Application of Neural Network Models for Analyzing the Impact of Flight Speed and Angle of Attack on Flow Parameter Non-Uniformity in a Turbofan Engine Inlet Duct

This study investigates the aerodynamic performance of a fourth-generation normal shockwave inlet system, with a primary focus on minimizing pressure losses and ensuring uniform airflow distribution. A computational model was developed, incorporating a section of the fuselage along with the complete inlet duct. The model was discretized using a hybrid mesh approach to enhance numerical accuracy. The analysis was conducted at a flight altitude of 8000 m, encompassing 370 distinct cases defined by varying angles of attack and Mach numbers. This comprehensive parametric study yielded a dataset of 10,800 total pressure measurements across predefined sampling locations. Based on the obtained results, flow distortion coefficients in both circumferential (CDI) and radial directions (RDI) were systematically determined for each test case. The interdependencies between CDI, RDI, Mach number, and angle of attack (α) were analyzed and presented in a consolidated manner. In the second phase of the study, an artificial neural network (ANN) utilizing a Feed-Forward architecture was implemented to predict pressure distributions for intermediate flight conditions. The ANN was trained using the CFG algorithm, and the predictive accuracy was assessed through the determination coefficients computed by comparing ANN-based estimates with numerical simulation results. The findings demonstrate the efficacy of ANN-based modeling in enhancing the predictive capabilities of inlet flow dynamics, offering valuable insights for optimizing next-generation supersonic air intake systems.