C(NN)FD -- Deep Learning Modelling of Multi-Stage Axial Compressors Aerodynamics

• URL: http://arxiv.org/abs/2503.14369v1
• Date: Tue, 18 Mar 2025 15:58:58 GMT
• Title: C(NN)FD -- Deep Learning Modelling of Multi-Stage Axial Compressors Aerodynamics
• Authors: Giuseppe Bruni, Sepehr Maleki, Senthil K Krishnababu,
• Abstract summary: This paper demonstrates the development and application of a generalized deep learning framework for predictions of the flow field and aerodynamic performance of multi-stage axial compressors.<n>A physics-based dimensionality reduction unlocks the potential for flow-field predictions for large-scale domains, re-formulating the regression problem from an unstructured to a structured one.<n>The proposed framework has the advantage of physically explainable predictions of overall performance, as the corresponding aerodynamic drivers can be identified on a 0D/1D/2D/3D level.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of scientific machine learning and its applications to numerical analyses such as CFD has recently experienced a surge in interest. While its viability has been demonstrated in different domains, it has not yet reached a level of robustness and scalability to make it practical for industrial applications in the turbomachinery field. The highly complex, turbulent, and three-dimensional flows of multi-stage axial compressors for gas turbine applications represent a remarkably challenging case. This is due to the high-dimensionality of the regression of the flow-field from geometrical and operational variables, and the high computational cost associated with the large scale of the CFD domains. This paper demonstrates the development and application of a generalized deep learning framework for predictions of the flow field and aerodynamic performance of multi-stage axial compressors, also potentially applicable to any type of turbomachinery. A physics-based dimensionality reduction unlocks the potential for flow-field predictions for large-scale domains, re-formulating the regression problem from an unstructured to a structured one. The relevant physical equations are used to define a multi-dimensional physical loss function. Compared to "black-box" approaches, the proposed framework has the advantage of physically explainable predictions of overall performance, as the corresponding aerodynamic drivers can be identified on a 0D/1D/2D/3D level. An iterative architecture is employed, improving the accuracy of the predictions, as well as estimating the associated uncertainty. The model is trained on a series of dataset including manufacturing and build variations, different geometries, compressor designs and operating conditions. This demonstrates the capability to predict the flow-field and the overall performance in a generalizable manner, with accuracy comparable to the benchmark.

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