Xinyu Hui, Northwestern Polytechnical University Xi’an, China

Title:
Fast pressure distribution prediction and unsteady periodic flow field prediction method based on deep learning

Abstract:

In the aerodynamic design, optimization of the pressure distribution of airfoils is crucial for the aerodynamic components. Conventionally, the pressure distribution is solved by computational fluid dynamics, which is a time-consuming task. Surrogate modeling can leverage such expense to some extent, but it needs careful shape parameterization schemes for airfoils. As an alternative, deep learning approximates inputs-outputs mapping without solving the efficiency-expensive physical equations and avoids the limitations of particular parameterization methods. Therefore, I present a data-driven approach for predicting the pressure distribution over airfoils based on Convolutional Neural Network (CNN). Given the airfoil geometry, a supervised learning problem is presented for predicting aerodynamic performance. Furthermore, we utilize a universal and flexible parametrization method called Signed Distance Function to improve the performances of CNN. Given the unseen airfoils from the validation dataset to the trained model, the model achieves predicting the pressure coefficient in seconds, with a less than 2% mean square error.

A method based on deep learning to predict periodic unsteady flow field is proposed, and can predict the real-time complex vortex flow state at different moments accurately. Combining conditional generative adversarial network and convolutional neural network, improve the conditional constraint method from conditional generative adversarial network, a deep learning framework with conditional constraints is proposed, which is the regression generative adversarial network. The two scenarios of conditional generative adversarial network and regression generative adversarial network are tested and compared via giving different periodic moments to predict the corresponding flow field variables at this moment. The final results demonstrate that regression generative adversarial network can estimate complex flow fields, and way more faster than CFD simulation.