Data-driven optimization of asymmetric curved winglets in fin-and-tube heat exchangers

Abstract

The drive to reduce energy consumption and enhance heat transfer capability makes the optimization of heat exchangers (HEs) crucial in modern thermal management systems. Recognizing the potential for significant energy savings, this study focuses on the novel asymmetrically placed curved winglets in fin-and-tube heat exchangers. A total of 2320 simulations, designed using a Latin hypercube sampling plan, have been performed. The dependent variables are calculated using computational fluid dynamics to train artificial neural networks that serve as a surrogate model for genetic algorithm (GA) to perform multi-objective optimization. The GA aims to maximize the enhancement factor (η) – a ratio of the Colburn and friction factors.

Since HEs operate over a range of Reynolds numbers (Re), all the previous optimization-based studies have been based on a single Re that raises several questions about the HE’s performance at off-design conditions (over a range of Re). Hence, the present optimization-based study considers three different (but fixed) Re values, followed by optimizing Re against each optimized winglet geometry. All the results are compared at design and off-design conditions. The cross-validation of the Pareto front points using CFD reveals a deviation of <5 %, indicating good predictive performance and consistency of the optimized datasets within the defined simulation framework. Compared to the baseline model without winglets, the optimized designs achieved η_max = 100.17 % and also outperformed under varied operating conditions. Hence, besides introducing a novel asymmetric design, this research provides guidelines on Re-based optimizations that could significantly improve HE performance in energy-dependent industries.