The aerodynamic noise of an aircraft leads to vibration fatigue damage to structures. Herein, a prediction method for aircraft surface noise under the comprehensive effect of mixed acoustic sources during flight, primarily surface aerodynamic, air intake, and tail nozzle jet noises, was studied. In the supersonic cruising state, the internal and external flow fields of the aircraft were solved using the Reynolds-averaged Navier–Stokes equations to obtain the statistical average solution of the initial turbulence. The non-linear disturbance equation was used to obtain the surface acoustic load of the aircraft. The calculation results revealed that the main source of aircraft surface noise is aerodynamic noise. The sound pressure level on the fuselage increases gradually from front to rear along the aircraft, and the OASPL at the air intake and tail nozzle is relatively large. The jet noise has little effect on the sound pressure level at the front of the fuselage and only contributes to the OASPL at the tail nozzle of the fuselage. The intensity of pressure pulsations from the engine exhaust in the tail section is 93.3% of the total intensity of pressure pulsations.

Presently, the heat dissipation design of DC charger of electric vehicle mainly adopts forced air cooling, which has poor sand and dust-proof performance. This paper provides a kind of air inlet filter scheme with curved structure. The ventilation and protection performances of four kinds of filter screens were tested by high temperature heat dissipation test and dust-proof test, which is of great significance to the protective design of chargers with forced air-cooled heat dissipation. Nylon filter screen and non-woven fabric filter screen have the best heat dissipation effect. The sand proof effect of non-woven fabric filter screen is better than nylon filter screen. The scheme of combining the structure of curved air inlet and non-woven fabric filter screen is the optimal scheme which has important application significance and popularization value.

This study aims to overcome the limitations of low prediction accuracy, long computation time, and limited computational states in aircraft surface noise analysis by introducing a machine learning-based approach. Using data obtained from high-fidelity numerical simulations, a deep feedforward neural network (DNN) model is developed to predict aircraft surface noise considering multiple influencing parameters. The model's robustness and predictive capability are enhanced through random search-based hyperparameter optimization, enabling efficient and accurate noise prediction within the training data range. Moreover, the incorporation of the softmax activation and cross-entropy loss functions facilitates automatic classification of flow states at monitoring points. An integrated neural network model, Physical-Model-Embedding Ensemble Neural Networks (PENN), incorporating empirical formulas for aircraft surface noise calculation is established, achieving noise fusion prediction combining simulation and engineering empirical formulas. The results demonstrate that the PENN model effectively addresses the limitations of low accuracy and poor generalization in high-precision modeling with small and sparse samples. Compared with the conventional DNN model, the PENN model achieves a 48.8% reduction in the root mean square error of the overall sound pressure level prediction.