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

TL;DR

This paper develops a deep learning framework with physics-based dimensionality reduction to predict flow fields and performance variations in multistage axial compressors, enabling real-time, explainable analysis of manufacturing impacts.

Contribution

It introduces a physics-informed deep learning model that reduces complexity and enhances explainability for turbomachinery flow predictions, outperforming traditional black-box models.

Findings

Achieves CFD-level accuracy in real-time predictions.

Effectively models manufacturing and build variation impacts.

Provides explainable insights into aerodynamic performance drivers.

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. Multistage 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 multistage axial compressors. A physics-based dimensionality reduction approach unlocks the potential for flow-field predictions, as it re-formulates the regression problem from an unstructured 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 the overall performance by identifying the corresponding aerodynamic drivers. The model is applied to manufacturing and build variations, as the associated performance scatter is known to have a significant impact on $CO_2$ emissions, which poses 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.