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Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

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  • Mohammad Owais Qidwai

    (Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, Okhla Campus-1, Okhla Industrial Estate, Phase-III, New Delhi 110020, India)

  • Irfan Anjum Badruddin

    (Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Noor Zaman Khan

    (Department of Mechanical Engineering, National Institute of Technology Srinagar, Hazratbal, Srinagar 190006, Jammu and Kashmir, India)

  • Mohammad Anas Khan

    (Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi 110025, India)

  • Saad Alshahrani

    (Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

Abstract

This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample points on which three-dimensional Computational Fluid Dynamics (CFD) solutions were calculated, which were used to train the machine learning model. Radial Basis Neural Network (RBNN) was used as a surrogate model coupled with Particle Swarm Optimization (PSO) to obtain the optimized location of jets. The RBNN provides continuous space for searching the optimum values. At the predicted optimum values from the coupled model, the CFD solution was calculated for comparison. The percentage error for the target function was 0.56%, whereas for the accompanied function it was 1.3%. The coupled algorithm has variable inputs at user discretion, including gaussian spread, number of search particles, and number of iterations. The sensitivity of each variable was obtained. Analysis of Variance (ANOVA) was performed to investigate the effect of the input variable on thermal resistance. ANOVA results revealed that gaussian spread is the dominant variable affecting the thermal resistance.

Suggested Citation

  • Mohammad Owais Qidwai & Irfan Anjum Badruddin & Noor Zaman Khan & Mohammad Anas Khan & Saad Alshahrani, 2021. "Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm," Mathematics, MDPI, vol. 9(17), pages 1-19, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2167-:d:629386
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    References listed on IDEAS

    as
    1. Khudhayr A. Rashedi & Mohd Tahir Ismail & Nawaf N. Hamadneh & S. AL Wadi & Jamil J. Jaber & Muhammad Tahir & Riaz Ahmad, 2021. "Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns," Journal of Mathematics, Hindawi, vol. 2021, pages 1-8, April.
    2. Abo-Zahhad, Essam M. & Ookawara, Shinichi & Radwan, Ali & El-Shazly, A.H. & Elkady, M.F., 2019. "Numerical analyses of hybrid jet impingement/microchannel cooling device for thermal management of high concentrator triple-junction solar cell," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Jaroslaw Krzywanski, 2019. "A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods," Energies, MDPI, vol. 12(23), pages 1-32, November.
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