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Geometric optimization of two-stage thermoelectric generator using genetic algorithms and thermodynamic analysis

ENERGY 2019年 171卷 37-48页

作者： Sun, Henan Ge, Ya Liu, Wei Liu, Zhichun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

multi-objective genetic algorithms are used to optimize the structure, assignment of configuration and load resistance of a two-stage thermoelectric generator, where Skutterudite and Bi2Te3 are chosen as upper stage and lower stage TE leg materials, respectively. Heat convection and radiation are considered on the top of the upper substrate. In the optimization process, the specific power and entropy generation rate are considered synchronously as objective functions to maximize the power output per unit area and to minimize the irreversibilities. The FEM is adopted in the simulation model, and the Seebeck effect, together with the Peltier effect, Joule heating, Thomson effect, and Fourier heat conduction phenomena are all considered in the simulation process. Shannon's entropy method is applied to select the best solution from the Pareto Frontier. Besides, the exergy destruction rate is analyzed, the results show that the exergy destruction rate increases as the load resistance increases. In addition, the different relationships between the load resistance and the voltage, power output, efficiency and entropy generation rate are presented. The principle of performance enhancement is also explained by comparing the ZT value along the TE legs. The optimization is important to the development of more compact and high-efficiency thermoelectric generators. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Thermoelectric generator multi-objective genetic algorithm Specific power Entropy generation rate Exergy destruction rate

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An Interactive Evaluation Method of Decentralized Procurement Plan by multi-objective genetic algorithm

An Interactive Evaluation Method of Decentralized Procuremen...

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International Conference on Data Communication Networking

作者： Ryoji Kobayashi Masakatsu Mori Masaki Samejima Norihisa Komoda Graduate School of Information Science and Technology Osaka University Yokohama Research Laboratory Hitachi Ltd.

ISBN: (纸本)9789898565723

This paper addresses evaluation of a decentralized procurement plan for the support of the discussion among decision-makers with considering a catastrophic disaster. For the evaluation of the decentralized procurement plan, we have formulated the decentralized procurement planning problem as 3-objective optimization problem. However, multiple-objective genetic algorithms (MOGA) to solve the problem take several minutes and display many Pareto solutions. We propose the interactive evaluation method of the decentralized procurement plan that is an expanded interactive MOGA (iMOGA) with loss evaluation simulator and solution selection by characteristics of the decentralized procurement plan. Experimental results show that the proposed method can allow the decision-makers to find their preference solutions with 38% fewer interactions than the basic iMOGA can.

关键词： Supply Chain Decentralized Procurement Plan multi-objective genetic algorithm Interactive Evaluation

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Performance of Supercritical CO2 Power Cycle and Its Turbomachinery with the Printed Circuit Heat Exchanger with Straight and Zigzag Channels

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ENERGIES 2021年第1期14卷 62页

作者： Saeed, Muhammed Alawadi, Khaled Kim, Sung Chul Khalifa Univ Sci & Technol Dept Mech Engn POB 127788 Abu Dhabi U Arab Emirates Publ Author Appl Educ & Trainings Dept Automot & Marine Technol Kuwait 70654 Kuwait Yeungnam Univ Sch Mech Engn 280 Daehak Ro Gyongsan 38541 South Korea

Since printed circuit heat exchangers (PCHE) are the largest modules of a supercritical carbon dioxide Brayton cycle, they can considerably affect the whole system's performance and layout. Straight-channel and zigzag-channel printed circuit heat exchangers have frequently been analyzed in the standalone mode and repeatedly proposed for sCO2-BC. However, the impact of heat exchanger designs with straight and zigzag-channel configurations on the performance of the cycle and its components, i.e., the turbine and compressor, has not been studied. In this context, this study evaluates the effect of different heat exchanger designs with various values of effectiveness (epsilon), inlet Reynolds number (Re), and channel configuration (zigzag and straight channel) on the overall performance of the sCO2-BC and its components. For the design and analysis of PCHEs, an in-house PCHE design and analysis code (PCHE-DAC) was developed in the MATLAB environment. The sCO2-BC performance was evaluated utilizing an in-house cycle simulation and analysis code (CSAC) that employs the heat exchanger design code as a subroutine. The results suggest that pressure drop in PCHEs with straight-channel configuration is up to 3.0 times larger than in PCHEs with zigzag-channel configuration. It was found that a higher pressure drop in the PCHEs with straight channels can be attributed to substantially longer channel lengths required for these designs (up to 4.1 times than zigzag-channels) based on the poor heat transfer characteristics associated with these channel geometries. Thus, cycle layouts using PCHEs with a straight-channel configuration impart a much higher load (up to 1.13 times) on the recompression compressor, this in turn, results in a lower pressure ratio across the turbine. Therefore, the overall performance of the sCO2-BC using PCHEs with straight-channel configurations is found to be substantially inferior to that of layouts using PCHEs with zigzag-channel configurations. Finall

关键词： supercritical carbon dioxide cycle simulation multi-objective genetic algorithm heat exchanger optimization sCO(2)-Brayton cycle

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Turbine Design and Optimization for a Supercritical CO2 Cycle Using a multifaceted Approach Based on Deep Neural Network

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ENERGIES 2021年第22期14卷 7807页

作者： Saeed, Muhammad Berrouk, Abdallah S. Burhani, Burhani M. Alatyar, Ahmed M. Wahedi, Yasser F. Al Khalifa Univ Sci & Technol Mech Engn Dept POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Ctr Catalysis & Separat CeCas POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Aerosp Engn Dept POB 127788 Abu Dhabi U Arab Emirates Abu Dhabi Maritime Acad POB 54477 Abu Dhabi U Arab Emirates

Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO(2)-BC). At the same time, the turbine design and optimization process for the sCO(2)-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO(2) power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST's Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (r(s4)/r(3)), hub ratio (r(s4)/r(3)), speed ratio (?s) and inlet flow angle (a(3)) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine's rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO(2)-turbine performance parameters are most sensitive to the design parameter speed ratio (?s), followed by inlet flow angle (a(3)), and are least receptive to shroud ratio (r(s4)/r(3)). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design a

关键词： turbine design supercritical CO2 artificial neural network optimization multi-objective genetic algorithm machine learning

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A multi-objective genetic algorithm Based on Simulated Annealing

A Multi-objective Genetic Algorithm Based on Simulated Annea...

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4th International Conference on multimedia Information Networking and Security (MINES)

作者： Tang Xin-hua Chang Xu Fang Zhi-feng Shandong Univ Polit Sci & Law Dept Informat Sci & Technol Jinan Peoples R China

ISBN: (纸本)9780769548524;9781467330930

Combined the characteristic of simulated annealing, we propose a multi-objective genetic algorithm based on simulated annealing. We take the advantage of simulated annealing, improve the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm. Experimental results show that the improved algorithm improve the solution efficiency of the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm effectively.

关键词： Simulated annealing algorithm multi-objective genetic algorithm genetic algorithm non-inferior solution

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Hybrid Evolutionary Framework for Selection of Genes Predicting Breast Cancer Relapse

Hybrid Evolutionary Framework for Selection of Genes Predict...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Perino, Lorenzo Cascianelli, Silvia Masseroli, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781728169262

Predicting relapse events is still one of the major challenges for breast cancer research. Despite gene expression-based classifiers may tackle this task, working on thousands of genes and only few samples jeopardizes the performances of a classifier trained without a proper gene selection. We propose a novel hybrid evolutionary gene selection framework, which uses a multi-objective genetic algorithm (MOGA) to search a wider range of gene selections and handles MOGA results in a whole new way, so as to overcome the limit of the non-easy interpretability of the MOGA broad set of solutions. To a classifier our framework provides a gene signature not only bringing the best cross-validation result, but also having noteworthy and robust performances when tested on unseen samples of an hold-out set. The robustness in hold-out showed the strength of our innovative key element: the final module of the framework, which fully exploits the high variability of MOGA outputs, rather than choosing just one of the solutions, as commonly done in the literature. It combines all MOGA results in more robust and compact gene occurrence-based signatures, under the reasonable assumption that highly recurrent genes have a more crucial biological role, more suitable clinical application and good discriminative power between relapsed and relapse-free patients, as confirmed by the obtained classification results.

关键词： Breast cancer relapse Gene selection Hybrid feature selection multi-objective genetic algorithm

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Design and Optimization of a Novel Dual-Stator Flux-Switching Permanent Magnet Machine

Design and Optimization of a Novel Dual-Stator Flux-Switchin...

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3rd IEEE Student Conference on Electrical Machines and Systems (SCEMS)

作者： Wang, Yongji Zhao, Wenliang Yu, Mingjie Yang, Yubo Kwon, Byung-il Shandong Univ Sch Elect Engn Jinan Peoples R China Hanyang Univ Dept Elect Syst Engn Ansan South Korea

ISBN: (纸本)9781728156224

In this paper, a 24-slot/25-pol e dual-stator flux switching permanent magnet machine (DSFSPMM) is designed and optimized to improve electromagnetic performance by combining the spoke-type magnet configurations, phase-group concentrated coil windings and an unaligned arrangement of rotor teeth. The design concept and operation principle of the machine are introduced in detail. Due to the complex dualstator structure, the proposed DSFSPMM model is established and analyzed by using the finite element method software. To achieve the optimal structure size of the machine, the multiobjective genetic algorithm (MOGA) is carried out based on the single-parameter scanning. As a result, it shows that the optimized model exhibits high torque density, low torque ripple, and high efficiency.

关键词： finite element method flux switching permanent magnet machine multi-objective genetic algorithm

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Shape Optimization to Reduce Wind Pressure on the Surfaces of a Rectangular Building with Horizontal Limbs

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第1期65卷 134-149页

作者： Paul, Rajdip Dalui, Sujit Kumar Indian Inst Engn Sci & Technol Civil Engn Dept PO Bot Garden Howrah 711103 W Bengal India

The present study consists of shape optimization of a rectangular plan shaped tall building with horizontal limbs under wind attack, which would minimize the wind pressure on all the faces of the building model simultaneously. For the purpose, the external pressure coefficients on different faces of the building (C-pe) are selected as the objective functions. The position of the limbs and the wind incidence angle are taken as design variables. The design of experiment (DOE) is done using random sampling. The values of the objective functions are obtained by using Computational Fluid Dynamics method of simulated wind flow at each design point. The building model has a constant plan area 22500 mm(2). The length and velocity scales are taken as 1:300 and 1:5, respectively. The results are used to construct the surrogate models of the objective functions using Response Surface Approximation method. The optimization study is done using the multi-objective genetic algorithm. The building shapes corresponding to the Pareto optimal decision variables are shown. The function values corresponding to the decision variables are verified by further introducing a CFD study.

关键词： tall building wind pressure coefficient optimization multi-objective genetic algorithm pareto-optimal solutions Response Surface Approximation

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multi-objective optimum design for FS welded 7039 aluminium alloy considering weld quality issues

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MATERIALS TODAY COMMUNICATIONS 2021年 26卷

作者： Verma, Shubham Garg, Deepika Misra, Joy Prakash Batra, Usha Maharishi Markandeshwar Univ Mullana India GD Goenka Univ Gurugrarn India Natl Inst Technol Kurukshetra Haryana India BHU Indian Inst Technol Varanasi Uttar Pradesh India

The optimization techniques gain immense exposure as a potential methodology for attaining the optimal process parameters for manufacturing problems. The present study deals with optimizing approaches in analyzing and predicting the tensile behavior of friction stir welded AA7039. Rotational speed, welding speed, and tilt angle are used as input variables;ultimate tensile strength (UTS) and tensile elongation (TE) is observed as a response parameter. Central composite design (CCD) of response surface methodology (RSM) is used to perform the experiment. multi-objective genetic algorithm (MOGA) and HMOGA (Hybrid multi-objective genetic algorithm) using goal attainment method are employed to determine the optimal values of input parameters. It is observed that both techniques give better optimal process parameters for attaining maximum values of UTS and TE. Moreover, HMOGA provides better results than MOGA owing to the presence of consistency in the solution.

关键词： AA7039 friction stir welding ultimate tensile strength tensile elongation multi-objective genetic algorithm Hybrid multi-objective genetic algorithm

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Structural optimization of phase change material based heat exchanger for breathing air cooling

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APPLIED THERMAL ENGINEERING 2021年 191卷 116829-116829页

作者： Lv, You Huang, Yawen Duan, Qiyao Xiao, Jie Feng, Naiyi Zhu, Yuan Chen, Tinghan Zhou, Shaoxin Southern Univ Sci & Technol Sch Microelect Shenzhen 518055 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518005 Peoples R China Southern Univ Sci & Technol Sch Innovat & Entrepreneurship Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Key Lab Energy Convers & Storage Technol Minist Educ Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Engn Res Ctr Integrated Circuits Next Generat Com Minist Educ Shenzhen 518055 Peoples R China Peking Univ Sch Life Sci Beijing 100871 Peoples R China Yantai Nanshan Univ Coll Engn Yantai 265713 Peoples R China

The enormous amount of heat in fires can bring the inhaled air temperature to an extremely high level, which is fatal to the civilians. However, conventional rescue respirators are unable to control the breathing air temperature. In our previous work, a phase change material-based heat exchanger has been designed to cool down the inlet air from 500 K to a breathable 313 K and sustaining for more than 20 min. As a part of wearable respirator, its wearing comfort must be considered by minimizing its pressure drop and keeping it lightweight. Hence, the design is modified by proposing a cooling module with twin heat exchangers and the structural design of the heat exchangers is optimized by response surface methodology and multi-objective genetic algorithm. The optimized design can offer the required cooling performance with up to 65% lower breathing resistance and 30% lighter weight, which is beneficial for the further applications.

关键词： Heat exchanger Breathing air cooling Structure optimization Response surface method multi-objective genetic algorithm

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