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

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.