Prediction of Transonic Flow over Cascades via Graph Embedding Methods on Large-Scale Point Clouds

Abstract

In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed improvement compared to traditional CFD calculations while maintaining a commendable level of accuracy. Taking advantage of a multilayer graph structure, the framework can extract both global and local information from the cascade flow field simultaneously and present prediction over unstructured data. In line with the results obtained from the test datasets, we conducted an in-depth analysis of the geometric attributes of the cascades reconstructed using our framework, considering adjustments made to the geometric information of the point cloud. We fine-tuned the input using 1603 data points and quantified the contribution of each point. The outcomes reveal that variations in the suction side of the cascade have a significantly more substantial influence on the field results compared to the pressure side and explain the way graph neural networks work for cascade flow-field prediction, enhancing the comprehension of graph-based flow-field prediction among developers and proves the potential of graph neural networks in flow-field prediction on large-scale point clouds and design.

A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

“…Wu et al [77] also leveraged the property of GAN combined with CNN to directly establish a one-to-one mapping between a parameterized supercritical airfoil and its corresponding transonic flow field profile over the parametric space. Lan et al [95] presented a novel framework to predict cascades flow fields, utilizing GCN and point clouds to enhance prediction performance, as shown in Figure 4. It has been proved that the innovative framework can reconstruct the internal flow field at a high speed on a large-scale point cloud, while maintaining the accuracy of the prediction.…”

“…It has been that the innovative framework can reconstruct the internal flow field at a high spe large-scale point cloud, while maintaining the accuracy of the prediction. [95].…”