Intelligent predictive optimization method for integrated aircraft-engine thermal management system

Sudden load variations of airborne electronic equipment cause transient and significant temperature changes during flight missions. To address this, an Integrated Aircraft-Engine Thermal Management System (IAETMS) model is established. Simulation results indicate that the heat dissipation capacity of the thermal management system is improved by 28.6% compared to the conventional single-bypass structure under the three-bypass flight condition. To overcome thermal lag, a Long Short-Term Memory (LSTM) network is employed to forecast transient thermal loads, while a genetic algorithm is adopted for the real-time optimal control of the system. Thus, the dynamic prediction and optimal regulation of the IAETMS are achieved. Ultimately, the proposed intelligent predictive optimization framework ensures the temperature stability at key nodes, improves the overall heat dissipation capacity by 14% and residual cooling capacity by 61% compared to traditional rule-based control strategies, and significantly enhances the thermal endurance of the aircraft under extreme operational conditions. The novelty of this work lies in the integration of LSTM-based predictive control with multi-condition optimization for aircraft thermal management, significantly improving transient response and heat dissipation efficiency.