High and low order precision data fusion method for aircraft based on transfer learning
In order to enhance the efficacy of aircraft aerodynamic performance analysis and reduce expenditure,two data fusion methods based on transfer learning are implemented.The first one is a multi-fidelity fusion method with weight distribution based on sample features.In this case,an adap-tive boosting algorithm is employed to assess the sample error rate,and the weighted fusion is con-ducted by using the error rate.Another method is a parameter freezing transfer method based on deep neural network models.This method trains high-order and low-order precision data into network layers with the objective of achieving data fusion in the sense of the model.Both methods consider deep fusion training of high-order precision wind tunnel test data and low-order precision CFD data,fully leveraging the correlation between high-order and low-order data,thereby improving prediction accuracy.The results of the analysis of the YF-16 airplane model demonstrate that the data fusion method based on transfer learning can achieve accurate prediction of aerodynamic forces and returns more precise predictions compared with the traditional CoKriging method.
data fusiontransfer learningwind tunnel testingCFD calculationdeep neural network
NETL
NSTL
万方数据
