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Multi-Objective Optimization of Swirling Impinging Air Jets with Genetic Algorithm and Weighted Sum Method

Sudipta Debnath1, Zahir Uddin Ahmed2, Muhammad Ikhlaq3,4,*, Md. Tanvir Khan5, Avneet Kaur6, Kuljeet Singh Grewal1
1 Future Urban and Energy Lab for Sustainability (FUEL-S), Faculty of Sustainable Design Engineering (FSDE), University of Prince Edward Island, Charlottetown, PE C1A4P3, Canada
2 Department of Mechanical Engineering, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh
3 School of Engineering, Newcastle University, Newcastle Upon Tyne, NE17RU, UK
4 Dyson Institute of Engineering and Technology, Tetbury Hill, Malmesbury, SN160RP, UK
5 Department of Mechanical and System Engineering, Okayama University, Okayama, 7008530, Japan
6 Faculty of Sustainable Design Engineering (FSDE), University of Prince Edward Island, Charlottetown, PE C1A4P3, Canada
* Corresponding Author: Muhammad Ikhlaq. Email: email

Frontiers in Heat and Mass Transfer https://doi.org/10.32604/fhmt.2024.059734

Received 15 October 2024; Accepted 09 December 2024; Published online 06 January 2025

Abstract

Impinging jet arrays are extensively used in numerous industrial operations, including the cooling of electronics, turbine blades, and other high-heat flux systems because of their superior heat transfer capabilities. Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution, which can lead to improved system performance and energy savings. This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system. The governing equations are resolved employing the commercial computational fluid dynamics (CFD) software ANSYS Fluent v17. The study focuses on four controlling parameters: Reynolds number (Re), swirl number (S), jet-to-jet separation distance (Z/D), and impingement height (H/D). The effects of these parameters on heat transfer and impingement pressure distribution are investigated. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Weighted Sum Method (WSM) are employed to optimize the controlling parameters for maximum cooling performance. The aim is to identify optimal design parameters and system configurations that enhance heat transfer efficiency while achieving a uniform impingement pressure distribution. These findings have practical implications for applications requiring efficient cooling. The optimized design achieved a 12.28% increase in convective heat transfer efficiency with a local Nusselt number of 113.05 compared to 100.69 in the reference design. Enhanced convective cooling and heat flux were observed in the optimized configuration, particularly in areas of direct jet impingement. Additionally, the optimized design maintained lower wall temperatures, demonstrating more effective thermal dissipation.

Graphical Abstract

Multi-Objective Optimization of Swirling Impinging Air Jets with Genetic Algorithm and Weighted Sum Method

Keywords

Jet impingement; multi-objective optimization; pareto front; NSGA-II; WSM
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