Abstract
Abstract An non-parametric surrogate model for high-dimensional aerodynamic optimization is introduced in this paper. It directly builds a mapping relationship between surface meshes of objects and the two-dimensional (2D) distributions of flow variables. NPSM utilizes Graph Neural Networks (GNNs) to extract performance-sensitive geometric features from surface meshes according to the feedback of prediction errors. With this geometric information, NPSM can predict 2D distribution of flow variables with Convolutional Neural Networks (CNNs) rather than only several scalar performance metrics. This process reduces the uncertainties caused by manual parameterization of high-dimensional geometries, such as fuselage, wings and blades in turbomachinery. The mapping relationship established by NPSM enables the Non-Parametric Sensitivity Analysis (NPSA) via Automatic Differentiation (AD), which can derive sensitivity of each mesh vertex rather than sensitivity of geometric parameters. This sensitivity information makes NPSM explainable, and provides more information for redistribution of geometry control points. To safeguard the robustness of optimization, a design classifier is designed to utilize the latent space built by NPSM to identify predictable designs. In this paper, two cases are used to demonstrate NPSM. The symmetric bumps case is used to demonstrate the methodology and functionalities of NPSM. The capability of processing high-dimensional geometries is demonstrated with the low-pressure (LP) steam turbine rotor.
Published Version
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