AI-Based Clustering of Numerical Flow Fields for Accelerating the Optimization of an Axial Turbine

Simon Eyselein, Alexander Tismer (), Rohit Raj, Tobias Rentschler and Stefan Riedelbauch
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Simon Eyselein: Institute of Fluid Mechanics and Hydraulic Machinery, University of Stuttgart, Pfaffenwaldring 10, 70569 Stuttgart, Germany
Alexander Tismer: Institute of Fluid Mechanics and Hydraulic Machinery, University of Stuttgart, Pfaffenwaldring 10, 70569 Stuttgart, Germany
Rohit Raj: Institute of Fluid Mechanics and Hydraulic Machinery, University of Stuttgart, Pfaffenwaldring 10, 70569 Stuttgart, Germany
Tobias Rentschler: Institute of Fluid Mechanics and Hydraulic Machinery, University of Stuttgart, Pfaffenwaldring 10, 70569 Stuttgart, Germany
Stefan Riedelbauch: Institute of Fluid Mechanics and Hydraulic Machinery, University of Stuttgart, Pfaffenwaldring 10, 70569 Stuttgart, Germany

Energies, 2025, vol. 18, issue 3, 1-24

Abstract: The growing number of Renewable Energy Sources has increased the demand for innovative and high-performing turbine designs. Due to the increase in computing resources over recent years, numerical optimization using Evolutionary Algorithms (EAs) has become established. Nevertheless, EAs require many expensive Computational Fluid Dynamics (CFD) simulations, and more computational resources are needed with an increasing number of design parameters. In this work, an adapted optimization algorithm is introduced. By employing an Artificial Intelligence (AI)-based design assistant, turbines with a similar flow field are clustered into groups and provide a dataset to train AI models. These AI models can predict the flow field’s clustering before a CFD simulation is performed. The turbine’s efficiency and cavitation volume are predicted by analyzing the turbine’s properties inside the predicted clustering group. Turbines with properties below a certain threshold are not CFD-simulated but estimated by the design assistant. By this procedure, currently, more than 30% of the CFD simulations are avoided, significantly reducing computational costs and accelerating the overall optimization workflow.

Keywords: optimization; axial turbine; clustering; autoencoder; artificial intelligence; evolutionary algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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