A comparison of turbine mass flow models based on pragmatic identification data sets for turbogenerator model development

Owen Tregenza, Noam Olshina, Peter Hield, Chris Manzie and Chris Hulston

Energy, 2022, vol. 247, issue C

Abstract: The use of turbogenerators as a means of waste heat recovery has gained interest from industry and academia in recent years. Accurate mass flow models of turbogenerators are required for assessing their impact on overall engine performance during design–development. This paper presents the results of an experimental model identification program for a commercially available turbogenerator. The experimental data was categorised into training and validation data sets. Training data sets were selected using a simulation based method to bound data within a region representative of turbocharger turbine operation. A comprehensive review of promulgated models is presented, and the replication and extrapolation performance with respect to the experimental data sets is assessed. A new family of models is proposed which is applicable to a large class of radial flow turbines. Application of a systematic model selection process based on Akaike Information Criteria yields models from this family with improved performance. Furthermore, the robustness of the proposed family of models is assessed using published experimental data sets from a range of turbine designs, demonstrating the versatility of the proposed model family and model selection techniques.

Keywords: Turbine; Mass flow model; Extrapolation; Performance map (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:247:y:2022:i:c:s0360544221033223

DOI: 10.1016/j.energy.2021.123073

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