Data-Driven AI Model for Turbomachinery Compressor Aerodynamics Enabling Rapid Approximation of 3D Flow Solutions

Abstract

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remain unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. To make further use of this vast amount of CFD data a data-driven AI model based on the Transformer architecture is developed and trained using the available CFD data. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analyzed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behavior in an optimization setup. Overall, the test case has shown promising results and has provided hints for further improvements to the AI model.