Aerodynamic Prediction and Design Optimization Using Multi-Fidelity Deep Neural Network

Abstract

With the rapid development of data-driven methods in recent years, deep neural networks have attracted significant attention for aerodynamic predictions and design optimizations. Among these methods, the multi-fidelity deep neural network (MFDNN), which can combine high-fidelity (HF) and low-fidelity (LF) data, has gained popularity. This paper systematically investigates the performances of employing MFDNN models in predicting aerodynamic coefficients and in performing aerodynamic shape optimizations (ASOs), especially the impact of using various HF/LF data ratios for training models. The results of the prediction accuracy of the aerodynamic coefficients of airfoils show that the less HF data used, the more advantages can be achieved by the MFDNN models than the single-fidelity models. The well-trained MFDNN models are then employed in an ASO problem of airfoil in the subsonic regime, and it is found that a higher HF/LF data ratio does not definitely result in a better performance in the ASO. As the insufficiency in the prediction accuracy of the optimal shapes appears when employing the non-updated MFDNN models, an update strategy is developed by tightly integrating the MFDNN models with the particle swarm optimization algorithm. To further reduce the time costs for updating models, a dual-threshold update strategy is then introduced, which can half the counts of evaluating HF data.

Keywords

• aerodynamic shape optimization
• multi-fidelity model
• surrogate model
• neural networks
• computational fluid dynamics