Research on flight load surrogate model using neural networks

In aircraft structural strength design, flight load analysis is required, but the load analysis cycle is long,so it is necessary to study more efficient and accurate flight load analysis methods to shorten the load design cycle.The horizontal tail of a turboprop aircraft is studied for example.The input cases and output loads for training and checking are obtained by flight simulation in the full flight envelope according to standards and horizontal tail distributed loads calculation.In this paper, three surrogate models of horizontal tail loads are built based on BP neural network, RBF neural network and ELM neural network respectively.And the accuracy and efficiency for horizontal tail root section loads prediction of different models are compared.And the quantitative analysis of contribution for input load parameters is conducted.The study results show that all three neural network models are accurate,which can greatly improve the analysis efficiency of flight load.