Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 9; p. 1022
• Main Authors: He, Lei, Qian, Weiqi, Zhao, Tun, Wang, Qing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 12-09-2020; MDPI
• Subjects: Aerodynamic coefficients; aerodynamic data fusion; Aerodynamics; Aircraft; Algorithms; Angle of attack; Architecture; Artificial neural networks; cokriging; Computational fluid dynamics; Computer simulation; Data integration; deep neural networks; Design optimization; Engineering; Experiments; Flight simulation; machine learning; Methods; Model accuracy; Modelling; multi-fidelity data; Neural networks; Noise; Parameter identification; variable complexity modeling
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e22091022

To generate more high-quality aerodynamic data using the information provided by different fidelity data, where low-fidelity aerodynamic data provides the trend information and high-fidelity aerodynamic data provides value information, we applied a deep neural network (DNN) algorithm to fuse the information of multi-fidelity aerodynamic data. We discuss the relationships between the low-fidelity and high-fidelity data, and then we describe the proposed architecture for an aerodynamic data fusion model. The architecture consists of three fully-connected neural networks that are employed to approximate low-fidelity data, and the linear part and nonlinear part of correlation for the low- and high-fidelity data, respectively. To test the proposed multi-fidelity aerodynamic data fusion method, we calculated Euler and Navier–Stokes simulations for a typical airfoil at various Mach numbers and angles of attack to obtain the aerodynamic coefficients as low- and high-fidelity data. A fusion model of the longitudinal coefficients of lift CL and drag CD was constructed with the proposed method. For comparisons, variable complexity modeling and cokriging models were also built. The accuracy spread between the predicted value and true value was discussed for both the training and test data of the three different methods. We calculated the root mean square error and average relative deviation to demonstrate the performance of the three different methods. The fusion result of the proposed method was satisfactory on the test case, and showed a better performance compared with the other two traditional methods presented. The results provide evidence that the method proposed in this paper can be useful in dealing with the multi-fidelity aerodynamic data fusion problem.