| Citation: | TAO Kaihang, ZHOU Zi, LIANG Zhirong, et al. Research on heat transfer deterioration prediction of supercritical RP-3 based on optimized neural network[J]. Journal of Aerospace Power, 2025, 40（11）：20250187 doi: 10.13224/j.cnki.jasp.20250187
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Currently, research on heat transfer deterioration in the flow and heat transfer process of supercritical aviation kerosene RP-3 remains limited. Existing approaches mainly rely on empirical correlations and numerical simulations, which struggle to accurately capture the nonlinear characteristics associated with complex fluid heat transfer. To precisely predict the heat transfer deterioration behavior of supercritical RP-3 under typical operating conditions, computational fluid dynamics methods were employed; after experimental validation, a high-fidelity database covering pipe diameters from 1 to 10 mm and pressures ranging from 3.0 to 6.0 MPa was established. A multilayer neural network structure was then designed and optimized, resulting in a well-tuned model with strong generalization ability and high prediction accuracy. The results indicated that as pipe diameter increased or pressure decreased, local buoyancy and thermal acceleration effects were significantly intensified, exacerbating the degree of heat transfer deterioration and shifting the temperature peak downstream. The optimized model accurately captured these nonlinear trends, with prediction errors achieved within 1.5%, thus providing a reliable data-driven tool for efficient design of the thermal management systems in aero-engines.
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