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

초록

This study presents a hybrid CFD-epsilon-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-omega turbulence model, while heat-exchanger performance was evaluated using the effectiveness-number of transfer units (epsilon-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 epsilon-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 degrees 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.

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