Deep transfer learning for three-dimensional aerodynamic pressure prediction under data scarcity

Abstract

Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft, and the requirement for mass data still means a high cost. To address this problem, we propose a novel Point-Cloud Multi-condition Aerodynamics Transfer Learning (PCMCA-TL) framework that enables aerodynamic prediction in data-scarce scenarios by transferring knowledge from well-learned scenarios. We modified the PointNeXt segmentation architecture to a PointNeXtReg+ regression model, including a working condition input module. The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset. The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training, pre-training, and TL models. Furthermore, by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the corresponding CFD results obtained in hours, the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.

Keywords

• Aerodynamic prediction
• Deep transfer learning
• Point cloud
• Multi-condition scenarios
• Small-scale dataset