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multi-objective optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition

ENERGIES 2024年第6期17卷 1334页

作者： Lyu, Renzhi Wang, Zhenpo Zhang, Zhaosheng Beijing Inst Technol Sch Mech Engn Beijing 100811 Peoples R China

Fuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a multi-objective energy management strategy that optimizes weight coefficients. On the basis of establishing a fuel cell battery hybrid system model, three modes of uniform speed, acceleration, and deceleration were identified through clustering analysis of vehicle speed. Reinforcement learning algorithms were used to learn the corresponding weights for different modes, which reduced the decline in fuel cell life while improving the economic efficiency. The simulation results indicate that, under the conditions of no load, half load, and full load, the truck only sacrificed 0.9-5.6%, 1.7-2.6%, and 1.2-1.6% SOC, saving 5.7-6.45%, 5.9-6.67%, and 6.1-6.67% in lifespan loss, and reducing hydrogen consumption by 3.0-7.1%, 2.8-4.4%, and 1.0-3.0%, respectively.

关键词： fuel cell hybrid electric trucks energy management strategy reinforcement learning multi-objective optimization

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multi-objective optimization of automotive power battery cooling plate structure using response surface methodology

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第11期38卷 6365-6374页

作者： Liu, Qingtong Sun, Qun Wang, Hao Cheng, Baixin Liaocheng Univ Sch Mech & Automot Engn Liaocheng 252000 Shandong Peoples R China

This study aims to investigate the multi-objective optimization method for liquid cooling plates in automotive power batteries. The response surface method and NSGA-II were combined to optimize the temperature of the battery system under liquid-cooled conditions and the internal pressure of the liquid-cooled plate. The optimal Latin hypercube sampling method was used for sampling, with the flow channel parameters of the liquid-cooled plate and the cooling fluid inlet flow rate as design variables and the maximum temperature of the battery system and the maximum internal pressure of the liquid-cooled plate as target functions. The response surface model was fitted, and the Pareto solution set for the target to be optimized was obtained using NSGA-II. The LINMAP decision-making algorithm was employed to obtain the optimal solution, which is a maximum temperature of 37.25 degrees C for the battery and a maximum pressure of 63.3 Pa for the liquid-cooled plate.

关键词： Automotive power battery cooling plate multi-objective optimization Response surface methodology NSGA-II Decision-making

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multi-objective optimization of office egg shadings using NSGA-II to save energy consumption and enhance thermal and visual comfort

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 157卷

作者： Yao, Bingying Salehi, Ali Baghoolizadeh, Mohammadreza Khairy, Yasmin Baghaei, Sh. Software Engn Inst Guangzhou Dept Software Engn Guangzhou 510000 Guangdong Peoples R China Thomas Jefferson Univ Coll Architecture & Built Environm East Falls Campus Philadelphia PA 19107 USA Shahrekord Univ Dept Mech Engn Shahrekord *** Iran King Khalid Univ Coll Sci Dept Phys Abha 61413 Saudi Arabia Islamic Azad Univ Dept Mech Engn Khomeinishahr Branch Khomeinishahr Iran

The construction industry's excessive energy usage necessitates practical solutions to address this pressing issue. Windows are crucial architectural elements as they allow the majority of natural light to enter a structure. The poorly designed window and related components cause the space to overheat, consuming more energy. However, it negatively impacts the building's thermal and visual comfort. The paper introduces a novel approach for optimizing control parameters for egg shadings through multi-objective methods. The recommended approach significantly reduces the building's energy consumption and enhances its thermal and visual comfort. EnergyPlus is a software utilized for conducting energy-related simulations. The JEPLUS software has therefore considered seven design variables, such as differences in the vertical and horizontal angles of the shade, length and horizontal dimensions, depth and vertical dimensions, and window positioning. These year-round simulations are performed in four different orientations for four different Iranian towns with various climates. The data is improved by using the program JEPLUS + EA. To find the best locations on the Pareto front, the data is optimized using the NSGA-II approach. The optimization results indicate that a lower angle improves visual comfort but takes more electricity for lighting. The findings of multi-objective optimization of controlled blind characteristics improved thermal and visual comfort with ranges of 5-45% and 55-100%, respectively, and lowered overall building energy consumption by 1-19% yearly, depending on the building's geographic orientation.

关键词： multi-objective optimization DGI PPD AEC Egg shading

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multi-objective optimization and sensitivity analysis of offset strip fin compact heat exchangers

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NUMERICAL HEAT TRANSFER PART B-FUNDAMENTALS 2024年

作者： Yildizeli, Alperen Ustun, Orkun Mert Mutlu, Kaan Cadirci, Sertac Istanbul Tech Univ Dept Mech Engn TR-34437 Istanbul Turkiye

In this study, the aerothermal performance of Offset Strip Fin Compact Heat Exchangers (OSF-CHE) is optimized with the multi-objective Particle Swarm optimization method. Available empirical correlations for the Fanning factor and Colburn factor are utilized in the optimization process. Selected off-design Pareto-optimal solutions for compact heat exchanger configurations are reconstructed and simulated by Computational Fluid Dynamics simulations at various boundary conditions after extensive validation processes. Optimized OSF-CHE designs are shown to have improved aerothermal performances among comparable designs in the literature. It has been shown that a high Reynolds number in the range of 1,500-2,000 as a flow attribute provides the Fanning factor ranging from 0.0228 to 0.0619 and the Colburn factor between 0.0067 and 0.0122 and Re is more dominant than geometric design variables on the objective functions. Low and moderate Reynolds number flows at 500 and 1,000 offer a wider selection range for both objective functions on the Pareto-front.

关键词： Computational fluid dynamics design optimization multi-objective optimization offset strip fin compact heat exchangers particle swarm optimization

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multi-objective optimization of process parameters for laser metal deposition of NiTi shape memory alloy based on neural network and genetic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第9-10期130卷 4663-4678页

作者： Gao, Jiali Wang, Xu Wang, Chi Hao, Yunbo Liang, Xudong Li, Weiqi Zhao, Kai Univ Shanghai Sci & Technol Coll Mech Engn Shanghai 200093 Peoples R China Shanghai Aerosp Equipments Manufacture Co Ltd Shanghai 200245 Peoples R China

Due to the poor machinability and weldability of nickel-titanium (NiTi) shape memory alloy, application of NiTi alloy components prepared by smelting technology has been limited in aerospace and other fields. Laser metal deposition (LMD) technology opens a new way for the fabrication of NiTi alloy components. However, deposition quality and deposition rate are significantly influenced by the employed LMD process parameters. In this work, a small-sample prediction and optimization model based on BP-GA neural network for LMD process parameters selection was developed to improve the deposition quality and deposition rate of NiTi alloy components. The initial step involved the design of a central composite experiments consisted of thirty small-sample single-track experiments and building of the prediction model for deposition quality and deposition rate, wherein the inputs were consisted of the process parameters such as the laser power, scanning speed, and powder feeding rate. The responses, on the other hand, included the microhardness, roughness, and deposition rate. Prediction of the single-track cladding results by regression models of the response surface methodology, the ML models of back propagation neural network and random forest algorithms were comparatively analyzed and the prediction model was established. Then, based on the prediction model, non-dominated sorting genetic algorithm-II algorithm was applied to optimize the three input process parameters with the multi-objective of maximizing microhardness and deposition rate, and minimizing roughness. And the optimal combination of process parameters was obtained as a laser power of 1292.14 W, a scanning speed of 8.79 mm/s and a powder feeding rate of 16.78 g/min. Finally, single-track deposition experiments under the optimal combination of process parameters were carried out. The results showed that microhardness of 267.01 (5.23% improvement) and roughness of 7.86 mu m (20.04% improvement) was achieved whil

关键词： Laser metal deposition Nickel-titanium alloys multi-objective optimization Genetic algorithms Neural network

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multi-objective optimization for microbial electrolysis cell-assisted anaerobic digestion of swine manure

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 77卷 545-556页

作者： Zou, Lifei Zhao, Xingling Wu, Kai Liang, Chengyue Liu, Jing Yang, Hong Wang, Changmei Yang, Bin Yin, Fang Zhang, Wudi Yunnan Normal Univ Kunming 650500 Peoples R China Minzu Normal Univ Xingyi Xingyi 562400 Peoples R China Yunnan Res Ctr Biogas Technol & Engn Kunming 650500 Peoples R China

As a new technology, microbial electrolysis cell-assisted anaerobic digestion (MEC-AD) has been applied to the utilization of swine manure. Methane production and total energy efficiency are both important indicators in MEC-AD;thus, it is necessary to simultaneously optimize methane production and total energy efficiency. In this study, the Box-Behnken design (BBD) was used as the experimental design, the response surface methodology (RSM) and back propagation neural network (BP-NN) methods were used to construct multi-objective models, and the non-dominated sorting genetic algorithm II (NSGA II) was used for multi-objective optimization. The BP- NN model was superior to the RSM model in terms of goodness of fit. Additionally, the relative error between the predicted and experimental values of the Pareto front was determined to be <3%. The maximum methane production and total energy efficiency were 333.97 mL/g total solid and 61.38%, respectively. Therefore, multi- objective optimization of MEC-AD systems may be accomplished using BBD-(BP-NN)-NSGA II.

关键词： Microbial electrolysis cell Box-Behnken design Response surface methodology Back propagation neural network Non-dominated sorting genetic algorithm II multi-objective optimization

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multi-objective optimization-based workflow scheduling for applications with data locality and deadline constraints in geo-distributed clouds

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 157卷 485-498页

作者： Wu, Dongkuo Wang, Xingwei Wang, Xueyi Huang, Min Zeng, Rongfei Yang, Kaiqi Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Software Shenyang 110819 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

Geo-distributed clouds have emerged as a new generation of cloud computing paradigm, in which each cloud is operated and managed by independent cloud service providers (CSPs). By enhancing cooperation among CSPs, it can offer efficient cross-cloud services. In geo-distributed clouds, the resources offered by CSPs are heterogeneous with different billing mechanisms and the data required by workflow applications are geographically distributed with locality characteristics. As such, it is significantly challenging for cloud users to select the appropriate resources to execute their workflow applications. In this paper, we model the constrained multi-objective workflow scheduling problem (CMWSP) in geo-distributed clouds as a constrained multiobjective optimization problem that minimizes both workflow makespan and resource rental costs. To solve the CMWSP, we propose a multi-objective multi-workflow scheduling mechanism (MOMWS), which integrates workflow preprocessing, evolutionary multi-objective optimization and intensification strategy while explicitly considering the data locality characteristics, deadline requirements, and rental period reuse. Specifically, we first design a task preprocessing algorithm for workflow applications to reduce transferred data volume by merging tasks with the same original datasets. Based on this algorithm, we introduce a priority assignment algorithm to decide the scheduling sequence of workflow applications. We next propose a makespan and costaware workflow scheduling algorithm to determine a set of high-quality approximations of the Pareto front to the CMWSP. Based on real -world CSPs and workflow applications, extensive experiments are carried out to demonstrate the effectiveness and efficiency of MOMWS.

关键词： Geo-distributed clouds Workflow scheduling multi-objective optimization Evolutionary algorithm

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multi-objective optimization and uncertainty quantification for inductors based on neural network

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2024年第4期43卷 890-903页

作者： Kong, Xiaohan Yin, Shuli Gong, Yunyi Igarashi, Hajime Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan

PurposeThe prolonged training time of the neural network (NN) has sparked considerable debate regarding their application in the field of optimization. The purpose of this paper is to explore the beneficial assistance of NN-based alternative models in inductance design, with a particular focus on multi-objective optimization and uncertainty analysis ***/methodology/approachUnder Gaussian-distributed manufacturing errors, this study predicts error intervals for Pareto points and select robust solutions with minimal error margins. Furthermore, this study establishes correlations between manufacturing errors and inductance value discrepancies, offering a practical means of determining permissible manufacturing errors tailored to varying accuracy *** NN-assisted methods are demonstrated to offer a substantial time advantage in multi-objective optimization compared to conventional approaches, particularly in scenarios where the trained NN is repeatedly used. Also, NN models allow for extensive data-driven uncertainty quantification, which is challenging for traditional ***/valueThree objectives including saturation current are considered in the multi-optimization, and the time advantages of the NN are thoroughly discussed by comparing scenarios involving single optimization, multiple optimizations, bi-objective optimization and tri-objective optimization. This study proposes direct error interval prediction on the Pareto front, using extensive data to predict the response of the Pareto front to random errors following a Gaussian distribution. This approach circumvents the compromises inherent in constrained robust optimization for inductance design and allows for a direct assessment of robustness that can be applied to account for manufacturing errors with complex distributions.

关键词： multi-objective optimization Surrogate optimization Uncertainties in electromagnetics

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multi-objective optimization of steam methane reformer in micro chemically recuperated gas turbine

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 62卷 307-320页

作者： Zhang, Haoqi Fan, Fengxian Huang, Diangui Han, Dong Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Shanghai Key Lab Multiphase Flow & Heat Transfer Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Key Lab Power Machinery & Engn Minist Educ Shanghai 200240 Peoples R China

A kinetic theory, known as the Langmuir-Hinshelwood-Hougen-Watson adsorption model, is applied to describe the steam methane reforming (SMR) in a 500 kW scale micro chemically recuperated gas turbine (CRGT) cycle. The response surface models of important performance parameters, including the methane conversion, carbon monoxide selectivity, and chemically recuperated heat as a function of the temperature, pressure, steam -to -carbon ratio, and contact time are numerically obtained based on the cases selected by the central composite design. The factors affecting the SMR performance are analyzed, and the reformer performance is optimized using both the desirability function combined with the response surface methodology and the second generation non -dominated sorting genetic algorithm. Finally, performance of the optimized reformer and electrical efficiency of the micro CRGT cycle with the reformer are evaluated. Results reveal that the efficiency of the micro CRGT is 40.70%, which is much higher than the typical high -efficiency micro gas trubine without reformer.

关键词： Steam methane reforming Reformer optimization Micro gas turbine Chemically recuperated gas turbine multi-objective optimization

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multi-objective optimization of a GPAF-BM for heat dissipation and crashworthiness based on an NHSMF approximate model

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STRUCTURAL AND multiDISCIPLINARY optimization 2024年第12期67卷 1-21页

作者： Wang, Peizhuo Qin, Dongchen Wang, Tingting Yang, Junjie Chen, Jiangyi Zhengzhou Univ Sch Mech & Power Engn Zhengzhou 450001 Peoples R China

Heat dissipation and collision safety are key factors affecting the safety of power battery systems. However, existing research on battery system safety only focuses on one aspect, with limited studies simultaneously addressing heat dissipation and collision safety. Therefore, this paper presents a gradient porosity aluminum foam battery module (GPAF-BM) with high heat dissipation efficiency and excellent crashworthiness by utilizing the enhanced heat transfer capability and good energy absorption characteristics of gradient open-cell aluminum foam (OCAF). To further improve its comprehensive performance, the heat dissipation efficiency and crashworthiness of the GPAF-BM are strictly optimized with multiple objectives. Additionally, to improve the efficiency of approximate model modeling, a new hybrid scaling multi-fidelity (NHSMF) approximate model construction method is proposed, which effectively improves the prediction accuracy of the model with a small sample size. Finally, the third-generation nondominant sorting genetic algorithm (NSGA-III) and normal boundary intersection (NBI) method are employed to ascertain the optimal solution. Compared to the original design, the optimized maximum intrusion displacement of the battery (Dmax) is 22.29% lower and the maximum battery temperature (Tmax) is reduced by 0.49 degrees C, the pressure drop of the (Delta P) decreased from the original design of 129.71 Pa to 115.53 Pa, a decrease of 10.93%. These improvements effectively enhance both the heat dissipation efficiency and crashworthiness of GPAF-BM.

关键词： Lithium-ion batteries Open-cell aluminum foam Heat dissipation Crashworthiness multi-objective optimization

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