Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization
The low performance of the thermoacoustic refrigerator has made it uncompetitive to currently available refrigeration systems and hence its path towards commercialization is being restricted. Recently, evolutionary algorithm such as genetic algorithm has become popular among researchers in optimizin...
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my.utm.934822021-11-30T08:33:40Z http://eprints.utm.my/id/eprint/93482/ Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization It, Sing Chan Mohd. Ghazali, Normah Zolpakar, Nor Atiqah Mohamad, Maziah TJ Mechanical engineering and machinery The low performance of the thermoacoustic refrigerator has made it uncompetitive to currently available refrigeration systems and hence its path towards commercialization is being restricted. Recently, evolutionary algorithm such as genetic algorithm has become popular among researchers in optimizing the performance of the thermoacoustic refrigerator due to its capability to provide a solution with a global maximum or minimum through simultaneous optimization of several objectives. The purpose of this study was to maximize the performance of the thermoacoustic refrigerator using the Multi-Objective Particle Swarm Optimization (MOPSO), an evolutionary optimization tool that has not been tried in this field before. By optimizing the two conflicting objectives which are maximizing the cooling power and minimizing the acoustic power required, simultaneous optimization of inter-dependent controlling parameters has been performed for two, three and four parameters. Comparing with the results of past studies, MOPSO has improved the stack COP by 6.92% compared to the parametric optimization approach and 2.96% higher than the maximum COP achieved by multi-objective genetic algorithm (MOGA) with an optimum COP of 1.39. Also, a maximum cooling power of 10.8 W was obtained. This study has highlighted the potential of MOPSO in providing optimized conditions for conflicting objectives desired for a thermoacoustic system. World Scientific Publishing Co. Pte Ltd 2020-06-01 Article PeerReviewed It, Sing Chan and Mohd. Ghazali, Normah and Zolpakar, Nor Atiqah and Mohamad, Maziah (2020) Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization. International Journal of Air-Conditioning and Refrigeration, 28 (2). pp. 1-10. ISSN 2010-1325 http://dx.doi.org/10.1142/S2010132520500121 DOI:10.1142/S2010132520500121 |
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TJ Mechanical engineering and machinery It, Sing Chan Mohd. Ghazali, Normah Zolpakar, Nor Atiqah Mohamad, Maziah Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization |
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The low performance of the thermoacoustic refrigerator has made it uncompetitive to currently available refrigeration systems and hence its path towards commercialization is being restricted. Recently, evolutionary algorithm such as genetic algorithm has become popular among researchers in optimizing the performance of the thermoacoustic refrigerator due to its capability to provide a solution with a global maximum or minimum through simultaneous optimization of several objectives. The purpose of this study was to maximize the performance of the thermoacoustic refrigerator using the Multi-Objective Particle Swarm Optimization (MOPSO), an evolutionary optimization tool that has not been tried in this field before. By optimizing the two conflicting objectives which are maximizing the cooling power and minimizing the acoustic power required, simultaneous optimization of inter-dependent controlling parameters has been performed for two, three and four parameters. Comparing with the results of past studies, MOPSO has improved the stack COP by 6.92% compared to the parametric optimization approach and 2.96% higher than the maximum COP achieved by multi-objective genetic algorithm (MOGA) with an optimum COP of 1.39. Also, a maximum cooling power of 10.8 W was obtained. This study has highlighted the potential of MOPSO in providing optimized conditions for conflicting objectives desired for a thermoacoustic system. |
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It, Sing Chan Mohd. Ghazali, Normah Zolpakar, Nor Atiqah Mohamad, Maziah |
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It, Sing Chan Mohd. Ghazali, Normah Zolpakar, Nor Atiqah Mohamad, Maziah |
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It, Sing Chan |
title |
Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization |
title_short |
Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization |
title_full |
Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization |
title_fullStr |
Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization |
title_full_unstemmed |
Four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization |
title_sort |
four-variable simultaneous optimization of the cooling and acoustic power with particle swarm optimization |
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World Scientific Publishing Co. Pte Ltd |
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2020 |
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http://eprints.utm.my/id/eprint/93482/ http://dx.doi.org/10.1142/S2010132520500121 |
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