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《能源前沿(英文)》 >> 2020年 第14卷 第3期 doi: 10.1007/s11708-018-0548-0

Multi-objective optimization in a finite time thermodynamic method for dish-Stirling by branch and bound method and MOPSO algorithm

. Department of Renewable Energies, Faculty of New Science and Technologies, University of Tehran 1417466191, Tehran, Iran.. Faculty of Mechanical Engineering and Mechatronic, Shahrood University of Technology, Shahrood 3619995161, Iran.. Faculty of Science and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Petaling Jaya, Malaysia; Department of Engineering, Lancaster University, Lancaster, LA1 4YW, UK.. School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China

录用日期: 2018-04-03 发布日期: 2018-04-03

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摘要

There are various analyses for a solar system with the dish-Stirling technology. One of those analyses is the finite time thermodynamic analysis by which the total power of the system can be obtained by calculating the process time. In this study, the convection and radiation heat transfer losses from collector surface, the conduction heat transfer between hot and cold cylinders, and cold side heat exchanger have been considered. During this investigation, four objective functions have been optimized simultaneously, including power, efficiency, entropy, and economic factors. In addition to the four-objective optimization, three-objective, two-objective, and single-objective optimizations have been done on the dish-Stirling model. The algorithm of multi-objective particle swarm optimization (MOPSO) with post-expression of preferences is used for multi-objective optimizations while the branch and bound algorithm with pre-expression of preferences is used for single-objective and multi-objective optimizations. In the case of multi-objective optimizations with post-expression of preferences, Pareto optimal front are obtained, afterward by implementing the fuzzy, LINMAP, and TOPSIS decision making algorithms, the single optimum results can be achieved. The comparison of the results shows the benefits of MOPSO in optimizing dish Stirling finite time thermodynamic equations.

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