Comparison of machine learning data fusion methods applied to aerodynamic modeling of rocket first stage with grid fins

Machine learning data fusion method has attracted significant attention recently in aerodynamic database construction since it makes a trade-off between high prediction accuracy and low fitting cost by fusing samples of different fidelities. But the research on methods for complex engineering project is not sufficient. In this paper, several commonly used variable-fidelity models （VFMs） of data fusion are applied to the control law design in the rocket first stage landing area control project with grid fins. Based on wind tunnel tests of partial test states, combined with CFD simulation results, VFMs successfully predict the whole aerodynamic characteristics of grid fins. Here, our objective is to compare the performances of these four VFM methods （AS-MFS, Co-Kriging, HK, MFNN） and the results show that: Gaussian exponential function is more suitable for aerodynamic modeling problems; Co-Kriging has the best performance in the interpolation of aerodynamic data; HK model has high prediction accuracy for interpolation but has poor performance for extrapolation; MFNN model can obtain smoother and more reasonable results in the extrapolation region.