Version 1
: Received: 14 May 2024 / Approved: 14 May 2024 / Online: 15 May 2024 (10:24:19 CEST)
How to cite:
Aulich, M.; Goinis, G.; Voß, C. Data-Driven AI Model for Turbomachinery Compressor Aerodynamics Enabling Rapid Approximation of 3D Flow Solutions. Preprints2024, 2024050980. https://doi.org/10.20944/preprints202405.0980.v1
Aulich, M.; Goinis, G.; Voß, C. Data-Driven AI Model for Turbomachinery Compressor Aerodynamics Enabling Rapid Approximation of 3D Flow Solutions. Preprints 2024, 2024050980. https://doi.org/10.20944/preprints202405.0980.v1
Aulich, M.; Goinis, G.; Voß, C. Data-Driven AI Model for Turbomachinery Compressor Aerodynamics Enabling Rapid Approximation of 3D Flow Solutions. Preprints2024, 2024050980. https://doi.org/10.20944/preprints202405.0980.v1
APA Style
Aulich, M., Goinis, G., & Voß, C. (2024). Data-Driven AI Model for Turbomachinery Compressor Aerodynamics Enabling Rapid Approximation of 3D Flow Solutions. Preprints. https://doi.org/10.20944/preprints202405.0980.v1
Chicago/Turabian Style
Aulich, M., Georgios Goinis and Christian Voß. 2024 "Data-Driven AI Model for Turbomachinery Compressor Aerodynamics Enabling Rapid Approximation of 3D Flow Solutions" Preprints. https://doi.org/10.20944/preprints202405.0980.v1
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 remains 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. Therefore the development of a data-driven AI model was initiated to ensure the potential of further use of the CFD data which is also used to train the AI model. 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 analysed 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 behaviour in an optimisation setup. Overall, the test case has shown promising results and has provided hints for further improvements of the AI model.
Keywords
AI for 3D CFD; turbomachinery; compressor design; aerodynamic optimization; transformer network; deep neural network
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
