A Data-Centric Approach to Loss Mechanisms for Compressor Preliminary Design
type: CreativeWork
description: In the preliminary stages of design, low-fidelity models, which describe the underlying design space, are used to guide designers to optimal points in the design space. However, the majority of models currently used in compressor preliminary design are often limited by the human ability to find physical patterns in complex multidimensional data. Detecting patterns in complex multidimensional data is exactly the task for which machine learning and other statistical techniques have been developed. The aim of this thesis, therefore, is to try and use machine learning to augment the human ability to find physical relationships and to consequently develop a more physically accurate and general preliminary design system for compressors. In this work, a data-centric approach is proposed that utilises the strengths of machine learning to highlight patterns in complex data, while also utilising the strengths of human wisdom to ensure the models are physically meaningful. This approach is applied to datasets populated by large numbers of computational simulations for compressor and turbine blades. It is shown using 2D data that a new profile loss model can be developed which halves the error in preliminary design correlations and generalises well to turbines as well as compressors. The reason for these improvements is shown to be the result of a more physically accurate decomposition for mixing loss than current industry standard models. This new mixing loss model utilises flow properties local to the wake and is shown to explain the limiting cases for the mixing of two flows, the mixing out of a trailing edge blockage and the form drag of an aerofoil. It is argued that the reason this model is more accurate and general than existing models is that it retains the physical form for the source of loss creation at the fundamental scale. The data-centric approach is also applied to a 3D RANS compressor linear repeating stage dataset and shown to help develop a model for the total…
abstract: In the preliminary stages of design, low-fidelity models, which describe the underlying design space, are used to guide designers to optimal points in the design space. However, the majority of models currently used in compressor preliminary design are often limited by the human ability to find physical patterns in complex multidimensional data. Detecting patterns in complex multidimensional data is exactly the task for which machine learning and other statistical techniques have been developed. The aim of this thesis, therefore, is to try and use machine learning to augment the human ability to find physical relationships and to consequently develop a more physically accurate and general preliminary design system for compressors. In this work, a data-centric approach is proposed that utilises the strengths of machine learning to highlight patterns in complex data, while also utilising the strengths of human wisdom to ensure the models are physically meaningful. This approach is applied to datasets populated by large numbers of computational simulations for compressor and turbine blades. It is shown using 2D data that a new profile loss model can be developed which halves the error in preliminary design correlations and generalises well to turbines as well as compressors. The reason for these improvements is shown to be the result of a more physically accurate decomposition for mixing loss than current industry standard models. This new mixing loss model utilises flow properties local to the wake and is shown to explain the limiting cases for the mixing of two flows, the mixing out of a trailing edge blockage and the form drag of an aerofoil. It is argued that the reason this model is more accurate and general than existing models is that it retains the physical form for the source of loss creation at the fundamental scale. The data-centric approach is also applied to a 3D RANS compressor linear repeating stage dataset and shown to help develop a model for the total…
author: Senior, Alistair
language: eng
keywords: Machine Learning, Compressor, Loss, Data-Centric, Loss models, Loss modelling, Compressor Design
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/a86f3055-bc86-4ded-ae00-8946e2bff32c/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2024-01-12
date modified: 2026-07-22T22:24:17Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.113330