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Multi-objective optimization design of lithium-ion battery liquid cooling plate with double-layered dendritic channels

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APPLIED THERMAL ENGINEERING 2021年 199卷 117541-117541页

作者： Fan, Yiwei Wang, Zhaohui Fu, Ting Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

To study simple and effective liquid cooling methods for electric vehicle lithium-ion battery, a novel doublelayered dendritic channels liquid cooling system was proposed based on the constructal theory, which included the heat transfer layer channel and the collecting layer channel. The trade-off between objective functions (pressure drop, surface standard deviation, and maximum temperature) were achieved by the volume fraction of cooling liquid as the constraint condition. At the same time, the cooling plate structure values of the design points (total number of branching levels, length recursion factor, height to width ratio and channel thickness ratio) were obtained by Latin hypercube sampling. And the Radial Basis Function (RBF) surrogate model was adopted to establish the relationship between the objective function and the design variables. On this basis, the multi-objective optimization design of the nsga-ii algorithm was carried out. Finally, the optimization results were verified by numerical simulation, and the performance was compared with that of typical channels (serpentine and parallel). The results reported that this optimized design can reduce the maximum temperature from 52.59 degrees C to 39.3 degrees C, and the standard deviation of surface temperature from 5.31 degrees C to 1.96 degrees C. And the pressure drop from 518.6 Pa to 136.5 Pa. These results can help the design of cooling system with lower temperature and low pump power.

关键词： Double-layered Dendritic channels Constructal theory Cooling plate nsga-ii algorithm

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Multi-objective optimization for an irreversible Braysson cycle

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2024年第8期149卷 3471-3485页

作者： Liu, Xu Ge, Yanlin Chen, Lingen Shi, Shuangshuang Feng, Huijun Wuhan Inst Technol Inst Thermal Sci & Power Engn Wuhan 430205 Peoples R China Hubei Prov Engn Technol Res Ctr Green Chem Equipme Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China

Based on the irreversible closed Braysson cycle model constructed by predecessors which included the heat leakage loss, internal irreversibility loss and thermal resistance loss and finite-time thermodynamics, nsga-ii algorithm is applied to conduct multi-objective optimization for the dimensionless ecological function (E over bar \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{E}$$\end{document}), efficiency (eta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eta$$\end{document}), dimensionless power density (P over bar d\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{P}_{{\text{d}}}$$\end{document}) and dimensionless power output (P over bar \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{P}$$\end{document}) of different objective combinations with logarithmic temperature ratio (x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document}) of working fluid and heat conductance distribution (u\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage

关键词： Multi-objective optimization Heat conductance distribution Logarithmic temperature ratio Finite time thermodynamics Irreversible Braysson cycle nsga-ii algorithm

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Multi-objective economic-statistical design of simple linear profiles using a combination of nsga-ii, RSM, and TOPSIS

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2022年第4期51卷 1704-1720页

作者： Ershadi, M. J. Roshanbin, N. Niaki, S. T. A. Iranian Res Inst Informat Sci & Technol IRANDOC Informat Technol Dept Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Ind Engn Tehran Iran Sharif Univ Technol Dept Ind Engn Tehran Iran

A multi-objective economic-statistical design is aimed in this article for simple linear profiles. In this design, the interval between two successive sampling intervals, the sample size and the number of adjustment points alongside, the parameters of the monitoring scheme are determined such that not only the implementation cost is minimized, but also the profile exhibits desired statistical performances. To this aim, three objective functions are considered in the multi-objective optimization model of the problem. The Lorenzen-Vance cost function is used to model the implementation cost as the first objective function to be minimized. The second objective function maximizes the in-control average run length of the monitoring scheme (ARL(0)), while the third objective function minimizes the out-of-control average run length (ARL(1)). In addition, a lower bound is defined in a constraint for ARL(0) and an upper bound is determined in another constraint for ARL(1). The complex multi-objective optimization problem is solved by an nsga-ii algorithm, whose parameters are tuned using response surface methodology (RSM). A numerical illustration is solved, for which the Pareto optimal solutions are ranked by a multi-criteria decision-making method called Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Finally, sensitivity analyses are conducted on the parameters of the cost function, based on which the time required to sample and create a profile is the most important factor.

关键词： Simple linear profiles economic-statistical design nsga-ii algorithm TOPSIS RSM

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Neumann-Michell theory-based multi-objective optimization of hull form for a naval surface combatant

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APPLIED OCEAN RESEARCH 2017年 63卷 129-141页

作者： Wu, Jianwei Liu, Xiaoyi Zhao, Min Wan, Decheng Shanghai Jiao Tong Univ Collaborat Innovat Ctr Adv Ship & Deep Sea Explor Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China

A numerical multi-objective optimization procedure is proposed here to describe the development and application of a practical hydrodynamic optimization tool, OPTShip-SJTU. Three components including hull form modification module, hydrodynamic performance evaluation module and optimization module consist of this tool. The free-form deformation (FFD) method and shifting method are utilized as parametric hull surface modification techniques to generate a series of realistic hull forms subjected to geometric constraints, and the Neumann-Michell (NM) theory is implemented to predict the wave drag. Moreover, nsga-ii, a muti-objective genetic algorithm, is adopted to produce pareto-optimal front, and kriging model is used for predicting the total resistance during the optimization process to reduce the computational cost. Additionally, the analysis of variance (ANOVA) method is introduced to represent the influence of each design variable on the objective functions. In present work, a surface combatant DTMB Model 5415 is used as the initial design, and optimal solutions with obvious drag reductions at specific speeds are obtained. Eventually, three of optimal hulls are analyzed by NM theory and a RANS-based CFD solver naoe-FOAM-SJTU respectively. Numerical results confirm the availability and reliability of this multi-objective optimization tool. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： OPTShip-SJTU Multi-objective optimization nsga-ii algorithm Neumann-Michell theory Wave drag

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Multi-objective optimization of rolling schedule for tandem cold strip rolling based on nsga-ii

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JOURNAL OF MANUFACTURING PROCESSES 2020年 60卷 257-267页

作者： Wang, Yu Li, Changsheng Jin, Xin Xiang, Yongguang Li, Xiaogang Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China HBIS Grp Tangsteel Co Tangshan 063012 Peoples R China

A multi-objective optimization model of rolling schedule based on nsga-ii algorithm was presented, and three objective functions are defined as motor power balance, good flatness and preventing slippage. The experimental results show the excellent performance of nsga-ii in obtaining non-dominated front which could suggest several alternative solutions to rolling schedule in a single run of the algorithm. Additionally, considering that the rolling load distribution strategy dynamically adjusts are required to balance the impact of roll surface condition changes on the rolling process, a model of weighted-sum approach with variable weight coefficients based on rolling length was proposed. In this model, a roll change cycle was divided into four optimization stages which are given different weight coefficients. Moreover, a smooth correction model of weight coefficient was proposed to ensure the quality of strip flatness. Compared to the traditional weighted-sum approach, the presented model on the basis of variable weight coefficients can make full use of Pareto set, and has better adaptability to changing rolling conditions. Finally, the proposed model was applied to the multi-objective optimization model above, the results showed that the final optimal solutions have better performance in almost all objective dimensions than the empirical one. The practical application has further confirmed the effectiveness of the multi objective optimization for rolling schedule.

关键词： Tandem cold rolling Rolling schedule Multi-objective optimization Smooth correction model nsga-ii algorithm

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A quantitative model and solution of reaction heat for microfluidic chips based on Kriging and nsga-ii

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THERMOCHIMICA ACTA 2020年 694卷 178803-178803页

作者： Gan, Yi Yang, Minghao Sun, FuJia Yang, Lihong Univ Shanghai Sci & Technol Sch Mech Engn Shanghai Peoples R China Chuo Univ Fac Sci & Engn Tokyo Japan

Microfluidic chips have been widely used in microfluidic calorimeters. The thermoelectric power can be obtained by measuring the laminar flow peak value of the reaction liquid. Due to heat dissipation and heat inhomogeneity, thermodynamic parameters can not accurately describe the thermal state and thermal properties. With the inlet velocity and velocity ratio in Y-channel as the two key parameters affecting the peak value of thermoelectric power, the reaction heat governing equation is established. The Kriging method is used to establish the reaction heat quantification model and Nondominated Sorting Genetic algorithm (nsga-ii) is used in global optimization. Then, the Pareto solution set of optimal inlet velocity and optimal velocity ratio is obtained. Finally, the established reaction heat governing model was experimentally verified;The predicted values of the Kriging quantitative model were consistent with the experimental results;The three sets of Pareto solutions can accurately describe the thermal state and thermal properties.

关键词： nsga-ii algorithm Kriging (KRG) model Microfluidic chip Chemical reaction heat Multi-objective optimization

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Optimization of cutting parameters considering tool wear conditions in low-carbon manufacturing environment

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JOURNAL OF CLEANER PRODUCTION 2019年第Jul.20期226卷 706-719页

作者： Tian, Changle Zhou, Guanghui Zhang, Junjie Zhang, Chao Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Shaanxi Peoples R China

Carbon emissions have drawn widely attention due to the worsening climate changes. In a machining process, a reasonable selection of cutting parameters can not only save the production cost and time, but also reduce the production carbon emissions. In addition, the wear conditions of different cutting tools also have great influence on cutting parameters selection as they could cause huge difference on carbon emissions. However, in traditional optimization methods of cutting parameters, the tool wear conditions are always ignored. Thus, in order to overcome this limitation, an optimization method of cutting parameters considering the tool wear conditions is developed. Firstly, the quantified relationships among cutting parameters, tool wear and production indexes (production carbon emissions, cost and time) are analysed. Then, a multi-objective cutting parameters optimization model is established based on the above production indexes to determine the optimal cutting parameters and tools. Thirdly, a modified nsga-ii algorithm is used to resolve the proposed model. Finally, a case study is designed to demonstrate the advantages and feasibility of the proposed approach. The results show that (i) the optimal cutting parameters change with the tool wear conditions;(ii) For the same type of cutting tools with different wear conditions, the optimal values of production carbon emissions, cost and time increase along with the raise of the tool wear conditions;(iii) For different available cutting tools with different tool wear conditions, it is necessary to apply a multi-objective optimization method to decide the optimal production carbon emissions, cost and time. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Low-carbon manufacturing Cutting parameters optimization Tool wear condition nsga-ii algorithm Game theory

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Multi-objective optimized operation of integrated energy system with hydrogen storage

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2019年第56期44卷 29409-29417页

作者： Fang Ruiming Huaqiao Univ Dept Elect Engn Xiamen 361021 Fujian Peoples R China

In this paper, an integrated energy system (IES) consisting of wind turbine unit, photovoltaic cell unit, electrolytic hydrogen unit, fuel cell unit, and hydrogen storage unit is proposed, and the construction of multi objectives for day-ahead power dispatching of the IES considering both operation and environment cost is discussed. By adopting piecewise linearization method, the optimization variables are divided into 24 periods, and the day-ahead power dispatching optimization problem is transformed into a 24-h piecewise optimization problem. On the basis, a complete non-linear mixed integer dynamic scheduling optimization model is established. An improved non-dominated sorting genetic algorithm (nsga-ii) is applied to solving the model. In optimization process, an interactive strategy is adopted to solve the coordination between discretization of variables and restriction of switching times of electrolyzer. Optimization results show that, compared with the single objective of minimizing operating costs, the multi-objective optimization scheme can reduce carbon emissions by 3.5% with 2.8% increase of operating cost. Compared with the single objective of minimizing environmental, the multi-objective optimization scheme can reduce operating cost carbon by 5.12% with 2.6% increase of environmental cost. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Integrated energy system Multi-energy complement Multi-objective optimization Energy management nsga-ii algorithm

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A multi-objective emergency vehicle scheduling optimisation model

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2020年第4期34卷 236-243页

作者： Yao, Jiao Shao, Chuwei Xia, Xiaomei Wang, Pincheng Wei, Yu Wang, Jin Univ Shanghai Sci & Technol Business Sch 334 Jungong Rd Shanghai Peoples R China Changsha Univ Sci & Technol Sch Comp & Commun Engn 960 South Rd Wangjiali Changsha 410114 Peoples R China

Various types of emergencies incidents occur frequently, which may cause casualties and huge economic losses. How to optimise the scheduling of emergency rescue vehicles and improve their rescue efficiency is of great significance. Based on the analysis of its influencing factors the emergency vehicle scheduling optimisation model was firstly established, which was oriented to achieve three objects, minimising total travel time, total cost of dispatching travel, and maximising the path reliability. Moreover, the model was solved by Non-dominated Sorting Genetic algorithm with elite strategy (nsga-ii algorithm). Finally, the local road network in Huangpu District, Shanghai, China was taken as to verify the model in this study. The results show that the optimal solution of the model can achieve the optimal total travel time, and total travel cost and path reliability of the scheduling scheme can also satisfy requirements, which means good validity and practicability.

关键词： emergency vehicle scheduling optimisation model multi-objective planning nsga-ii algorithm effectiveness coefficient comprehensive evaluation method influencing factors travel time cost of scheduling path reliability

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Predicting the Water Inflow Into the Dam Reservoir Using the Hybrid Intelligent GP-ANN- nsga-ii Method

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WATER RESOURCES MANAGEMENT 2024年第11期38卷 4137-4159页

作者： Moeini, Ramtin Nasiri, Kamran Hosseini, Seyed Hossein Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

A key issue for effective management and operating of dam reservoirs is predicting the water inflow values into dam reservoir. To address this subject, here, genetic programming (GP) is used by proposing two cases. In the first case, water inflow values are predicted separately for each month. However, in the second case, these values are predicted simultaneously for all months. Furthermore, for each case, two approaches are proposed here. In the first approach, the hybrid method, called the ANN-NGSA-ii method, is proposed to find proper input data sets. However, in the second approach, the useful input data sets are found automatically using the GP method. For comparison purpose, the ANN and SARIMA models are also used, to predict the water inflow values. As a case study, in this research, the Zayandehroud dam reservoir is selected. The results indicate that the ANN model outperforms both results of the GP and SARIMA methods. In other words, correlation coefficient (R2), Nash Sutcliffe (NS), and root means square error (RMSE) values of ANN are 0.97, (0.88), 0.954 (0.87), and 17.19 (30.54) million cubic meters, respectively, for training (test) data set.

关键词： Water inflow prediction Genetic programming Artificial neural network nsga-ii algorithm Zayandehroud dam

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