A comparison of turbine mass flow models based on pragmatic identification data sets for turbogenerator model development. (15th May 2022)

• Record Type: Journal Article
• Authors: Tregenza, Owen; Olshina, Noam; Hield, Peter; Manzie, Chris; Hulston, Chris
• Is Part Of: Energy. Volume 247(2022)
• Publication Date: 2022-05-15
• Subjects: Turbine -- Mass flow model -- Extrapolation -- Performance map; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.123073
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Highlights: Comparison of turbine mass flow models calibrated using an experimental data set. A novel turbine mass flow model family is presented & tuned using experimental data. The proposed model demonstrated improved performance compared to reviewed models. Akaike Information Criteria is used for rigorous model selection. The proposed novel model is generalisable to other variable flow restrictions.