Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics

• URL: http://arxiv.org/abs/2310.04264v4
• Date: Fri, 6 Sep 2024 14:35:22 GMT
• Title: Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics
• Authors: Giuseppe Bruni, Sepehr Maleki, Senthil K. Krishnababu,
• Abstract summary: This paper demonstrates the development and application of a deep learning framework for predictions of the flow field and aerodynamic performance of multi-stage axial compressors. A physics-based dimensionality reduction unlocks the potential for flow-field predictions. The proposed architecture is proven to achieve an accuracy comparable to that of the CFD benchmark, in real-time, for an industrially relevant application.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Applications of deep learning to physical simulations such as Computational Fluid Dynamics have recently experienced a surge in interest, and their viability has been demonstrated in different domains. However, due to the highly complex, turbulent and three-dimensional flows, they have not yet been proven usable for turbomachinery applications. Multi-stage axial compressors for gas turbine applications represent a remarkably challenging case, due to the high-dimensionality of the regression of the flow-field from geometrical and operational variables. This paper demonstrates the development and application of a deep learning framework for predictions of the flow field and aerodynamic performance of multi-stage axial compressors. A physics-based dimensionality reduction unlocks the potential for flow-field predictions, as it re-formulates the regression problem from an un-structured to a structured one, as well as reducing the number of degrees of freedom. Compared to traditional "black-box" surrogate models, it provides explainability to the predictions of overall performance by identifying the corresponding aerodynamic drivers. This is applied to model the effect of manufacturing and build variations, as the associated performance scatter is known to have a significant impact on $CO_2$ emissions, therefore posing a challenge of great industrial and environmental relevance. The proposed architecture is proven to achieve an accuracy comparable to that of the CFD benchmark, in real-time, for an industrially relevant application. The deployed model, is readily integrated within the manufacturing and build process of gas turbines, thus providing the opportunity to analytically assess the impact on performance with actionable and explainable data.

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