Characteristics prediction and optimization for fan duct surface heat exchanger using regional heat transfer correlation and NSWOA aided by Sobol’ - Zhe Xu; Zongling Yu; Xin Ning; Changyin Zhao; Zhihua Zhu; Zhibin Feng (Case Studies in Thermal Engineering)
Fan duct surface heat exchanger is a new type air-oil heat exchanger adopted in aero-engine lubricating oil system in recent years. A characteristics prediction method based on regional heat transfer correlation is proposed for it to realize heat transfer capacity and oil-side pressure drop calculation, which is validated by conducting thermodynamic experiment. Compared to experimental data, the average relative errors of heat transfer capacity and oil-side pressure drop prediction are 9.39 % and 8.39 %, respectively, which indicates that this method is feasible and reliable. A heat transfer unit response model between nine configuration parameters and heat transfer efficiency, weight is constructed by combining Taguchi and Particle Swarm Optimization-trained Adaptive Neuro-Fuzzy Inference System, and based on it, a characteristics optimization method based on Non-dominated Sorting Whale Optimization Algorithm aided by Sobol’ is proposed to simultaneously realize heat transfer enhancement, flow resistance reduction, and lightweight design. Compared to the original configuration, heat transfer capacity of the selected optimal solutions increases by 12.42 % averagely, while oil-side pressure drop and weight separately decrease by 37.64 % and 12.70 % on average, which indicates that this method is effective and helpful and can provide beneficial guidance for heat exchanger design.