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

Giuseppe Bruni, Sepehr Maleki, Senthil K Krishnababu

TL;DR

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.

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.

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

TL;DR

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.

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.

Paper Structure

This paper contains 21 sections, 5 equations, 22 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Novel contributions
  4. Physics-based dimensionality reduction
  5. Multi-dimensional physical loss function
  6. Iterative architecture and uncertainty estimation
  7. Transfer Learning
  8. Methodology
  9. Data Generation
  10. Data Pre-Processing
  11. The C(NN)FD framework
  12. Model architecture
  13. Iterative networks
  14. Uncertainty Estimation
  15. Multi-dimensional physical loss function
  16. ...and 6 more sections

Figures (22)

  • Figure 1: Overview of the C(NN)FD framework
  • Figure 2: Overview of the CFD domain and axial velocity contours at the mixing-plane locations
  • Figure 3: C(NN)FD architecture overview
  • Figure 4: CFD Database resulting from the design activity
  • Figure 5: Effect of design variations: $\dot{m}$ comparison
  • ...and 17 more figures