Deep learning modelling of manufacturing and build variations on
multi-stage axial compressors aerodynamics
- URL: http://arxiv.org/abs/2310.04264v3
- Date: Wed, 13 Mar 2024 13:42:40 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 real-time predictions of the impact of manufacturing and build variations.
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: Application of deep learning methods to physical simulations such as CFD
(Computational Fluid Dynamics) for turbomachinery applications, have been so
far of limited industrial relevance. This paper demonstrates the development
and application of a deep learning framework for real-time predictions of the
impact of manufacturing and build variations, such as tip clearance and surface
roughness, on the flow field and aerodynamic performance of multi-stage axial
compressors in gas turbines. The associated scatter in compressor efficiency is
known to have a significant impact on the corresponding overall performance and
emissions of the gas turbine, 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 and potentially
reduce requirements for expensive physical tests.
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