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Unmanned Aerial Vehicles (UAVs) encounter various uncertainties, including unfamiliar environments, signal delays, limited control precision, and other disturbances during task execution. Such factors can significantly compromise flight safety in complex scenarios. In this paper, to enhance the safety of UAVs amidst these uncertainties, a control accuracy prediction model based on ensemble learning abnormal state detection is designed. By analyzing the historical state data, the trained model can be used to judge the current state and obtain the command tracking control accuracy of the UAV at that instant. Ensemble learning offers superior classification capabilities compared to weak learners, particularly for anomaly detection in flight data. The learning efficacy of support vector machine, random forest classifier is compared and achieving a peak accuracy of 95% for the prediction results using random forest combined with adaboost model .

An improved anti-icing design with film heating ejection slot and cover for the inlet part of aero-engine was brought out, which combines the interior jet impingement with the exterior hot air film heating and shows promising application for those parts manufactured with composite materials. A hybrid method based on the combination of the Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA) is developed to optimize the anti-icing design for a typical aero-engine inlet vane in two dimensions. The optimization aims to maximize the heating performance of the hot air film, which is assessed by the heating effectiveness. The film-heating ejection angle and the cover opening angle are the two geometric variables to be optimized. Numerical model was established and validated to generate training and testing samples for BPNN, which was used to predict the objective function and find the optimal design variables in conjunction with the GA.

For the ice protection of the engine air induction part manufactured with low thermal conductivity composite material, the combined heating method using interior impingement and exterior air film has certain advantages. To study the influence of the external jet air film on the impingement characteristics of droplets, the numerical simulation method of three dimensional water droplet impingement based on Eulerian method was developed and verified by experimental data from references. The droplets impingement characteristics under three different blowing ratios and two different velocities were then investigated based on the configuration of 3D cylinder with two parallel jet holes.

The main objective of this work is to investigate, by means of numerical simulations, the performance of the engine nacelle ventilation cooling system of a helicopter under hover and forward flight conditions, and to propose a simplified method of evaluating the performance based on rotor downwash flow by taking the synthetical effect of engine nacelle, exhaust ejector and external flow of a helicopter into account. For the engine nacelle of a helicopter, an integrated model of the nacelle and exhaust ejector was set up including the domain of external flow. The unstructured grid and finite volume method were applied for domains and control equations discreteness, and the standard k-ε model was applied for solving turbulent control equations. Using the business CFD software, the flow field and the temperature field in the nacelle were calculated for single inlet scheme and double inlets scheme, total up to 9 schemes. The performance of the exhaust ejector was computed.