A Hybrid CFD–ε–NTU–Neural Network Framework for Installed Thermal Performance Evaluation of Nacelle-Integrated Compact Heat Exchangers

This study presents a hybrid CFD–ε–NTU–neural network framework for evaluating the thermal performance of compact heat exchangers installed in confined nacelle environments under severe space and weight constraints. Airflow was simulated using steady Reynolds-Averaged Navier–Stokes (RANS) equations with the shear stress transport (SST) k–ω turbulence model, while heat-exchanger performance was evaluated using the effectiveness–number of transfer units (ε–NTU) method. To provide a continuous functional interface between the analytical model and the CFD solver, a neural network was employed as a representation of ε–NTU-based thermal performance. The proposed framework was validated against wind tunnel experiments, showing good agreement with measured data, with a maximum deviation of approximately 2.2 °C in coolant outlet temperature. Using the validated framework, a multi-objective redesign of a compact heat exchanger was performed under practical installation constraints. The selected design achieved a 28.4% reduction in heat exchanger mass while maintaining cooling performance comparable to the baseline design. Installation performance was evaluated numerically in a modified nacelle configuration, where the redesigned heat exchanger increased the air mass flow rate by approximately 13% under identical fan operating conditions because of its reduced air-side flow resistance. The proposed framework provides a practical and efficient approach for system-level design and installation analysis of compact heat exchangers for aircraft thermal management systems.