基于多目标函数的热声制冷机性能优化

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基于遗传算法热电冷联产系统多目标方案优化

ＶＯ１８Ｎｏ１．．
Ｊｎ２２ａ．Ｏ１
基于遗传算法热电冷联产系统多目标方案优化
王耀文，黄锦涛，李祥勇，庄少欣，彬肖
（西安交通大学能源与动力工程学院，西安７０４）１０９
摘要：根据用户全年冷、电负荷设计冷热电三联产系统方案并实现优化运行是决定联产系统经济性的关键．热、建立
ＣＥｐ（）Ｄｓ５
１多目标优化数学模型
１１优化目标函数．
一
式中，，ｋ、，权系数；Ｅｋ、：ｋ为ＰＣ
ｃＰｃ为分产和联Ｈ
产一次能耗率；为分产系统运行成本，￥／；ＣｙＣＥ。ＣＥｃＰＤ、ＤｃＨ分产和联产系统Ｃ２放量，ｙＯ排ｔ．／１２决策变量及约束条件．
优化．
式中，。ＣＨＣ为ＣＰ向用户供能所节省费用，Ｃ．￥；。。。
为售电费用，Ｃ为燃料费用，ＣＭ￥；￥；。为运行维护费用，Ｃ．￥；ｏ为购电费用，Ｃ印初投资，ｉｂ￥；为￥；为折现率；为联产系统寿命年限，．），年
年运行成本如式（）３．
Ｃ叩＝Ｃｆ＋Ｃ０＋Ｃ。Ｍｌｙ—Ｃ。。ｌｂｅ１
，
（）３
Ｃ排放量如式（）Ｏ：４．
ＣＥ＝ＥＤＥＦ×Ｅｄ ’ × ｃ＋ＦＦ。（）４
这里从ＣＨＣＰ系统的节能性、济性和环保性３经个方面人手，立了以一次能源节约率ＰＳ净现值建Ｅ、ＮＶ和Ｃ排放量ＣＰＯＤＥ为优化目标的，发电动力以设备容量、热锅炉容量和制冷设备容量为优化变量余

基于多目标遗传算法的制冷循环优化

基于多目标遗传算法的制冷循环优化关键词：多目标遗传算法、制冷循环、优化随着各种科技的发展，人们对于生活劳动力的需求不断减少。

然而在各种行业中，能源启动机越来越重要，而各种制冷设备则是其中的一个。

在许多应用领域，例如食品、药品和化学制品的生产和运输过程中，需要使用大量制冷设备来确保产品的质量和可靠性。

制冷设备的效率直接关系到能源使用效率，可以在很大程度上影响生产过程中的效率和运营成本。

因此，制冷循环的优化设计越来越成为热点问题。

多目标遗传算法（MOGA）是一种优化算法，它能够在多个目标函数之间寻找最佳的可行解，为制冷循环的优化设计带来了新的思路。

一般情况下，我们需要在多个目标函数之间进行平衡，因此常规的优化算法通常是针对单一目标函数设计的。

例如，当我们想要在保证制冷循环的制冷剂泄漏率最低和能源消耗最低的情况下实现最佳制冷循环效率时，就需要使用MOGA优化算法，这种算法能够将复杂的问题简化为可处理的问题，同时提供有效的解决方案。

MOGA算法的优势在于它是利用遗传算法进行全局搜索，从而寻找出整个解空间中的最佳解，尤其是在存在多个目标函数时。

这种算法的本质是使用遗传算法中的基因操作，如:交叉、变异和选择，等通过多次循环，在多个目标函数之间建立 Pareto 前沿。

在此过程中，算法通过对整个解空间的全局搜索，能够不断寻找更优解，直到最终达到目标并获得最优解。

同时，MOGA最大的优势是可以在无需任何边界条件的情况下，对目标函数进行寻优，这种方法不仅简化了问题的复杂性，而且能快速找到最优解。

制冷循环的设计优化是一个典型的多目标优化问题。

在MOGA的优异表现下，制冷空调行业面临巨大变革。

很多的液体和气体机组使用了新的冷媒，这些冷媒不仅对环境造成威胁，而且对工艺的要求变得更加严苛。

基于此，如何有效地降低制冷循环中制冷剂的使用量，同时确保制冷质量和系统能效，是一项具有挑战性的任务。

MOGA能够在多目标函数间全局寻优，从而找到最适合的工艺参数，例如制冷剂的含量，压缩机的排气压力，调节阀的开度等等。

基于多目标优化的电气设备参数优化策略研究

基于多目标优化的电气设备参数优化策略研究电气设备是现代社会中不可或缺的重要组成部分，而电气设备参数的优化策略研究是提高电气设备性能和效率的关键。

基于多目标优化的电气设备参数优化策略研究，旨在通过考虑多个目标和约束条件，找到最优的参数配置，使电气设备在不同工作条件下具有更好的性能。

在电气设备参数优化中，常见的目标包括提高能源效率、降低成本、提高可靠性和延长设备寿命等。

传统的优化方法往往只考虑单一的目标，无法兼顾多个目标。

而基于多目标优化的方法可以在不同的目标之间寻找权衡，从而得到更加全面和综合的优化结果。

通过多目标优化方法，可以实现电气设备的性能最大化和资源的最优利用。

在电气设备参数优化策略研究中，关键的一步是建立合适的数学模型。

根据电气设备的特性和工作条件，可以使用各种数学模型来描述设备的性能和约束条件。

例如，对于电气设备的能源效率问题，可以建立能源消耗函数来描述设备在不同参数配置下的能耗情况。

对于成本问题，可以建立成本函数来描述设备在不同参数配置下的成本费用。

通过数学模型的建立，可以将问题转化为一个多目标优化问题，从而可以应用各种优化算法来求解最优解。

在电气设备参数优化策略研究中，常用的优化算法包括遗传算法、模拟退火算法和粒子群算法等。

这些算法都具有全局搜索和迭代优化的特点，可以在较短的时间内找到较好的优化结果。

这些算法通过不断更新候选解的参数配置，不断迭代搜索全局最优解。

通过与数学模型的结合，可以将这些算法应用到电气设备参数优化中，从而得到最优的参数配置。

除了优化算法的选择，决策者在电气设备参数优化中的角色也非常关键。

决策者需要对不同目标的重要性进行权衡，以确定最优的参数配置。

决策者可以根据实际需求和约束条件，制定适当的权衡策略，从而实现多目标的优化。

在实际应用中，基于多目标优化的电气设备参数优化策略研究可以应用于各种电气设备，如电动机、变压器、发电机等。

通过优化电气设备的参数配置，可以提高设备的工作效率和能源利用率，降低能耗和成本，延长设备的使用寿命，提高设备的可靠性和安全性。

一种多目标循环性能优化筛选混合工质的方法[发明专利]

专利名称：一种多目标循环性能优化筛选混合工质的方法专利类型：发明专利
发明人：邓娜,景晓宇,蔡荣昌,张于峰
申请号：CN201810155077.4
申请日：20180223
公开号：CN108491579A
公开日：
20180904
专利内容由知识产权出版社提供
摘要：本发明公开了一种多目标循环性能优化筛选混合工质的方法，只需给定制冷、热泵、有机朗肯的工作条件(蒸发温度、冷凝温度)和混合工质组分，以性能系数COP或热效率η及制热量/制冷量或输出功等为多目标函数，将排气压力、压缩比、排气温度等确定为约束条件，即可获得工质的最佳配比及目标函数值。

本发明提出的多目标循环性能优化筛选混合工质的方法，适用于制冷、热泵及有机朗肯等多种热力循环的混合工质的筛选。

并通过调用现有工质物性计算软件中的工质热物性，进行理论循环计算，节约了计算的时间，提高了计算结果的精确性。

申请人：天津大学
地址：300072 天津市南开区卫津路92号
国籍：CN
代理机构：天津市北洋有限责任专利代理事务所
代理人：琪琛
更多信息请下载全文后查看。

制冷压缩机的多目标综合评价和优化选择

制冷压缩机的多目标综合评价和优化选择
曹国庆;涂光备
【期刊名称】《流体机械》
【年(卷),期】2005(033)008
【摘要】利用多目标模糊决策理论,从技术性、经济性、可靠性、外观使用性、功能性5个方面探讨了用性能指标来综合评价和优化选择制冷压缩机的方法和过程.从而为改进和提高制冷压缩机性能提供科学合理的参考,也为比较和评价不同的制冷压缩机提供了一种新的思路和方法.
【总页数】5页(P78-82)
【作者】曹国庆;涂光备
【作者单位】天津大学,天津,300072;天津大学,天津,300072
【正文语种】中文
【中图分类】TB657
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3.公路建设项目路线方案选择多目标综合评价 [J], 彭超志
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因版权原因，仅展示原文概要，查看原文内容请购买。

冷热电联供系统多目标运行优化

第36卷第3期2019年3月控制理论与应用Control Theory&ApplicationsV ol.36No.3Mar.2019冷热电联供系统多目标运行优化张立志,孙波†,张承慧,李帆(山东大学控制科学与工程学院,山东济南250061)摘要:冷热电联供系统能够提高能源利用率和减少碳排放,是解决能源和环境危机的重要途径.本文提出了冷热电联供系统多目标优化运行方法,以运行成本日节约率、一次能源日节约率、CO2日减排率综合最优为目标,利用遗传算法求解,得到关键设备的逐时出力计划,并以此为基础,设计了由MATLAB和LABVIEW构成的运行优化器.最后本文基于TRNSYS与LABVIEW搭建了冷热电联供系统(CCHP)软硬件混合实时仿真系统,验证了运行优化器的有效性,结果表明本文提出的优化运行方法较传统运行模式,可有效提高冷热电联供系统的经济性、节能性和环保性.关键词:冷热电联供;多目标优化;遗传算法;运行优化器;实时仿真引用格式:张立志,孙波,张承慧,等.冷热电联供系统多目标运行优化.控制理论与应用,2019,36(3):473–482 DOI:10.7641/CTA.2018.80465Multi-objective operation optimization ofcombined cooling,heating,and power systemZHANG Li-zhi,SUN Bo†,ZHANG Cheng-hui,LI Fan(School of Control Science and Engineering,Shandong University,Jinan Shandong250061,China) Abstract:Combined cooling,heating,and power(CCHP)systems can improve energy efﬁciency and reduce carbon emissions and have become one of the important ways to address the energy and environmental crisis.In this paper,a multi-objective operation optimization method for a CCHP system is presented.The optimization objective is to maximize the operation cost-saving ratio(OCSR),energy-saving ratio(ESR),and carbon dioxide emission reduction ratio(CERR)of the CCHP system compared to a separate production system.A genetic algorithm(GA)is used to solve the optimal operation problem of the CCHP system.An operation optimizer composed of MATLAB and LABVIEW is designed based on the optimal operation strategy.A CCHP hardware and software hybrid real-time simulation system based on TRNSYS and LABVIEW is designed to verify the effectiveness of the operation optimizer.The results indicate that the multi-objective optimization yields improved economic and environmental beneﬁts,as well as high energy efﬁciency,compared to the traditional mode.Key words:combined cooling,heating,and power system;multi-objective optimization;genetic algorithm;operation optimizer;real-time simulationCitation:ZHANG Lizhi,SUN Bo,ZHANG Chenghui,et al.Multi-objective operation optimization of combined cooling,heating,and power system.Control Theory&Applications,2019,36(3):473–4821IntroductionA combined cooling,heating,and power(CCHP) system based on the principle of energy cascade uti-lization can simultaneously meet electricity,cooling, and heating demands.It provides a large opportuni-ty for energy saving and air pollutant emission reduc-tion[1],thereby attracting considerable attention world-wide[2–6].Most CCHP systems exhibit low operating efﬁciency,poor economy,and signiﬁcant energy waste because of the lack of a reasonable and feasible opti-mization operation strategy.Therefore,the integrated performance of the CCHP system must be improved to further study the optimal operation strategies and de-velop an operation optimizer,which has become a new research topic[7–11].However,as a multi-generation to-tal energy system,the CCHP system has a large number of components,and its energyﬂow is strongly coupled. Meanwhile,the operating conditions are complex and changeable,resulting in the difﬁculty in operation opti-mization.Studies on the CCHP system operation strategies can be divided into two categories:traditional opera-tion strategy and optimization operation strategy.The former is divided into the following electric load(FEL) mode and following thermal load(FTL)mode accord-ing to the priority to meet the electric load or heat-Received24June2018;accepted11October2018.†Corresponding author.E-mail:sunbo@;Tel.:+86531-88395717.Recommended by Associate Editor:GENG Hua.Supported by the National Natural Science Foundation of China(61733010,61320106011,61573224)and the Young Scholars Program of Shandong University(2016WLJH29).474Control Theory&Applications V ol.36ing(cooling)demand.The operating space of dif-ferent regions was divided and discussed by Fang et al.[12].Each region adopted different traditional op-eration modes to improve the overall performance of the system.A hybrid optimization operating strategy based on“FEL”and“FTL”was proposed by Ma-go et al.[13],and the effectiveness of reducing energy consumption,operating costs,and greenhouse gas e-missions of the CCHP system was discussed.Another approach is the optimal operation mode,which aims to obtain the optimal integrated performance of the CCH-P system.The mode is typically solved by intelligent algorithms,such as particle swarm optimization,genet-ic algorithm(GA),and linear programming.Liu et al. and Wu et al.[14]developed a multi-objective optimiza-tion operation strategy that simultaneously involved the energy-saving ratio(ESR)and the cost-saving ratio(C-SR)of the CCHP system.The optimal solution was hi-erarchically calculated by the mixed-integer nonlinear programming(MINLP)approach.Zhao et al.[15]pro-posed a multi-objective optimization model that con-sidered the energy,economy,and environment for the CCHP system,and the Pareto optimal solution set was solved by the niche particle swarm algorithm.A multi-objective optimization model of the CCHP system was established by Zeng et al.[16],in which the equal weight-s method was used to convert multi-objective problem to a single objective problem,and the optimal solution was obtained using the multi-population genetic algo-rithm(MPGA)method.Wei et al.[17]presented a multi-objective optimization model to maximize the ESR and minimize the energy costs of the CCHP system.The non-dominated sorting genetic algorithm–II(NSGA–II) was employed to identify the optimal operation strate-gy.Indeed,the multi-objective optimization was widely researched in recent years[18–19].In summary,the application of the traditional opera-tion mode is simple and easy,which is a typical method used by most CCHP systems.However,it can easily cause energy waste and difﬁcultly in achieving the op-timal integrated performance of the system.In contrast, studies on the optimization operation strategy,which can further enhance the overall performance of the CCHP system,only apply at the theoretical stage.The operation optimizer that can be applied to the actual sys-tem has not been developed hitherto;therefore,it can-not solve the current situation,and the CCHP system still adopts the traditional operating strategy.Herein,a multi-objective optimization model inte-grating the operation cost-saving ratio(OCSR),ESR, and carbon dioxide emission reduction ratio(CERR) is designed for the CCHP system.The GA is used to obtain the optimal operation strategy of the system to solve the proposed model.An operation optimizer com-prising MATLAB and LABVIEW is designed based on the proposed strategy.Finally,a CCHP hardware and software hybrid real-time simulation system based on TRNSYS and LABVIEW is built to verify the effec-tiveness of the operation optimizer.The results indicate that the multi-objective optimization improves the eco-nomic and environmental beneﬁts,as well as the energy efﬁciency,compared to the traditional mode.This study is organized as follows:the multi-objective optimization model of the CCHP system is shown in Section2;the operation optimizer is present-ed in Section3;the real-time simulation system is de-signed,and the optimization results are presented and analyzed in Section4;and the conclusions are summa-rized in the last section.The primary contributions of this paper are summa-rized as follows:1)The CCHP system coupled with solar energy is proposed,and its design and operational principles are presented.2)A multi-objective optimization operation strat-egy is employed to guarantee that the proposed system achieves the optimal operational performance.Based on the proposed strategy,an operation optimizer com-prising MATLAB and LABVIEW is designed such that the optimization operation strategy can be applied to an actual system.3)A CCHP hardware and software hybrid real-time simulation system based on TRNSYS and LABVIEW, which is a real-time simulation system similar to the actual system,is built to verify the effectiveness of the operation optimizer.2Multi-objective optimization model2.1System design and energyﬂow analysisThe CCHP system involves a wide range of tech-nologies and components and typically consists of a power generator unit(PGU),a heat recovery unit,an absorption chiller(AC),and an auxiliary boiler.In this study,a solar photovoltaic array(PV)and an electric chiller are added to the traditional CCHP system struc-ture(Fig.1).The electricity subsystem consists of a PV and a PGU by natural gas(NG).The generated electricity can be used to satisfy the user’s electric demand,and can drive the electric chiller to meet the user’s cooling load. The system operates in the grid-connected mode.When redundant power is generated,it will be FEL back in-to the power grid(PG).The shortfall power can also be supplemented by the PG.In addition,the waste heat produced by the PGU,which includes the jacket water heat and exhaust heat,is recovered and used to produce cool air in the absorption chiller and for heating,re-spectively.When the recovered heat is insufﬁcient,the auxiliary boiler is used to provide additional heat.The electric chiller is responsible for refrigeration when theNo.3ZHANG Li-zhi et al:Multi-objective operation optimization of combined cooling,heating,and power system 475absorption chiller is not meeting the cooling demand.The energy ﬂow of this CCHP system should ﬁrst be analyzed to study the optimal operating strategy.In Fig.1,E pv and E pgu represent the electricity generated by the PV and PGU,respectively;E grid is the electrici-ty from the grid;E ech is the input power of the electric chiller;and E ,H ,and C represent the user demand for electricity,heating,and cooling,respectively.Fig.1Structure and energy ﬂow of the CCHP systemThe electrical energy balance is expressed as follows:E (t )+E ech (t )=E pv (t )+δ(t )E grid (t )+E pgu (t )−(1−δ(t ))E gs (t ),(1)where δ(t )represents the state variable of the interac-tion with the power grid (i.e.,1represents that the CCH-P system purchases electricity from the PG in period t ,whereas 0implies that the system sells electricity to the PG in period t ).The heating balance of the CCHP system isH (t )+Q ach (t )+Q ex (t )=Q re (t )+Q b (t ),(2)where Q ach is the input power of the absorption chiller;Q ex is the heat loss of the system;Q re represents the waste heat produced by the PGU;and Q b represents the heating power from the auxiliary boiler.The cooling balance of the CCHP system isC (t )=C ach (t )+C ech (t ),(3)where C ach and C ech are the cooling output of the ab-sorption chiller and the electric chiller,respectively.2.2Multi-objective operation optimization mod-elBased on the electricity price,energy price,and oth-er parameters,combined with the weather information and load forecasting data,a multi-objective optimiza-tion method is used to solve the optimal hourly output plan of the system equipment.2.2.1Optimal variablesThe energy ﬂow analysis showed that the PGU is the key piece of equipment in the CCHP system,which has an important inﬂuence on the operation of the entiresystem.Meanwhile,the electric chiller with a higher coefﬁcient of performance (COP)can increase the cool-ing efﬁciency of the system,which is relatively easy to control.Therefore,E pgu and C ech are chosen as the variables to be optimized;they are continuous vari-ables.The energy output of the AC and the gas boiler can be easily obtained after the optimal variables are determined.In addition,the PV always operates at the maximum power for fully utilizing the solar energy.2.2.2Objective functionsThe multi-objective (MO)function considering three aspects of economy,energy,and environment is chosen to evaluate the CCHP system compared to a sep-arate production (SP)system and improve the integrated performance of the system.Herein,the electricity de-mand of the SP system is supplied by the PG,whereas the heating and cooling demands are met by the boiler and electric chiller,respectively.The OCSR is selected as the economy objective:OSCR =1−F 1(t )F 2(t ),(4)whereF 1(t )=T ∑t =1(P gb (t )E CCHP ,gb (t )−P gs (t )E CCHP ,gs (t )+G CCHP ,gas (t )P gas (t )),F 2(t )=T ∑t =1(P gb (t )E SP ,gb (t )+G SP ,b (t )P gas (t )),P gb (t )and P gas (t )are the electricity price and the bio-gas price in period t ,respectively.P gs (t )represents the subsidized price of the redundant electricity sold back to the PG.The ESR is selected as the energy objective:ESR =1−T ∑t =1(E CCHP (t ))T ∑t =1(E SP (t )),(5)where E CCHP (t )and E SP (t )represent the energy con-sumption of the CCHP and SP systems in period t ,re-spectively.The CERR is selected as the environment objective:CERR =1−G 1(t )G 2(t ),(6)whereG 1(t )=T ∑t =1(E CCHP ,gb (t )×u grid +G CCHP ,gas (t )×u gas ),G 2(t )=T ∑t =1(E SP ,gb (t )×u grid +G SP ,gas (t )×u gas ),u grid and u gas represent the carbon dioxide emission conversion factors of the electricity from the grid and the fuel,respectively.476Control Theory&Applications V ol.36Therefore,the objective functions can be expressed as follows:MAXα1OCSR+α2ESR+α3CERR,(7) where0 α1,α2,α3 1,andα1+α2+α3=1,α1,α2,α3are the weights of the economy objective, energy objective,and environment objective,respec-tively.To consider each indicator,we setα1=α2=α3=1/3.2.2.3ConstraintsThe optimization process must meet the following inequality constraints to ensure the rationality of the model:0 E pgu(t) N pgu,(8)0 C ech(t) N ech,(9)0 Q b(t) N b,(10)0 C ach(t) N ach,(11) where N pgu,N ech,N b,and N ach represent the rated capacities of the PGU,electric chiller,auxiliary boiler, and absorption chiller,respectively.2.2.4Solution methodTo solve the proposed model,the GA is used to de-termine variables E pgu and C ech.Fig.2shows the op-erating process of the solution method.First,the relevant parameters,such as the energy loads of a building,performance parameters of the C-CHP and SP systems,cost parameters,and GA param-eters,must be preset.The initial values of the optimiza-tion variables are coded in binary form.Subsequently, theﬁtness of the objective function is calculated.The searching process will be stopped if the convergence criterion is satisﬁed.The binary codes are then decoded into decimalization,and the optimization results,E pgu and C ech,will be obtained.Additionally,the operation strategy can be acquired;otherwise,the search return-s to the calculation again through selection,crossover, and mutation until the optimal criterion is satisﬁed.3Operation optimizer designBased on the abovementioned analysis,current s-tudies on the optimization operation strategy of the C-CHP system are only applicable at the theoretical stage, and no operational optimizer has yet been developed for use in CCHP systems because the complexity of the system complicates the integration of the data acquisi-tion and operation optimization functions.In particular, the challenging optimal operation strategy of the CCHP system complicates the solution of the intelligent algo-rithm.While MATLAB contains a rich set of intelligent algorithm toolkits that are convenient to invoke,it is al-so an excellent choice as the operating environment for CCHP system optimization strategies.However,MAT-LAB exhibits drawbacks in data acquisition and stor-age,human–computer interaction,and other aspects.LABVIEW and MATLAB are used to design a run-ning optimizer to solve the aforementioned problems (Fig.3).LABVIEW exhibits powerful data collection capability and has a good hardware and software inter-face,which can develop an excellent human-computer interaction interface.Therefore,the operation optimiz-er based on LABVIEW and MATLAB can complete the data acquisition and optimization functions of the CCH-Psystem.Fig.2Operating process of the solutionmethodFig.3Operation optimizer of the CCHP system1)Data acquisition.No.3ZHANG Li-zhi et al:Multi-objective operation optimization of combined cooling,heating,and power system477The CCHP system has variousﬁeld devices,in-cluding internal combustion engines(ICEs),absorption chillers,electric chillers,and PLC controllers.The communication methods for collecting the operating da-ta of different equipment are different,thereby compli-cating data collection.For example,the PLC controller, when collecting onsite sensor data,communicates with the upper computer via the Ethernet,whereas the oper-ating data of the internal combustion generator set are collected via the Modbus protocol.Simultaneously,in the CCHP system,the related data of cooling and heat energy change in minutes because of the hysteresis of cooling and heat energy,while the electrical data change in seconds.In the data acquisition process,if the same data update frequency is used,it will inevitably cause data redundancy or loss of valid data.In summary,the data acquisition of the CCHP system has two major d-ifﬁculties:a variety of device communication methods and a multi-time-scale data collection problem.The OPC technology is used to solve the afore-mentioned problems,which can unify the data access method and integrate different communication proto-cols;therefore,it is an effective method to centrally collect operating data.Subsequently,in the OPC serv-er,data items with different time scales are respectively placed in different OPC groups,and the matching sam-pling rate is set to solve the problem of multi-time-scale data collection.The OPC server included in LABVIEW renders the data acquisition problem simple.Based on the LABVIEW development environmen-t,the MATLAB SCRIPT node technique implements the data interaction between LabVIEW and MATLAB, resulting in two software that are seamlessly connected and deeply integrated.Therefore,the operating status data of the CCHP system and the load data collected in LABVIEW can be transferred to MATLAB for op-timization calculations.The same optimization results calculated in MATLAB can also be sent to LABVIEW through this technology.2)Operation optimization.The important function of the operation optimizer is to realize the optimal operation of the CCHP sys-tem,which improves its overall performance.A multi-objective optimization model considering indexes from the economy,energy,and environment perspectives is designed for the CCHP system in MATLAB.To solve the proposed model,the GA is used to obtain the opti-mal operation strategy of the system.Fig.4shows the workﬂow of the operation optimizer.First,the relevant parameters in LABVIEW must be preset,such as the ca-pacity and efﬁciency parameters of the devices,natural gas prices,grid prices,and emission factors,while si-multaneously forecasting the building load and PV out-put data.Subsequently,according to the operational re-quirements,the weights of the economy objective,en-ergy objective,and environment objective are set to de-termine the objective function of the optimal operation. Furthermore,the optimization period and the interval are set to one day and1h,respectively.Based on the abovementioned data,the day-ahead operation strategy of the CCHP system is obtained using the GA in MAT-LAB,which is transmitted to LABVIEW for analysis. If the optimization result does not meet the expectation-s,the objective function should be re-determined,and the optimization solution can be performed again un-til the optimal result is obtained.Additionally,to recti-fy the error of the current prediction data,based on the day-ahead optimization result and the real-time load da-ta collected by LABVIEW,the rolling optimization is performed in one-hour units.The result of the rolling optimization is then solved in MATLAB and transmit-ted to LABVIEW.Finally,the optimization instruction-s are passed to the devices through OPC technology to complete the operation optimization of the entire CCHP system.During the operation optimization,LABVIEW collects and stores the operating data in real time and monitors the operating status,such that the CCHP sys-tem efﬁciently and stably operates.4Real-time simulation system and case analysis4.1Real-time simulation system designThe theoretical analysis and the ofﬂine simulation cannot be realized to verify the operating optimizer de-signed herein.A real-time simulation system that is close to the real system must be provided.However,the simulation system design is extremely difﬁcult.First, the CCHP simulation model designed should be consis-tent with the operating characteristics of the actual sys-tem.The operational data are real time,and a data inter-face that communicates with the upper computer must be present.Therefore,a software and hardware hybrid real-time simulation system is designed based on TRN-SYS and LABVIEW(Fig.5).The software used in the simulation system are TRNSYS,MATLAB,and LAB-VIEW.The hardware used is the NI data acquisition de-vice supported by LABVIEW to realize the integrated software and hardware.A real-time simulation is real-ized through TRNSYS modeling;thus,a CCHP real-time simulation model and a data interaction interface are provided for the operating optimizer.478Control Theory&Applications V ol.36Fig.4Workﬂow of the operationoptimizerFig.5Construction of the CCHP hardware and software hybrid real-time simulation system1)CCHP real-time simulation model.As an energy system simulation platform,TRN-SYS can simulate the input and output properties of each essential element of the CCHP system,such as the ICEs,chillers,boilers,and heat exchangers.Fur-thermore,TRNSYS can achieve modular encapsula-tion with each module connecting with each other ac-cording to energy,mass,andﬂuid laws to complete the system model.The input parameters of each mod-ule,such as the power of the ICE and electric chiller, can be read from the outside.In TRNSYS,a pace set-ting module can be used to adjust the TRNSYS sim-ulation speed to operate in real time.2)CCHP real-time simulation model.The system implements communication between the CCHP simulation model and the upper optimizer through MATLAB,LABVIEW,and the NI data ac-quisition card.TYPE155,a subroutine of TRNSYS, is employed to implement the TRNSYS–MATLAB interface module,which uses the component object model(COM)technology.The COM is a type of ob-ject model using components to release units,which allows for the software components to interact in a uniﬁed manner.MATLAB and LABVIEW achieve data interaction through the MATLAB SCRIPT node. Similar to the actual system,the process controller is indispensable to ensure the stability of the simula-tion system.Therefore,the Siemens PLC controller is selected,which writes the data acquisition and PID control program.Therefore,the operating data outputNo.3ZHANG Li-zhi et al:Multi-objective operation optimization of combined cooling,heating,and power system 479from the simulation model should be converted into analog and digital signals,such that data can be ac-quired by the process controller.The LABVIEW and NI data acquisition cards can perform AD conversion and signal processing on the collected data.On the one hand,the optimization instruction is transmitted to the simulation model by the PLC controller.On the other hand,the operational data (such as jacket wa-ter temperature,domestic hot water temperature,and valve switching)from the TRNSYS simulation model are converted into analog signal and digital signal out-puts to be collected by the process controller,which provides a real operating environment for the operat-ingoptimizer.Fig.6Overall construction of the CCHP hybrid real-timesimulation testing systemIn summary,Fig.6shows the overall structure of the software –hardware hybrid real-time simulation testing system designed based on the operating opti-mizer.Figure 7depicts the system construction of the hybrid simulation testing system after design and con-nection.Fig.7System construction of the hybrid real-time simulationtesting system4.2Case analysis 4.2.1Basic dataThe hourly electric,cooling,and heating loads of an ofﬁce building in Jinan during the typical winter and summer days are chosen to verify the availability of the proposed operation optimizer (Fig.8).(a)Winterday(b)Summer dayFig.8Typical daily demand curvesBased on the abovementioned load data,the rat-ed capacities of the major equipment in the CCHP system can be determined as shown in Table 1.The technical parameters listed in Table 2are considered constant.Table 3presents the GA parameters.Table 4shows the time-of-use price and timetable.480Control Theory&Applications V ol.36Table1Capacities of the major equipmentDevices Capacities/kWPGU32PV20Absorption chiller44Electric chiller28Boiler65Table2Technical parameters of the CCHP systemParameters ValuesRated COP of the electric chiller 4.0Rated COP of the absorption chiller 1.2Efﬁciency of the boiler0.82Efﬁciency of electricity generation0.35Table3GA parametersVariable ValuesIndividuals100Generations500Crossover probability0.7Mutation probability0.1Table4Time-of-use price of electricityElectricity price/Subsidized price/(￥·(kWh)−1)(￥·(kWh)−1) Peak value(8:00∼11:00) 1.0690.62(19:00∼23:00)Flat value(7:00∼8:00)0.6870.36(11:00∼19:00)Valley value0.3630.12(23:00∼7:00)4.2.2Result and analysisThe FEL mode is selected as a reference to com-pare and analyze the integrated performance of the C-CHP system in the optimized operation -bined with the abovementioned data,the OCSR,ES-R,and CERR of the system under the two operating modes can be calculated(Table5).Table5Comparison between the optimal strategy and the FEL modeSeason Mode OCSR/%ESR/%CERR/%Optimized29.5010.5736.38 WinterFEL25.34 5.5633.43Optimized41.2228.0156.13 SummerFEL40.3321.2751.92According to the data in Table5,the economy, energy,and environmental indicators are higher than those of the FEL model when the CCHP system is un-der the optimized operation mode in both the summer and winter conditions,demonstrating that the optimal strategy presented herein signiﬁcantly affects the im-provement in the integrated performance of the CCHP system.A detailed analysis is performed below.1)Winter condition.When the CCHP system is operating in the win-ter conditions,the chillers will not be active because cooling is not demanded,and the system is primari-ly used to meet the user’s electricity and heating de-mand.Fig.9shows the power changes in the primary units in the system operation.Fig.10depicts the heat changes in the primary units in the system operation.As shown in Figs.9and10,the plant output grad-ually changes on an hourly basis during the operat-ing period.The electricity outputs of the PV are rel-atively low from10:00to17:00because of the low temperature and light intensity in the winter.In the optimized operation mode,when the electric load-s reach the peak at9:00to19:00,the PGU operates at a high load state,and the grid provides the surplus of electricity when the PGU and PV cannot meet the electrical demand.When the electric loads are low at0:00to6:00,the PGU is in operation because the heating demand is high when the PGU and auxiliary boiler are required to provide heating energy for the building,and the redundant electricity is available for feedback to grid.Overall,the power of the auxiliary boiler changes with the PGU power during the op-erational period,demonstrating the opposite change trend.However,in the FEL mode,the power of the PGU is following the electric load,and the PGU op-erates at a rated power at9:00to19:00while the heat-ing demand is low;hence,it is bound to cause energywaste.(a)Optimal operation mode。

基于多目标优化的空调调度算法研究

基于多目标优化的空调调度算法研究空调技术的发展和应用相当广泛，但是目前现有的空调调度算法主要是单一目标优化，而对于大型建筑群体区域内的空调调度问题，单一目标优化算法的效果无法达到满意的效果，因此研究如何利用多目标优化算法来改进现有的空调调度算法是非常有必要的。

多目标优化算法是指在满足多个目标需求的情况下，对一组能够满足约束条件的决策变量进行优化，以获得多个最优解。

多目标优化算法的应用非常广泛，在各个领域都有很好的应用实践。

在空调调度问题上，多目标优化算法同样适用。

在空调调度问题中，我们需要考虑如下多个因素：舒适度、能耗、空气质量、设备损耗等等。

这些因素之间并不是简单的一对一的关系，而是相互交织、相互影响的。

因此在调度过程中需要综合考虑所有的因素，才能达到最优的效果。

空调调度问题是一个典型的多目标优化问题，可以采用不同的多目标优化方法来解决此问题。

其中常用的方法有多目标遗传算法、多目标粒子群算法、多目标差分进化算法等等。

在这些方法中，遗传算法的应用比较广泛，在空调调度问题上效果也比较显著。

基于遗传算法的空调调度算法主要包括以下几个步骤：首先设计适应度函数，通过适应度函数来评价空调调度策略的好坏程度；然后设计变异算子和交叉算子，通过变异和交叉来产生新的解，并保证新的解仍满足约束条件；最后进行选择操作，选出适应度较高的解作为下一代的父代。

通过不断的重复以上的步骤，逐渐优化空调调度策略，直至满足所有优化目标。

在具体的实现过程中，我们需要设计适合实际情况的适应度函数，并调整遗传算法的参数，以达到最佳的优化效果。

同时，为了保证算法的实用性和可行性，还需要对算法的可调节性和鲁棒性进行分析和测试。

总的来说，基于多目标优化的空调调度算法研究是一个比较复杂和繁琐的过程，但是它可以使得我们在调度过程中更好的平衡各种目标，使得空调系统在舒适度、能效、空气质量等方面都达到最优化的效果。

同时，对于建筑群体的空调调度问题，采用多目标优化算法比单一目标优化算法更加符合实际需求，能够获得更加令人满意的优化效果。

基于NSGA多目标遗传算法直接空冷凝汽器设计优化的开题报告

基于NSGA多目标遗传算法直接空冷凝汽器设计优化的开题报告一、选题背景空气冷凝器广泛应用于空调、冷冻机等空调系统中，是空调系统中重要的换热器件之一。

随着人们对环境保护和能源节约的要求越来越高，空气空调系统中的空气冷凝器设计相应地得到了更高的要求。

传统空气冷凝器在设计时，通常采用经验公式和定性评估方法进行初步设计，再通过经验调整和仿真检验等手段进行优化。

这种方法虽然具有实用性，但设计结果可能并不是最优的。

随着计算机技术的不断进步和多目标优化算法的不断发展，利用多目标优化算法进行空冷凝汽器设计优化的方法已经成为了空调系统设计的趋势之一。

多目标优化算法可以在考虑建模精度的同时，根据不同的设计需求，寻找到最优的设计方案。

本课题旨在研究运用多目标优化算法优化空气冷凝器设计，有望从理论上提高空冷凝汽器的制热能力和能耗效率，从而更好地满足现代社会能源节约和环境保护要求。

二、研究内容1.调研和分析空气冷凝器设计的主要参数以及其对空气冷凝器性能的影响。

2.探究多目标优化算法的基本原理及其在空气冷凝器设计中的应用，选取适合的算法。

3.运用数值模拟方法建立空气冷凝器的数值模型。

4.以制热能力、能耗效率、压降等指标为优化目标，利用选定的多目标优化算法，寻找最优的空气冷凝器设计方案。

5.验证优化方案的有效性和可行性。

三、研究方法1.基于ANSYS Fluent软件，建立空气冷凝器的数值模型，对空气冷凝器的流场和换热特性进行模拟分析，确定设计变量及其范围。

2.采用NSGA多目标遗传算法，构建多目标优化模型。

将制热能力、能耗效率、压降等指标定义为优化目标，确定设计变量的范围和相应的约束条件，得出最优设计方案。

3.在模拟和优化过程中，利用Design Explorer将结果可视化，并进行结果分析和比对。

4.验证和优化所得方案的可行性和有效性。

四、预期成果1.建立空气冷凝器模型的数值分析方法，验证其可行性和有效性。

2.优化设计方案，得到最优的空气冷凝器设计，使其制热能力和能耗效率达到较高的水平。

联合运行冷水机组负荷分配的多目标优化研究

ａｙｐｏｅｓＡｅｒｓ．ｏＵｔｉｎｍｅｏＳｕｅｅｅｔｔｅｂｓｏｐｏｉｅｓｌｔｎｏｅｐｏｌｍ．ｅａｐｏｃｓｒｒｃｓ．ｎａｅｔｔ．ｏｐａｔｄｉｓｄｔｓｌｃｅｔｍｒｍｓｏｕｉｆｔｒｂｅＴｈｐｒａｈｉｈｏｈｃｏｈｔｓｅｎａ４ｕｉｃｉｌｒｓｓｅｒｓｎａｙｉ．ｅｔｄｏ．ｎｔｈｌｙｔｍｉｐｅｅｔｅｎｎａｌｓｓ
２０年１０８２月
洁净与空调技术Ｃ＆ＣＣＡ
第４期
联合运行冷水机组负荷分配的多日标优化研究
武汉第二船舶设计究所李学斌
摘要根据冷水机组的性能系数（ＯＣＰ）和能耗与其部分负荷率（ＬＰＲ）的特性，提出了同时考虑性能系数和能耗最优的机组负荷分配多目标优化模型。基于改进的非支配解排序的多目标遗传算方法（ＧＩ，求出ＮＳＡＩ）Ｐｒｔａｅｏ最优解。采用理想点方法得到最优折中解。文中给出了４台机组运行的优化算例。关键词负荷分配；多目标遗传算法；理想点方法；冷水机组
到一个解，并且还要求决策者的先验知识。近年来
从Ｐｒｔ最优解集中挑选最后折中解。文中讨论了ａｅｏ
４台机组负荷分配的实例。
１模型和求解
对于多目标优化问题，在Ｐｒｔ解集求出后，ａｅｏ
出现的进化算法是解决多日标优化问题的有效算法…。对于多目标优化问题，当Ｐｒｔａｅｏ最优解集求

一种生物质气化冷热电联供系统的多目标优化方法与流程

一种生物质气化冷热电联供系统的多目标优化方法与流程随着全球能源危机的加剧和对环境保护的需求不断增长，生物质能作为可再生能源备受瞩目。

生物质气化冷热电联供系统是一种将生物质资源转化为电力、热能和制冷能的技术，具有很高的应用潜力。

然而，如何优化该系统以提高能源转化效率成为了研究的重点。

一种多目标优化方法是解决这一问题的有效途径。

多目标优化方法能够同时考虑多个目标，如提高能源利用率、降低碳排放和降低投资成本等。

在生物质气化冷热电联供系统中，多目标优化方法可以帮助我们找到最优的操作策略和系统配置，以实现更高的效益。

多目标优化方法的流程一般分为以下几个步骤：首先，确定目标函数。

在生物质气化冷热电联供系统中，我们可以考虑多个目标，如最大化电力输出、最大化热能供应、最小化碳排放等。

根据实际需求和系统特点，选择合适的目标函数。

其次，建立模型。

建立生物质气化冷热电联供系统的数学模型是优化的基础。

模型要考虑到能源的转化和分配过程，包括生物质气化过程、燃气发电过程、余热回收利用等。

通过模型，我们可以计算不同操作策略下的目标函数值。

然后，选择优化算法。

多目标优化方法有很多种，如遗传算法、粒子群算法等。

选择合适的优化算法可以提高优化效率和精度。

接下来，进行优化计算。

根据所选的优化算法，利用计算机进行数值计算，寻找最优的操作策略和系统配置。

计算过程中，会生成一组解集，称为“帕累托前沿”，包含了系统在不同目标下的最优解。

最后，进行结果评价与决策。

通过对“帕累托前沿”的分析，可以比较不同解的优劣，选择出最佳的解决方案。

在做出决策时，还要考虑到系统的可行性、稳定性和经济性等因素。

通过以上流程，我们可以得到一种生物质气化冷热电联供系统的多目标优化方法与流程。

这种方法能够帮助我们提高能源转化效率，降低能源消耗和环境污染，推动可再生能源的发展。

随着科学技术的不断发展，相信这种方法将在未来得到更广泛的应用。

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蒋智杰１ꎬ２ ꎬ吴锋２ ꎬ章超明１ꎬ２ ꎬ李蒙２ ꎬ云慧敏２
(１. 武汉工程大学机电工程学院ꎬ湖北武汉４３００７３ꎻ ２. 武汉工程大学光电信息与能源工程学院ꎬ 湖北武汉４３００７３)
摘要:在考虑热漏、热阻及不可逆因子等因素的情况下ꎬ建立复指数传热规律的不可逆热声制冷机循环模型. 以热声制冷机输入功率Ｐꎬ系统输出率Ａꎬ系统不可逆损失率Ｑꎬ制冷率Ｒ构建多目标函数ꎬ采用线性加权评价函数法求解ꎬ分析多目标函数与各参数的优化关系ꎬ得出多目标优化可以较好地协调各性能指标间关系的结论.
Ｋｅｙｗｏｒｄｓ: ｔｈｅｒｍｏａｃｏｕｓｔｉｃｒｅｆｒｉｇｅｒａｔｏｒｓꎻ ｃｏｍｐｌｅｘｉｎｄｅｘｈｅａｔｔｒａｎｓｆｅｒꎻ ｆｉｎｉｔｅ￣ｔｉｍｅｔｈｅｒｍｏｄｙｍａｍｉｃｓꎻ ｍｕｌｔｉ￣ｏｂｊｅｃｔｉｖｅｏｐｔｉｍｉｚａｔｉｏｎ
０引言
多目标最优化[１￣２] 是一门迅速发展起来的方法ꎬ是最优化的一个重要分支ꎬ它主要研究在某种意义下多个数值目标的同时最优化问题ꎬ吸引了不少学者的关注. 多目标方法有传统多目标优化方法ꎬ也有近代的遗传算法ꎬ其问题的本质是在很多情况下ꎬ各个子目标可能是相互冲突的ꎬ一个子目标的改善ꎬ有可能引起另一个子目标性能的降低ꎬ也就是说ꎬ要使多个子目标同时达到最优是不可能的ꎬ而且只能在他们中间进行协调和折中处理ꎬ使各个子目标函数尽可能达到最优. 很多时候对热机[３￣４] 采用单目标优化有时得不到最优值ꎬ而采用多目标均能得到最优值ꎬ且多目标优化可以较好地协调各性能指标间的关系ꎬ使所要求的各项指标均能达到较优ꎬ因此多目标优化比单目标优化更加合理.
Ａｂｓｔｒａｃｔ:Ａｃｙｃｌｅｍｏｄｅｌｇｅｎｅｒａｌｉｚｅｄｉｒｒｅｖｅｒｓｉｂｌｅｔｈｅｒｍｏａｃｏｕｓｔｉｃｒｅｆｒｉｇｅｒａｔｏｒｗｉｔｈｃｏｍｐｌｅｘｅｘｐｏｎｅｎｔｉａｌｈｅａｔｔｒａｎｓｆｅｒｗａｓｂｕｉｌｔｂｙｕｓｉｎｇｆｉｎｉｔｅ￣ｔｉｍｅｔｈｅｒｍｏｄｙｍａｍｉｃｓｉｎｗｈｉｃｈｈｅａｔｒｅｓｉｓｔａｎｃｅｈｅａｔｌｅａｋａｇｅａｎｄｉｎｔｅｒｎａｌｄｉｓｓｉｐａｔｉｏｎｗｅｒｅｃｏｎｓｉｄｅｒｅｄ. Ｗｅｃｏｎｓｔｒｕｃｔｍｕｌｔｉ￣ｏｂｊｅｃｔｉｖｅｆｕｎｃｔｉｏｎｕｓｉｎｇｔｈｅｒｍｏａｃｏｕｓｔｉｃｒｅｆｒｉｇｅｒａｔｏｒｉｎｐｕｔｐｏｗｅｒＰꎬ ｓｙｓｔｅｍｅｘｅｒｇｙｏｕｔｐｕｔｒａｔｅＡꎬ ｓｙｓｔｅｍｉｒｒｅｖｅｒｓｉｂｌｅｌｏｓｓｒａｔｅＱꎬ ｃｏｏｌｉｎｇｒａｔｅＲ. Ａｎｄｔｈｅｏｐｔｉｍａｌｐｅｒｆｏｒｍａｎｃｅｏｆｔｈｅｓｙｓｔｅｍｂｙｕｓｉｎｇｔｈｅｌｉｎｅａｒｗｅｉｇｈｔｅｄｅｖａｌｕａｔｉｏｎｆｕｎｃｔｉｏｎｍｅｔｈｏｄ. Ｔｈｅｒｅｓｕｌｔｓｓｈｏｗｔｈｅｏｐｔｉｍｉｚａｔｉｏｎｒｅｌａｔｉｏｎｓｈｉｐｂｅｔｗｅｅｎｍｕｌｔｉ￣ｏｂｊｅｃｔｉｖｅｆｕｎｃｔｉｏｎａｎｄｅａｃｈｐａｒａｍｅｔｅｒꎬ ａｎｄｃｏｎｃｌｕｄｅｔｈａｔｍｕｌｔｉ￣ｏｂｊｅｃｔｉｖｅｏｐｔｉｍｉｚａｔｉｏｎｃａｎｂｅｔｔｅｒｃｏｏｒｄｉｎａｔｅｔｈｅｒｅｌａｔｉｏｎｓｈｉｐｏｆｅａｃｈｐｅｒｆｏｒｍａｎｃｅｉｎｄｅｘ.
ＪＩＡＮＧＺｈｉｊｉｅ１ꎬ２ ꎬＷＵＦｅｎｇ２ ꎬＺＨＡＮＧＣｈａｏｍｉｎｇ１ꎬ２ ꎬＬＩＭｅｎｇ２ ꎬＹＵＮＨｕｉｍｉｎ２
(１. ＳｃｈｏｏｌｏｆＭｅｃｈａｎｉｃａｌａｎｄＥｌｅｃｔｒｉｃａｌＥｎｇｉｎｅｅｒｉｎｇꎬ ＷｕｈａｎＩｎｓｔｉｔｕｔｅｏｆＴｅｃｈｎｏｌｏｇｙꎬ Ｗｕｈａｎ４３００７３ꎬＣｈｉｎａꎻ ２. ＳｃｈｏｏｌｏｆＯｐｔｏｅｌｅｃｔｒｏｎｉｃＩｎｆｏｒｍａｔｉｏｎａｎｄＥｎｅｒｇｙＥｎｇｉｎｅｅｒｉｎｇꎬ ＷｕｈａｎＩｎｓｔｉｔｕｔｅｏｆＴｅｃｈｎｏｌｏｇｙꎬ Ｗｕｈａｎ４３００７３ꎬＣｈｉｎａ)
关键词:热声制冷机ꎻ复指数传热ꎻ有限时间热力学ꎻ多目标优化中图分类号:ＴＫ１１文献标志码:ＡＤＯＩ:１０.３９６９ / ｊ.ｉｓｓｎ.１０００￣２３７５.２０１９.０１.０１４
Ｐｅｒｆｏｒｍａｎｃｅｏｐｔｉｍｉｚａｔｉｏｎｏｆｔｈｅｒｍｏａｃｏｕｓｔｉｃｒｅｆｒｉｇｅｒａｔｏｒｂａｓｅｄｏｎｍｕｌｔｉ￣ｏｂｊｅｃｔｉｖｅｆｕｎｃｔｉｏｎ
分功率. 引入热漏ｑ、不可逆因子 φꎬ得出实际的放热率和吸热率: Ｑ̇ Ｈ＝ＱＨＣ－ｑＱ̇ Ｌ＝ＱＬＣ－ｑ ϕ ＝ＱＨＣ / Ｑ′ＨＣ ≥１在有热阻、热漏不可逆循环下的热声制冷机放热率
ห้องสมุดไป่ตู้
和吸热率可表示为:
收稿日期:２０１８０４２７基金项目:国家自然科学基金(５１１７６１４３) 资助作者简介:蒋智杰(１９９２ ) ꎬ男ꎬ硕士生ꎬＥ￣ｍａｉｌ:１１１９５３１６８２＠ｑｑ. ｃｏｍ
７４
湖北大学学报( 自然科学版)
第４１卷
１热声制冷机热力学循环模型的建立
在热声制冷机中ꎬ假设工质在传热过程中与高低温换热时平均温度ＴＨＯ、ＴＬＯ与换热器两端温度有
ＴＨＯ > ＴＨ > ＴＬ > ＴＬＯ的关系ꎬｘ＝ＴＬＯ / ＴＨＯ且考虑工质传热中热漏率、不可逆因子对制冷机的影响ꎬ建立在复指数传热规律下不可逆热声制冷机循环模型( 如图１) ꎬ参照文献[５￣６] 由于实际工质与高低温热源传
热中不仅存在热阻、热漏ꎬ还有其他一些不可逆过程ꎬ使得热声制冷机比仅有热阻的制冷机多输入一部
第４１卷第１期２０１９年１月
湖北大学学报( 自然科学版) ＪｏｕｒｎａｌｏｆＨｕｂｅｉＵｎｉｖｅｒｓｉｔｙ( ＮａｔｕｒａｌＳｃｉｅｎｃｅ)
文章编号:１０００２３７５(２０１９)０１００７３０４
Ｖｏｌ. ４１Ｎｏ. １Ｊａｎ. ꎬ２０１９
基于多目标函数的热声制冷机性能优化