, methodology and tools. Both within each discipline and in the wider process system technology context. The gas technology centre at NTNU/SINTEF is well suited to accumulate promising results from several basic programs and to make this technology available to the industry in form of system development projects, process studies and system evaluations etc.。
Advanced Structural Analysis and Design
Advanced Structural Analysis and Design Advanced Structural Analysis and Design is a field that encompasses the design and analysis of structures that are subjected to various types of loads, such as gravity, wind, earthquake, and temperature. It is a critical field that requires a deep understanding of the behavior of different materials, such as concrete, steel, and timber, under different loading conditions. In this essay, I will discuss the various challenges and requirements of advanced structural analysis and designfrom multiple perspectives. From the perspective of a structural engineer, advanced structural analysis and design require a deep understanding of the principles of mechanics, mathematics, and physics. Structural engineers need to be able to analyze the behavior of different materials and structures under various loading conditions and design structures that can withstand these loads. They need to be familiar with various design codes and standards, such as the American Concrete Institute (ACI) and the American Institute of Steel Construction (AISC), and ensure that their designs comply with these codes. From the perspective of a construction manager, advanced structural analysis and design require careful planning and coordination. Construction managers need to ensure that the design is constructible and that the construction process is efficient and safe. They needto work closely with the structural engineer to ensure that the design is feasible and that the construction process does not compromise the safety and integrity of the structure. From the perspective of a building owner, advanced structural analysis and design require a balance between cost, safety, and aesthetics. Building owners need to ensure that the structure is safe and meets all applicable codes and standards, but they also need to consider the cost of construction and the aesthetic value of the structure. They need to work closely with thestructural engineer and the architect to ensure that the design meets their needs and expectations. From the perspective of a material supplier, advancedstructural analysis and design require the production of high-quality materialsthat meet the specifications and requirements of the structural engineer. Material suppliers need to ensure that their products are reliable and consistent and that they can provide the necessary technical support to the structural engineer during the design and construction process. From the perspective of a governmentregulator, advanced structural analysis and design require the enforcement of codes and standards that ensure the safety and integrity of structures. Government regulators need to ensure that the design and construction of structures comply with applicable codes and standards and that the structures are safe for the public to use. They also need to ensure that the structural engineer and the construction team are qualified and licensed to perform their respective roles. In conclusion, advanced structural analysis and design is a critical field that requires a deep understanding of the behavior of different materials andstructures under various loading conditions. It requires careful planning and coordination between the structural engineer, the construction manager, the building owner, the material supplier, and the government regulator. It also requires a balance between cost, safety, and aesthetics. Advanced structural analysis and design plays a crucial role in ensuring the safety and integrity of structures and the well-being of the public.。
Engineering Design and Optimization
Engineering Design and OptimizationEngineering design and optimization is a critical process in the development of new products, systems, and processes. It involves the application of scientific and mathematical principles to create efficient and effective solutions to complex problems. The goal of engineering design and optimization is to improve the performance, reliability, and cost-effectiveness of products and systems, while also minimizing their environmental impact. This process requires a deep understanding of the underlying principles of physics, mathematics, and materials science, as well as the ability to think creatively and innovatively. One of the key challenges in engineering design and optimization is the need to balance competing objectives. For example, when designing a new product, engineers must consider factors such as performance, cost, reliability, and manufacturability. These objectives are often conflicting, and it can be difficult to find a solution that optimizes all of them simultaneously. This requires careful analysis and trade-off decisions, as well as the use of advanced optimization techniques tofind the best possible solution. Another challenge in engineering design and optimization is the need to account for uncertainty. In many engineering problems, there are uncertainties in factors such as material properties, operating conditions, and external loads. These uncertainties can have a significant impact on the performance and reliability of the final design. Engineers must therefore use probabilistic methods and robust design techniques to account for these uncertainties and ensure that the final design is resilient to variations in operating conditions. In recent years, there has been a growing emphasis on sustainability in engineering design and optimization. This includes the need to minimize the environmental impact of products and systems, as well as to optimize their energy efficiency and use of natural resources. This requires a holistic approach that considers the entire life cycle of a product, from raw material extraction to end-of-life disposal. Engineers must therefore consider factors such as material selection, energy efficiency, and recyclability when optimizing the design of new products and systems. Advances in computer-aided design (CAD) and simulation tools have revolutionized the field of engineering design and optimization. These tools allow engineers to create detailed virtual models ofproducts and systems, and to simulate their performance under various operating conditions. This enables engineers to explore a wide range of design options andto quickly evaluate their performance, cost, and reliability. As a result, the design process has become more iterative and collaborative, with engineers able to explore a larger design space and to quickly identify the best possible solutions. In conclusion, engineering design and optimization is a complex and challenging process that requires a deep understanding of scientific and mathematical principles, as well as the ability to balance competing objectives and account for uncertainty. It also requires a growing emphasis on sustainability and environmental impact, as well as the use of advanced CAD and simulation tools. By addressing these challenges, engineers can create innovative and effectivesolutions to the complex problems of the modern world.。
Engineering Design Optimization
Engineering Design Optimization Engineering design optimization is an important aspect of modern engineering, as it helps to ensure that products and systems are designed to meet the needs of users while minimizing costs and maximizing efficiency. In this essay, I will explore the various perspectives on engineering design optimization, including its benefits, challenges, and potential solutions. One of the main benefits of engineering design optimization is that it can help to reduce costs and improve efficiency. By using mathematical models and simulations, engineers can identify the most efficient design solutions for a given product or system, which can help to reduce material and labor costs, as well as improve overall performance. Thisis particularly important in industries such as aerospace and automotive, where even small improvements in efficiency can have a significant impact onprofitability. However, there are also several challenges associated with engineering design optimization. One of the main challenges is the complexity of the design process, which can involve multiple variables and constraints. This can make it difficult to identify the optimal design solution, particularly when there are conflicting requirements or trade-offs between different design objectives. Additionally, there may be uncertainties or inaccuracies in the data used to model the system, which can further complicate the optimization process. To address these challenges, engineers have developed a range of tools and techniques for engineering design optimization. These include mathematical programming, evolutionary algorithms, and machine learning, which can be used to identify the optimal design solution based on a set of predefined objectives and constraints. Additionally, engineers may use sensitivity analysis and uncertaintyquantification to evaluate the robustness of the design solution and identify potential sources of error or variability. Another important perspective on engineering design optimization is the role of human factors in the design process. While mathematical models and simulations can be useful for identifying optimal design solutions, it is important to consider the needs and preferences of usersin the design process. This may involve conducting user surveys or focus groups to gather feedback on different design options, or incorporating ergonomic considerations into the design process to ensure that the product or system iscomfortable and easy to use. Finally, it is important to consider the ethical implications of engineering design optimization. While the goal of optimization is to improve efficiency and reduce costs, it is important to ensure that these benefits are not achieved at the expense of safety, reliability, or environmental sustainability. For example, optimizing a manufacturing process to minimize costs may result in increased pollution or environmental damage, which could have negative impacts on local communities and ecosystems. As such, engineers must consider the broader social and environmental impacts of their design decisions, and strive to develop solutions that are both efficient and sustainable. In conclusion, engineering design optimization is a complex and multifaceted process that involves multiple perspectives, including mathematical modeling, human factors, and ethical considerations. While there are many benefits to optimization, including improved efficiency and reduced costs, there are also several challenges that must be addressed, including the complexity of the design process and the need to consider the needs of users and the broader social and environmental impacts of design decisions. By using a range of tools and techniques, and incorporating multiple perspectives into the design process, engineers can develop optimal solutions that meet the needs of users while maximizing efficiency and sustainability.。
翻译论文高层建筑节能设计
High-rise building energy efficiency design好Pick to: early in the 20th century 70's, the concept of energy-saving building was officially proposed, the core of energy-saving building is to reduce energy consumption and enhance the architecture in building energy efficiency. But as China's sustained rapid economic growth, urbanization, high-rise building development has entered a residential areas, and become the mainstream of the construction industry. So, for the high-rise building energy saving design research, has become a hot issue of concern to the people.This paper discusses high-rise building energy-saving design summarized the present situation of and prospects for our country, the hope can high-rise building energy-saving design better and the international community, provide certain reference.Keywords: high-rise buildings; Energy-saving design; prospectprefaceEnergy is human survival and development of the basic conditions, along with the social progress and the rapid development of social economy, energy demand and supply, saving energy increasingly prominent contradiction between the current human face is one of the important tasks. But the energy conservation of the building is a basic world architecture development trend, as well as building a new growth point of science and technology.Along with the reform and opening-up and the rapid development of economy, China's high-rise buildings built in a high-rise building, the 1980s boom of the development is in the 1990s into the leap development stage. In high-rise building toward a higher, more strong direction at the same time, the energy saving design the general trend of development in human body to improve and health and improving the conditions of comfort, energy efficiency, rational utilization of resources, reduce greenhouse gas emissions, protecting environment for human survival. Therefore, high-rise building energy-saving design has become the world's architectural community, the concern of Chinese construction has become a hot issue.A, high-rise building energy-saving design in the application situationHigh-rise building efficiency is a complicated system engineering, from high-rise building ontology technology to high-level building materials products, high-rise building with equipment of thermal environment there are energy saving potential. Therefore, high-rise building energy saving design should be in guarantee its use function, construction quality and the premise of indoor environment, and adopts various effective energy saving technology and management measures.1, external wall thermal insulation energy-savingFor our country's existing high-rise building is concerned, its external form structural similarity is stronger, the characteristics of their energy consumption is evident, and the main embodied in the winter of heat preservation and heat insulation on summer energy-saving technology, which is beneficial in the popularization and promotion. In high-rise buildings of sino-foreign palisadestructure heat loss is bigger, peripheral protect structure wall and account for a large share. So high-rise building with wall body walling reform of energy-saving technology development is energy-saving design of high-rise building one of the most important aspects of exterior wall thermal insulation technology, development and energy saving material is the main energy saving high-rise buildings implementation. Energy-saving design mainly embodied in the following two aspects:The basic form of the building cannot change circumstances, high-rise building energy-saving design can use of external thermal insulation technology transformation pattern, building energy transformation can consider insulation energy-saving coating and lightweight composite insulation board techniques, such as using. At present, the efficient use of building insulation materials and composite wall practice in domestic popularize continuously, through adopting rock cotton, glass walls of cotton, polystyrene plastic, polyurethane foam plastics, polyethylene plastic new high efficiency heat insulation materials and reduce external heat transfer coefficient.In other functions of high-rise building the reconstructive requirement into consideration under the circumstances of less, energy efficiency design can use exterior insulation technology and exterior wall insulation energy-saving technologies combining transformation pattern. Reform, because its support structure, various technology using relatively independent space bigger, can consider to make the original wall insulation reinforcement again after the outsourcing, appropriate USES each type insulation energy-saving plank with insulation materials, and cooperate with insulation energy-saving door to wait the common use.2, roof insulation energy-savingRoofing is building periphery protect structure place suffers outdoor temperature the highest, area of high-rise building is larger also, as the importance of its energy-saving, particularly prominent therefore, roofing warm-keeping high-rise building for improving indoor temperature environment, save the overall energy consumption is very important.Our usual roofing heat insulating and energy-saving approach is low, the density of thermal conductivity small, bibulous rate is low, have a certain intensity of lightweight high efficiency heat preservation material set in waterproof layer and roof board, this kind of practice is between shop law. There are many high-rise building adopts the lightweight high strength, bibulous rate is very low extruded polystyrene board as thermal insulation material, using pour shop law, and good results were obtained. Pour shop law is set in the waterproof layer insulation layer, its advantage is due to heat preservation material protective effect, make waterproof materials from outdoor air temperature change and construction personnel for damage caused by one.when, greatly extend the waterproof material use fixed number of year, have certain energy-saving effect, but cost is higher.At present, our country USES light color slope roof energy saving design technology. It can effective drainage, prevent, roof leakage and could effectively heat insulation, energy saving. Deep dark roof only less than 30% of sunlight reflected, and nonmetal of light color roof at least 65% of sunlight reflected, reflectivity high roof about save 20 to 30 percent of energy consumption.Three, solar energy savingHigh-rise building energy efficiency design is another aspect of the new energy, development and utilization of solar energy as the one kind of renewable clean energy, is building a new energy ofone of the potential. Solar energy in in most regions of China has the good use conditions, the longest sunshine throughout the year up to 2 800 ~ 3 300 h, annual sunshine hours greater than 2 200 h (namely everyday average sunshine in more than 6 h) area, accounting for land area 2/3 above. Most of the heating area are distributed in long daylight area, these areas of 6hld sunshine hours in total appeared in more than 200 days, more can the d up to 300 d.Solar energy in China on high-rise buildings are mainly: ways of using passive solar heating, solar hot water, active solar energy heating and air conditioning, and solar power, etc. At present, with energy saving design research and the development of the high-rise building design of high-rise building layout, based on the characteristics and use features, additional sunshine with indoor heating equipment between combination of energy-saving design has won more and more people concern. It can make the high-rise buildings in the outdoor meteorological data change drastically circumstances, indoor temperature distribution more balanced, improve the indoor thermal environment and save energy.4 and reasonable use of various base terrain energy-savingThe low south in north high, should follow terrain north-facing slope to parallel is decorated high-rise buildings, such already can obtain sufficient sunshine and good ventilation, and positive view is broad, can reduce the sunshine spacing between high-rise buildings.The north slope in meteorological low on QianHouPai high-rise buildings, tonal the layer, avoid excessive widening sunshine spacing. Also try to use by the formation of the building bottom elevation of semi-closed space, as the auxiliary parking, storage space.In east and west on the slope, high-rise buildings should press vertical slope and follow the direction of slope decorate, der gewaltige hohenunterschied change its layers and height, maintain reasonable sunshine spacing, save land.On the flat terrain, should be decorated south, north to (PianJiao), can be appropriately it to comply with the wind, and can obtain good sunshine, was to reduce the distance, the best way to economical use of land.In marginal areas, according to the site, decorate tower, shape half-unit high-rise buildings; North of the base, decorate high-rise building, is also a good measure of land-saving.Second, high-rise building energy saving design development prospectsFacing the global energy of the growing tension, the countries of the world, especially euramerican developed country to energy saving technology has given full attention. In the past 30 years, countries in the high-rise building design and construction, new building materials, the development and application of the high-rise building energy conservation regulations formulated and implemented, high-rise building energy-saving attestation and management has done a lot of work. Not only save a huge amount of energy, gained considerable economic benefit, and improved the atmosphere environment, reduces the destruction of the ozone layer.But in China the high-rise buildings from energy-saving design work in the early 1990s just start. Therefore, we should do: first, energy aspect in design technology, actively adopt energy-saving building materials, new energy-saving wall body, the roof insulation technology, energy-saving door window insulation and airtight technology, increasing new and renewable energy such as solar energy and low thermal can in high-rise buildings, and further promote the application of different energy heat pump technology, products and recycling waste heat, waste heat technology; Secondly, in the high-rise building energy-saving design management, to strengthen high-level building energy efficiency standardization work, strictly enforce the energy-saving design ofhigh-rise building, high-rise building energy-saving design as high-rise building design meets the standards important assessment project; Again, in the high-rise building, the use of equipment products should adopt the energy-saving heating and air-conditioning equipment, and improve the energy efficiency of equipment operation, in order to achieve the purpose of energy-saving better. In short, the energy conservation of the building is one of the basic state policy of national development, high-rise building is the landmark buildings, so strong, pays special attention to energy conservation design of high-rise building, reasonable use and efficient use of energy resources, improving energy utilization, will promote national and local building energy saving and promote the whole society of building energy saving technology progress. China's high-rise building energy-saving road, should be in learning and borrowing abroad on the basis of successful practice, combined with actual situation in China, the establishment and the high-rise building energy-saving related key technology research and development efforts, accelerate to popularize the foreign advanced applicable energy-saving design technology, promote the development of the high-rise building energy-saving design. I believe in the near future, the joint effort of the high-rise building, our energy saving cause prosperous, bright future will be.References:[1] peak, under the guidance of the principle of sustainable architecture "senior commerce-residence building [J], journal of architectural technology university carried xian, 2003, 192-195 page.[2] Y angShan etc, the structure design of high-rise building [M], China construction science research institute, 1982.[3] li jun, "residential energy-saving design" [M], China architecture &building press, 2003.[4] HaoCheng seal, ZhuLi ZhuY an, concerning the residential building system, the development of thinking [J].j load of survey and design of construction technology and hebei province, January 2001.[5] LTK BREEAM: BREEAM for environmental rating for the Economies - Building homes, Establishment, true Warlord Garston, 2000.高层建筑节能设计摘要: 早在20世纪70年代,建筑节能的概念就被正式提出,建筑节能的核心是减少建筑能耗,提高建筑中的能源利用效率。
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Design and optimisation of the lining of a tunnel in the presenceof expansive clay levelsJ.Pe´rez-Romero a,*,C.S.Oteo b ,P.de la FuentecaUniversity Alfonso X el Sabio,E.T.S.de Ingenieros de Caminos,Canales y Puertos,Avenida de la Universidad 1,Villanueva de la Can ˜ada,28691Madrid,SpainbUniversity of A Corun ˜a,E.T.S.de Ingenieros de Caminos,Canales y Puertos,Campus de Elvin ˜a,15071A Corun ˜a,SpaincPolytechnic University of Madrid,E.T.S.de Ingenieros de Caminos,Canales y Puertos,Ciudad Universitaria,28040Madrid,SpainAvailable online 17April 2006AbstractThis paper describes the study made of the Trasvasur tunnels (Canary Islands,Spain),which were excavated in rocky formations of volcanic origin presenting metric levels of expansive clays.The building of these tunnels had to be abandoned 30years ago,owing to the problems caused by the expansivity of the ground.It was subsequently decided to resume the project,making joint use of geotechnical investigation campaigns,convergence measurements and numerical simulations,thereby contributing towards the optimisation of the cross-section of the tunnelled area,support and lining.The work done has shown that when building tunnels in the presence of expansive clays it is advisable to use circular or similar cross-sections.It has also been found to be advisable to seal offthe excavation as quickly as possible to prevent alteration due to the decompression of the expansive clay levels and their absorbing water from the tunnel itself.Ó2006Elsevier Ltd.All rights reserved.Keywords:Tunnel;Expansive clay;Numerical analysis;Convergence measurements1.IntroductionThe Trasvasur tunnels are part of a set of hydraulic Works connecting several dams in the south of the island of Gran Canaria (Canary Islands,Spain)to the Tirajana Gorge.The water-transfer channel has an average slope of 0.3%and a total length of 13.8km,of which 10.2km run through a tunnel to be excavated by ripper.The works commenced in 1974but were interrupted shortly after-wards because of the instability problems found in the tun-nels known as tunnel IV and tunnel V.In both cases the instability was associated with the presence of levels of expansive clay embedded in a rocky matrix with varying degrees of alteration.Fig.1schematically illustrates the state of the works when they were interrupted.Tunnel IV had been designed to have a total length of 3148m,of which 1206m was built from mouth 3,locatedin the Fataga Gorge,and 434m from mouth 4,located inthe Vicentes Gorge.The sections built had been lined with concrete 0.20–0.30m thick in the side-wall and crown areas,but the floors had not yet been lined when the work was stopped.In 1995the state of the tunnel was inspected (Fig.2)and the lining was found to be deformed and seri-ously cracked between kilometre points 0+715and 0+840.Fig.3schematically illustrates the damage found,consist-ing of the breakage of the lining key,where the clay appears at the top of the tunnelled area,as well as major horizontal deformation of the lining where the clay levels are located in side-wall or floor areas.Tunnel V is 2844km long,of which 464m had been excavated from mouth 1,located in the Tirajana Gorge,and 870m from mouth 2,which like mouth 3is located in the Fataga Gorge.Since much of the work done was tunnelled in healthy rock no lining had been placed.When this tunnel was inspected,the floor had risen considerable between kilometre points 0+670and 0+880,as Fig.4shows,swing to the intrusion of terrain from a clay level0886-7798/$-see front matter Ó2006Elsevier Ltd.All rights reserved.doi:10.1016/j.tust.2006.02.002*Corresponding author.Fax:+34958279124/2231214.E-mail address:joaquin.perez@ (J.Pe´rez-Romero)./locate/tustTunnelling and Underground Space Technology 22(2007)10–22Tunnelling andUnderground Space Technologyincorporating Trenchless Technology Researchthat was located in the lower part of the tunnelled section (Fig.5).The project was resumed about 25years later,with a view to completing these tunnels.A total of six different sections were designed,to be supported by grout 0.05–0.20m thick,bolts injected with resin and arcwelded mesh.The interior housed a ‘‘U’’-shaped reinforcedconcretechannel,as shown in Fig.6.2.Geological framingFig.7shows the geological profile of tunnels IV and V,indicating the sections tunnelled before the Works were abandoned in 1974.The base level in the area consists of sub-horizontal lay-ers of ignimbrites composed of pyroclastic fragments of sand and silt embedded in a clay matrix (Fig.8c).In some levels the clay fraction is clearly dominant,and smectite concentrations of over 90%have been detected (mainly montmorillonite and Na-montmorillonite).Above the ignimbrites phonolitic lava flows are found (Astudillo et al.,1994),consisting of latites and quartzla-tites with the usual hypocrystalline,phaneritic,porphyritic and vesicular flow structure (Fig.8b).Expansive clay levels of volcanic tuffs have been identified,of decimetric and even metric thickness,located between the phonolitic (Fig.8a).Finally,in the more superficial areascolluvialFig.2.State of tunnel IV in1995.J.Pe ´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–2211and eluvial levels appear,resulting from the disturbance ofthe underlying formations.The route of tunnels IV and V passes through all the materials mentioned above.Specifically,the route of tunnel V is located in a thick formation of ignimbrites containing a highly plastic and expansive clay level located both in the side-wall area and beneath the floor of the tunnel section,whereas tunnel IV mostly passes through phonolites with the presence of a significant subhorizontal level of volcanic tuffs,which are also expansive and highly plastic,up to2.50m thick,appearing above the tunnel crown and descending below the floor.There is a clear link between the presence of these highly plastic expansive clay levels and the pathologies described above that forced the works to beabandoned.Fig.4.State of tunnel V in1995.12J.Pe´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–223.Geotechnical characterisation of the materialsIn 1995a new construction firm (Dragados)began towork on the completion of tunnels IV and V.A number of geotechnical research studies were commissioned from the consulting firm Intensa-Inarsa,in order to design solu-tions for the expansive clay areas.In this section the prospecting work and laboratory tests that were carried out,mostly to characterise the compress-ibility properties,expansive potential and structure of the clay levels that had affected and damaged tunnels IV and V,are described.In areas presenting the most serious expansivity problems,six rotating boreholes with continu-ous core-sample recovery were drilled in the interior of tun-nels IV and V.The clay levels were found to be 0.10–2.50m thick.Laboratory tests were performed on nine paraffin sam-ples extracted form the boreholes,and on six samples obtained with a thin-wall sampler in superficial parts of the tunnelled areas.It should be clarified that by the time the sampling was done the tunnels had been abandoned for nearly 20years after they were first excavated,and they had sometimes been used as water-storage tanks,so the properties of the ground close to the tunnelling perimeter were affected by the consequent decompression and mois-ture changes.Table 1shows the results of the laboratory tests carried out on these samples.As can be seen,the samples taken near to the tunnelling perimeter (samples MA-1to MA-6)have a higher relative water content,generally presenting higher liquidity indices (LI),and a considerable lower dry density (c d );the degrees of saturation (Sr)of these samples are very high,sometimes approaching 100%.This demon-strates the intense alteration of the clay near the tunnelled areas,where it has undergone major tensional relaxation and an increase in moisture from the excavation itself.A further indication of the alteration that the clay had undergone can be found in the values for resistance to unconfined compression in the interior of the boreholes ranged between 400and 1200kPa,whereas the decom-pressed clay close to the tunnelled area presented values of around 30kPa.With regard to the expansive potential of the clay,swell-ing-pressure values were zero to highly variable,betweenJ.Pe ´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–2213180and 1600kPa,depending on the moisture content,while the values for free volumetric swelling during soaking exceeded 15%.In situations of semiconfinement,the volu-metric-swelling values ranged between 4.5%and 12.5%,which gives some idea of the major expansive potential of the clay levels.Six X-ray diffraction tests were performed,with very high smectite percentages being found,in general close to 90%,which are clearly related to the expansive nature of the formation.Finally,five oedometric tests were carried out,the results of which allow the structure of the clay and the manner in which it is altered by the decompression andswelling processes to be studied (Pe´rez-Romero et al.,2002).In Fig.9the deformation curves are presented in a representation plane with the standardised voids index (Iv,Burland,1990)compared with the effective vertical pressure (at logarithmic scale).The intrinsic compression line (ICL)and sedimentation compression line (SCL)pro-posed by Burland (1990)for clays have been indicated on this plane,as has the value for the effective vertical yield stress (r vy 0)for each test performed.Samples B ,C and D were taken from the interior of the boreholes,while the remaining two samples (F and H )are disturbed,having been taken from the tunnelling perimeter.As it can be seen,the behaviour of the samples largely depends on the site where they were taken.Thus,the samples from the tunnel-ling perimeter present a higher value for the standardised voids index (Iv),while for vertical-stress range applied,the samples from the interior of the boreholes are highly overconsolidated and also present somewhat higher values for effective vertical yield stress.In short,the evidence is clear that the conditions of depo-sition of these clays,possibly at high temperatures,have provided their structure with a fabric of particle bonds that play a decisive role in their mechanical behaviour.Considering all the data available,geotechnical parame-ters have been assigned for the clay levels and for the solid rock (Table 2).4.Results from the building of an experimental tunnel Once the properties of the expansive clays had been determined,the possibility of altering the cross-section of the project was studied,as shown in Fig.6,to which end a numerical analysis was performed on the interaction between the tunnel excavation and lining (Oteo et al.,1999).An elastic model of the terrain and two hypotheses for its expansivity were considered:the first with volumet-ric expansion of up to 2%throughout the tunnelling perim-eter,and the other incorporating the presence of an expansive horizontal layer 1.50m thick centred on the axis of the tunnel.The laws of bending moments and maximum stresses forecast were compared for three different cross-sections:the project crosssection (horseshoe/with a semi-Table 1Index properties for samples from tunnels IV and V Sample Prospect Tunnel Kilometre point Dry specificweight c d (kN/m 3)Percentage of fines (%)Water content in situ (%)LL (%)PL (%)PI (%)LI (%)Sr (%)USCS MI-1S2IV 0+82515.5999000–35ML MI-2S4IV 0+76012.097–1144470––CH MI-3S4IV 0+76012.910041374790À0.58CH MI-4S4IV 0+76013.299341295673À0.391MH MI-5S4IV 0+76015.5100241154867À0.492MH MI-6S6V 0+645–100–844836––MH MI-7S6V 0+645–100–864541––MH MI-8S6V 0+645–99–854837––MH MI-9S5V 0+67519.692–542331––CH MA-1–IV 0+82010.09164208441640.1100CH MA-2–IV 0+81410.29060198491490.1100CH MA-3–IV 0+80513.33621793841À0.457MH MA-4–V 0+68513.664277829490.077CH MA-5–V 0+69810.5945110345580.190MH MA-6–V0+70515.83025792851À0.198GC14J.Pe´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–22circular arch),a circular cross-section and an oval cross-section(Fig.10).The numerical analysis revealed that the project cross-section was not ideal,since it had to bear the highest bending moments,while the optimal possible behaviour was offered by the circular cross-section.It was not considered advisable to use the tunnel sec-tions that had been previously damaged,since working inside them would be too dangerous.The building of a bypass was proposed to avoid the affected sections,the axis of would lie at a distance of15m.This change in the hor-izontal route would not create any problems because the purpose of the tunnels was to house a water channel.It was decided to build an experimental tunnel that would provide enough information to allow the remainder of the Project to be undertaken with the necessary safe-guards.Fig.11shows the cross-section considered(‘‘SC’’) for the sections suffering from the presence of expansive clays.This cross-section has a circular inner perimeter,sup-ported by steel frames,Bernold sheets and a layer of sprayed concrete0.07m thick.The support base would be made of concrete that was reinforced immediately,inTable2Geotechnical parameters of the expansive clay levels and solid rockMaterial Symbol Parameter Units Value Expansive clay G s Specific weight of solid particles kN/m326.7K Permeability coefficient m/s1·10À8S3r /0Angle of shearing resistance°220K0Coefficient of earth pressure at rest–0.8LL Liquid limit%80e L Void ratio at liquid limit– 2.18CÃc Intrinsic compression index–0.5181C c Compression index–0.4CÃcC s Swelling index–0.1CÃc Solid rock c Unit weight of rock kN/m320E Young’s modulus MPa1500m Poisson’s coefficient–0.2ucs Unconfined compression strength MPa50J.Pe´rez-Romero et al./Tunnelling and Underground Space Technology22(2007)10–2215order to increase its horizontal rigidity.Finally,a concretelining would be fitted,working backwards,to obtain a sec-tion with a total thickness of un 0.30m.For part of the route (SC 0)it was decided not to place the final concrete lining,in order to enable the influence of the lining on the behaviour of the tunnelling area to be studied.Fig.12shows an alternative,less reinforced cross-section,which would be used for the first sections of the experimental tunnel to compare its behaviour with that of the previous ones (sections SC and SC 0).At the beginning of the experimental galleria two different cross-sections were used:cross-section IV,with a sprayed-concrete lining,and cross-section V,which was furtherreinforced with steel frames and Bernold sheets.Fig.13shows a plan of the experimental gallery,indicating the cross-sections used for each tunnel section (two new SC and SC 0cross-sections and two that were the same as those in project IV and V).Convergence measurements were taken in the experi-mental gallery,the results of which are presented in Fig.14.The behaviour of the complete section (SC)was satisfactory,since any deformation was stabilised as soon as the floor was reinforced,and the convergence values recorded were lower than 0.01m.However,in the other sections (IV,V and SC 0)deformation that increased over time was recorded,reaching 0.08m,which meant that the base had to be reinforced as the final stabilisation measure.5.Numerical and parametric analysis of the interaction between the tunnel excavation and the support 5.1.Description of the numerical analysisThe building of the experimental galleries demonstrated the feasibility of the solution and highlighted the difference between the behaviour of sections that had been reinforced to greater or lesser degrees.For these reasons a parametric study was carried out,based on numerical analysis,to determine the relative influence between the various differ-ent factors present in the project (Pe´rez-Romero,2000).The following variables were taken into account:A.Excavation cross-section:semicircular (Fig.15a),cir-cular (Fig.15b)and round arch (horseshoe –Fig.15c).B.Position of the expansive clay level:crown,side walls and floor.C.Thickness of the expansive clay level:1and 2m thick.D.Free volumetric swelling of the clay:0%(nonexpan-sive),2.5%and5.0%.16J.Pe´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–22E.Depth of the tunnel axis:18,30and50m.F.Lining:concrete perimeter lining0.30m thick(simi-lar to section SC in the experimental gallery),con-crete lining0.30m thick on the crown and side walls but not in thefloor area(similar to section SC0in the experimental gallery),andfinally concrete lining0.30m thick on thefloor and sprayed-concrete lining0.05m thick on the side walls and crown(sim-ilar to Section5of the experimental gallery).These variables were combined to generate a total of28 different scenarios,named according to the nomenclature given in Table3.Fig.16shows thefinite-element mesh used for the calculations.The constitutive model used for the calculation was that of a partially saturated soil.The clay levels were described by a critical-state model(modified Cam-clay)and the solid rock was simulated by means of an elastic model.For both materials the parameters deduced from the laboratory tests shown in Table2were used.Once a geostatic stress state in equilibrium had been obtained,the elements corresponding to the excavation cross-section were eliminated,such that the normal pressure at the limit of the excavation was can-celled.A boundary condition was later imposed for zero suction on the perimeter of the excavation,starting a pro-cess of saturation on the ground that progressed inwards.A certain time after the excavation the lining was placed, at which time an impermeable perimeter of the tunnelled area was obtained.5.2.Behaviour of the tunnelled areaIt is interesting to note the tenso-deformational behav-iour of the ground near the tunnelled area.A good case in point is the one called‘‘cs28t’’,consisting of a circular excavation with an expansive level(tuffs)2m thick located at the level of the axis(Fig.17).The lining corresponds to a thickness of0.30m of concrete for the entire perimeter of the tunnelled area(Fig.15b).J.Pe´rez-Romero et al./Tunnelling and Underground Space Technology22(2007)10–2217Fig.18shows the changes in the voids ratio (e )and the angular deformation (de )one hour,one day and one week after the excavation.The combined effect of the decom-pression of the material,the swelling because of expansivity and the proximity of the clay to a critical state generatesvolume increases of up to 4.5%and angular deformations of over 3%.Fig.19shows the stress–strain path of a point located in the expansive clay level (point A in Fig.17).The initial site conditions have been marked with a white dot.Owing to the excavation of the tunnel,point A describes an stress decompression path (using the Cambridge variables:p 0mean pressure,q deviatoric stress)that approaches the crit-ical-state line (Fig.19a),which explains the angular defor-mations estimated in the calculation and the creeping processes observed in 1995in the unsupported sections (Figs.4and 5).The volumetric change estimated (Fig.19b)has two parts:an initial section where the volume increases,associ-ated with the decompression of the clay,and another sec-tion where the volume also increases but as a result solely of its expansive potential.The first section is the result of the mechanical interaction between the excavation,the support and the lining,such that fewer deformations will occur in sections where the support is closed and thicker.The second section mentioned is related to the supply of moisture from inside the tunnelled area (which houses a water channel)and the expansive potential of the clay.These factors are favoured if the perimeter of the tunnelled area is not sealed off.Finally,the influence of the clay structure on its mechanical behaviour has been described above,with dia-genetic bonds between particles.The undisturbed samples were found to have a lower expansive potential than the samples taken from where the ground intrudes into the tun-nelled area,which are extremely disturbed and partially remoulded,affecting their diagenetic bonds.Thus,the decompression and swelling phenomena have a positive synergy effect,since the alteration of the clay implies greater expansive potential,which in turn leads to greater deformation of the clay structure,with the consequent destruction of bonds between particles.5.3.Parametric analysis of resultsTable 4shows the results of the 28cases analysed:max-imum stresses in the lining and estimated displacements on the crown,side walls and floor.Table 3Nomenclature of cases studied in parametric analysis 1st LetterTunnel sectiono ovoidal c circularh horseshoe –round arch 2nd LetterPosition of the swelling layer v vertex (crown)s sidewall f floor3rd LetterThickness and change in volume of the swelling layer11m thick –2.5%volume increase upon soaking 22m –2.5%n 2m –0%d 2m –5%4th LetterDepth of the tunnel axis 818m 330m 550m5th LetterLiningtconcrete perimeter lining 0.30m thick (similar to section SC in the experimental gallery)pconcrete lining 0.30m thick on the crown and side walls but not in the floor area (similar to section SC 0)gconcrete lining 0.30m thick on the floor and sprayed-concrete lining 0.05m thick on the side walls and crown (similar to sectionV)18J.Pe´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–22It is found that the most damaging position for a claylevel harmful is on the side walls,at the same height as the axis of the tunnel,since this is where the highest stresses in the lining are generated.Fig.20shows the bending moments estimated for the three sections of the tunnelled area.It is clear that the excavation planned in the building project (Fig.6)is the one that works the worst,while the section tested in the experimental gallery seems to be opti-mal (Fig.11),being subjected to the smallest bending moments.According to the results of the numerical analysis,the linings with grout 0.05m thick are subjected to veryhighFig.18.Deformations estimated in the proximity of the tunnelled area.J.Pe ´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–2219stresses,which is consistent with the behaviour observedin section SC 0of the experimental gallery,which had to be reinforced with 0.30m of concrete lining.It is advis-able for the lining to be applied to the entire perimeter of the tunnelled area in order to make the section water-tight and prevent the clay from continuing to absorb water.Finally,the results of the numerical analysis of sec-tions with concrete linings 0.30m thick enables conver-gences of 0.01–0.03m to be estimated,i.e.similar values to those for the convergences measured in the experimen-tal tunnel.Table 5summarises the relative importance of the fac-tors considered,among which the position of the expansive layer and its swelling potential,the cross-section of the tun-nelled area and the support and lining thickness particu-larly stand out.6.Optimisation of the lining during the course of the works Once the studies reported above had been carried out,the works commenced,using the cross-section design shown in Fig.11,with the following construction phases.Phase 1:1.1.Excavation in 1.40m advance sections.1.2.Fitting TH-21trusses.1.3.Fitting Bernold sheets welded to the trusswings.Table 4Summary of results for a series of different cases CASE Maximum stresses in the lining Estimated displacements Tensile (MPa)Compression (MPa)Downwards at crown (mm)Inwards at side walls (mm)Upwards at floor (mm)hv28t 0.38À0.56À23.4À4.0 2.0hs28t 12.33À10.97À6.2À22.7 2.3hf28t 2.68À3.70À1.4À2.618.7cvn8t 0.55À0.81À17.0À2.5 2.0cv28t 0.79À2.14À18.4À2.3 1.5cvd8t 1.42À3.63À20.0À2.0 1.3cv23t 0.86À2.18À13.4À2.2 2.0cv25t 1.33À2.55À13.7À2.2 2.8cs18t 6.95À4.78À6.0À23.6 2.7csn8t 1.10À1.03À12.3À19.9 2.8cs28t 11.99À11.13À6.3À25.3 2.2csd8t 18.73À17.76À1.8À29.8 1.7cs23t 16.97À15.54À6.2À20.2 2.6cfn8t 0.24À0.53À6.4À2.412.8cf28t 1.31À2.430.2À2.618.8cfd8t 1.98À4.01 5.0À2.723.9cf23t 2.94À4.559.0À2.423.3cf25t 4.35À6.0811.5À1.726.2ov28t 0.84À2.07À19.3À2.5 1.7os28t 11.61À9.92À7.6À26.9 2.5of28t 0.31À1.32À0.4À2.421.1cv28p 0.69À1.99À18.4À2.3 1.6cs28p 8.81À10.08À5.0À30.1 2.4cf28p 3.02À3.59 1.7À3.621.5hs28g 114.4À118.28À3.5À38.3 2.0cs18g 38.69À31.39À4.6À29.5 2.4cs28g 44.60À50.92À4.1À34.6 1.7os28g61.17À66.227.6À26.92.520J.Pe´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–221.4.Sprayed concrete on the batter side of the Bernoldsheets,0.07m thick.1.5.Fitting bolts,1.50m long,to the resin at the base ofthe trusses.1.6.Sprayed concrete with metal fibres on the Bernoldsheets,0.05cm thick.Phase 2:2.1.Fitting curved floor trusses between the verticaltrusses.2.2.Fitting the reinforcement grid for the batter and soffitof the inverted arch (5B 20/m as main reinforcement and 5B 12/m as distribution reinforcement).2.3.Backfilling with sprayed concrete until the desiredshape for the inverted arch is achieved,approxi-mately 0.30m thick.2.4.Concrete backfilling and track laying.Phase 3(working backwards):3.1.Fitting reinforcement to the soffit of the vertical walls(5B 20/m as main reinforcement and 5B 12/m as dis-tribution reinforcement).3.2.Formwork and concrete pouring for the vertical wallup to the theoretical thickness of 30cm,having previ-ously smoothed the joint between the inverted arch and vertical wall.Phase 4:4.1.Removal of the track.4.2.Concrete pouring for the channel slab.Once phases 1and 2had been completed a convergence measurement campaign was begun,from which the follow-ing results were obtained:in 40sections,the convergence measurement was between 0.6and 1mm,whereas in the remaining four sections the convergence measurements lay between 3and 4mm.Since these convergence measurements are lower than those obtained in the experimental gallery (Fig.14)and also lower than those deduced from the numerical calcula-tion (Table 4),a final numerical analysis was performed to optimise the cross-section of the lining that remained to be built.A retrospective analysis was carried out using an elas-tic model and assuming the presence of an expansive clay level up to 2.5m thick.The position of the clay level was made to coincide for each section with the observations made during the excavation of the tunnels,i.e.above the crown,at the height of the side walls or beneath the inverted arch.The convergence results of the numer-ical model matched the convergence values recorded on the site for a value for the maximum volumetric swelling of the clay of between 1.13%and 0.28%.These values are much lower than those recorded in the laboratory during the course of the preliminary research.This differ-ence shows that the latest execution procedure was opti-mal,principally because of the geometry of the tunnelled area,which was almost circular,and because the support was sealed immediately over the entire perimeter of the tunnelled area,which impeded the decompression of the clay levels and the absorption of water from the tun-nelitself.Table 5Relative importance of the factors considered FactorInfluence on the lining stresses Influence on the liningdisplacements Position of expansive clay layer Very high Very high Cross-section of the tunnelled area HighLow Thickness of expansive clay layer Moderate LowDepth of axisModerate Moderate Potential swelling of clay layer Extreme Moderate Support and lining thicknessExtremeLowJ.Pe ´rez-Romero et al./Tunnelling and Underground Space Technology 22(2007)10–2221。