Taylor, Alan Martin (2021) One Dimensional Mean Line Performance Prediction Program for Turbocharger Axial Turbines. MPhil thesis, University of Sheffield.
Abstract
Mean line turbine performance prediction calculations are commonplace in the early stages of the turbine design process and are used to predict turbine performance and estimate geometric parameters before committing to CFD analysis. Napier Turbochargers also use such a tool for turbine performance map generation, turbine maps which are used in engine simulation software to predict engine performance. Periodically it is necessary to update the loss mechanisms used in the 1D tool based on current experimental data and research to ensure the accuracy and relevance of the results are maintained. The current performance prediction code used by Napier Turbochargers requires updating, it is necessary to review suitable, more recent axial turbine loss mechanisms with the aim of increasing the accuracy of efficiency prediction over a wide range of operating conditions for families of turbine geometries.
The results retrieved from the new program in conjunction with the selected loss models are compared to CFD results in order to validate the performance code and assess the chosen loss correlations. A number of productionised turbine geometries have been selected from the Napier Turbocharger portfolio to test the suitability of each loss correlation.
Analysis of the results revealed the governing parameters of each loss correlation and their applicability to a turbocharger turbine stage. The results were also assessed with the goal of possibly creating a selective tool to select the optimum loss correlation based on the required operating condition or turbine geometry.
Metadata
Supervisors: | Howell, Rob |
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Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Mechanical Engineering (Sheffield) |
Depositing User: | Mr Alan Martin Taylor |
Date Deposited: | 31 May 2022 10:21 |
Last Modified: | 31 May 2022 10:21 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:30828 |
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