Multisource aerodynamic data reconstruction method using an enhanced multifidelity neural network

The acquisition of aircraft aerodynamic pressure distribution typically relies on wind tunnel tests or numerical simulations. However, discrepancies in accuracy and efficiency between multisource data present challenges for aerodynamic data reconstruction. In the present work, we propose a novel multifidelity architecture to enhance the model's applicability to nonlinear inconsistency problems commonly encountered in transonic aerodynamic problems. By incorporating difference operations into the multifidelity neural network, the model can adaptively find suitable mapping relationships from potential low fidelity data. This method can reduce modeling errors when there is a trend inconsistencies between high and low fidelity data, which is a challenge that traditional multifidelity models struggle to address. To demonstrate the efficiency of our proposed ideas, we conducted multifidelity modeling on classic numerical examples and aerodynamic cases. Predictive results indicate that inconsistencies between multifidelity data can significantly affect traditional models, whereas the proposed multifidelity approach can enhance generalization performance. The reconstruction results of transonic pressure distribution for airfoils and the Office National d’Études et de Recherches Aérospatiales (ONERA) M6 wing indicate that the enhanced multifidelity model can effectively capture shock wave locations, thereby improving modeling accuracy. Analysis of the reconstruction results for pressure distribution indicates that the proposed method can reduce reconstruction error by over 30% compared to deep neural networks and multifidelity neural network. This method is also applicable for the data fusion of experimental and simulation data in various engineering problems.