Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm
Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present...
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MDPI AG
2022-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/14/2527 |
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| author | Joo Hyun Moon Kyun Ho Lee Haedong Kim Dong In Han |
| author_facet | Joo Hyun Moon Kyun Ho Lee Haedong Kim Dong In Han |
| author_sort | Joo Hyun Moon |
| collection | DOAJ |
| description | Heat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present study is to propose an improved Gaussian quantum-behaved particle swarm optimization (GQPSO) algorithm for enhanced optimization performance and its verification through application to a multivariable thermal-economic optimization problem of a crossflow plate–fin heat exchanger (PFHE). Three single objective functions: the number of entropy generation units (<i>NEGUs</i>), total annual cost (<i>TAC</i>), and heat exchanger surface area (<i>A</i>), were minimized separately by evaluating optimal values of seven unknown variables using four different PSO-based methods. By comparing the obtained best fitness values, the improved GQPSO approach could search quickly for better global optimal solutions by preventing particles from falling to the local minimum due to its modified local attractor scheme based on the Gaussian distributed random numbers. For example, the proposed GQPSO could predict further improved best fitness values of 40% for <i>NEGUs</i>, 17% for <i>TAC</i>, and 4.5% for <i>A</i>, respectively. Consequently, the present study suggests that the improved GQPSO approach with the modified local attractor scheme can be efficient in rapidly finding more suitable solutions for optimizing the thermal-economic problem of the crossflow PFHE. |
| first_indexed | 2024-03-09T13:25:24Z |
| format | Article |
| id | doaj.art-7c538f65d867499d8069e286f72e4375 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T13:25:24Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-7c538f65d867499d8069e286f72e43752023-11-30T21:24:28ZengMDPI AGMathematics2227-73902022-07-011014252710.3390/math10142527Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm AlgorithmJoo Hyun Moon0Kyun Ho Lee1Haedong Kim2Dong In Han3Department of Aerospace Engineering, Sejong University, Seoul 05006, KoreaDepartment of Aerospace Engineering, Sejong University, Seoul 05006, KoreaDepartment of Aerospace Engineering, Sejong University, Seoul 05006, KoreaKorea Aerospace Research Institute, Daejeon 34133, KoreaHeat exchangers are usually designed using a sophisticated process of trial-and-error to find proper values of unknown parameters which satisfy given requirements. Recently, the design of heat exchangers using evolutionary optimization algorithms has received attention. The major aim of the present study is to propose an improved Gaussian quantum-behaved particle swarm optimization (GQPSO) algorithm for enhanced optimization performance and its verification through application to a multivariable thermal-economic optimization problem of a crossflow plate–fin heat exchanger (PFHE). Three single objective functions: the number of entropy generation units (<i>NEGUs</i>), total annual cost (<i>TAC</i>), and heat exchanger surface area (<i>A</i>), were minimized separately by evaluating optimal values of seven unknown variables using four different PSO-based methods. By comparing the obtained best fitness values, the improved GQPSO approach could search quickly for better global optimal solutions by preventing particles from falling to the local minimum due to its modified local attractor scheme based on the Gaussian distributed random numbers. For example, the proposed GQPSO could predict further improved best fitness values of 40% for <i>NEGUs</i>, 17% for <i>TAC</i>, and 4.5% for <i>A</i>, respectively. Consequently, the present study suggests that the improved GQPSO approach with the modified local attractor scheme can be efficient in rapidly finding more suitable solutions for optimizing the thermal-economic problem of the crossflow PFHE.https://www.mdpi.com/2227-7390/10/14/2527plate–fin heat exchangerthermal-economic optimizationimproved Gaussian quantum-behaved particle swarm optimizationmodified local attractor |
| spellingShingle | Joo Hyun Moon Kyun Ho Lee Haedong Kim Dong In Han Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm Mathematics plate–fin heat exchanger thermal-economic optimization improved Gaussian quantum-behaved particle swarm optimization modified local attractor |
| title | Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm |
| title_full | Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm |
| title_fullStr | Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm |
| title_full_unstemmed | Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm |
| title_short | Thermal-Economic Optimization of Plate–Fin Heat Exchanger Using Improved Gaussian Quantum-Behaved Particle Swarm Algorithm |
| title_sort | thermal economic optimization of plate fin heat exchanger using improved gaussian quantum behaved particle swarm algorithm |
| topic | plate–fin heat exchanger thermal-economic optimization improved Gaussian quantum-behaved particle swarm optimization modified local attractor |
| url | https://www.mdpi.com/2227-7390/10/14/2527 |
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