Session: 31-08 Compressor Prediction Methodology

Paper Number: 122636

122636 - Manufacturing and Build Variations Modelling for Multi-Stage Axial Compressors: CFD Aerodynamic Predictions 

The numerical prediction of multi-stage compressor flows has historically been a challenge for CFD models, due to their complex and three-dimensional nature. High-fidelity models such as LES and DNS have shown promising results, but their application is limited by the high computational cost, while steady-state RANS are still used for the majority of applications in the industry. Over the years, engine manufacturers have developed best-practices and guidelines for multi-stage axial compressor simulations, providing reliable CFD models capable of accurate predictions when compared to test data, both with regards to performance and surge margin at different operating conditions. However, even with state-of-the-art models, several discrepancies can be observed and are generally addressed with calibration based on operational experience. These discrepancies are partially due to known limitations of steady-state CFD, as well as to the modelling assumptions and using idealised geometries. This paper demonstrates how the accuracy of CFD predictions can be greatly improved by modelling the effect of manufacturing and build variations, such as tip clearance, surface roughness and in-tolerance geometry variations. The resulting engine-build specific models show greatly improved agreement with engine test data, with no increase in computational cost. These models can be used as part of the design process, to develop robust axial compressor designs which are insensitive to manufacturing and build variations. Moreover, the results from the analyses can be used to re-define tolerance ranges and drawing specifications, to reduce the potential impact on build-to-build engine performance scatter.

Presenting Author: Giuseppe Bruni Siemens Energy Industrial Turbomachinery Ltd.

Presenting Author Biography: Giuseppe Bruni is currently employed as a Principal Aerodynamicist by Siemens Energy Industrial Turbomachinery Ltd, Lincoln, UK, where he has been working since 2016. He received the BSc and MSc degrees in Mechanical Engineering from University of Padova, Italy, respectively in 2014 and 2016. He received the MSc in Gas Turbine Technology from Cranfield University, UK in 2016, and is currently working towards a PhD at the University of Lincoln, UK, on the topic of Machine learning modelling of manufacturing variations on compressor aerodynamic performance. His research interest include the aerodynamic, aero-mechanical analysis and design of axial
compressors, as well as development and applications of optimisation and machine learning methods. He is a Chartered Engineer and member of the IMechE.

Authors:

Giuseppe Bruni Siemens Energy Industrial Turbomachinery Ltd.
Senthil K. Krishnababu Siemens Energy Industrial Turbomachinery Ltd.