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A platform for research: civil engineering, architecture and urbanism
Design Optimization of a Plate‐Fin Heat Exchanger With Metaheuristic Hybrid Algorithm
https://orcid.org/0009-0006-4192-8552
ABSTRACTThis paper introduces a novel approach to enhance the heat transfer efficiency of a plate‐fin heat exchanger. The metaheuristic hybrid approach combines particle swarm optimization (PSO) with the genetic algorithm (GA). Seven critical design parameters are used as variables. The research demonstrates the effectiveness of this hybrid method through a case study based on existing literature. The numerical results show the superior performance of the hybrid genetic algorithm particle swarm optimization over conventional GA and PSO techniques. The hybrid method achieves an optimal configuration with increased accuracy in a shorter computational time, offering significant time and cost savings in the design process.
Design Optimization of a Plate‐Fin Heat Exchanger With Metaheuristic Hybrid Algorithm
https://orcid.org/0009-0006-4192-8552
ABSTRACTThis paper introduces a novel approach to enhance the heat transfer efficiency of a plate‐fin heat exchanger. The metaheuristic hybrid approach combines particle swarm optimization (PSO) with the genetic algorithm (GA). Seven critical design parameters are used as variables. The research demonstrates the effectiveness of this hybrid method through a case study based on existing literature. The numerical results show the superior performance of the hybrid genetic algorithm particle swarm optimization over conventional GA and PSO techniques. The hybrid method achieves an optimal configuration with increased accuracy in a shorter computational time, offering significant time and cost savings in the design process.
Design Optimization of a Plate‐Fin Heat Exchanger With Metaheuristic Hybrid Algorithm
https://orcid.org/0009-0006-4192-8552
ABSTRACTThis paper introduces a novel approach to enhance the heat transfer efficiency of a plate‐fin heat exchanger. The metaheuristic hybrid approach combines particle swarm optimization (PSO) with the genetic algorithm (GA). Seven critical design parameters are used as variables. The research demonstrates the effectiveness of this hybrid method through a case study based on existing literature. The numerical results show the superior performance of the hybrid genetic algorithm particle swarm optimization over conventional GA and PSO techniques. The hybrid method achieves an optimal configuration with increased accuracy in a shorter computational time, offering significant time and cost savings in the design process.
Design Optimization of a Plate‐Fin Heat Exchanger With Metaheuristic Hybrid Algorithm
Heat Trans
Pachpute, Santosh L. (author) / More, Kiran C. (author)
Heat Transfer ; 54 ; 1163-1172
2025-03-01
Article (Journal)
Electronic Resource
English
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