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Performance Prediction of Cooled Compressors Using Neural Networks

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  • Technische Universität Braunschweig

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Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalInternational Journal of Gas Turbine, Propulsion and Power Systems
Volume15
Issue number6
Publication statusPublished - Nov 2024

Abstract

Future compressors require the use of novel technologies to improve their efficiency, off-design performance, and thermal management. A frequently discussed option to achieve these goals is the use of active cooling methods in compressors. Maintaining a low temperature during the compression process improves the overall performance of the compressor such as its efficiency or mass flow capacity. It is thus essential to consider the effect of cooling during the early design stages. However, current preliminary design methods rely heavily on empirical data and experience and are, therefore, only partially applicable for the design of cooled compressors. In this work, we demonstrate how modern data-driven methods may be used to obtain a surrogate model for the fast and accurate prediction of performance parameters for cooled compressors. To do so, we train a feed-forward neural network to predict the total pressure and temperature ratio, as well as the mass flow rate of a 4½-stage axial compressor test case with arbitrarily cooled stator vanes. The performance predictions of our machine learning model extend over the full (numerical) operating range of the test case and deviate by less than 1% from computational fluid dynamics (CFD) simulations on the test data set. We further demonstrate and discuss the accuracy and generalisation capabilities of this approach by predicting entire performance maps for different cooling configurations.

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Cite this

Performance Prediction of Cooled Compressors Using Neural Networks. / Blechschmidt, Dominik; Mimic, Dajan.
In: International Journal of Gas Turbine, Propulsion and Power Systems, Vol. 15, No. 6, 11.2024, p. 1-9.

Research output: Contribution to journalArticleResearchpeer review

Blechschmidt, D & Mimic, D 2024, 'Performance Prediction of Cooled Compressors Using Neural Networks', International Journal of Gas Turbine, Propulsion and Power Systems, vol. 15, no. 6, pp. 1-9. https://doi.org/10.38036/jgpp.15.6_v15n6tp01
Blechschmidt, D., & Mimic, D. (2024). Performance Prediction of Cooled Compressors Using Neural Networks. International Journal of Gas Turbine, Propulsion and Power Systems, 15(6), 1-9. https://doi.org/10.38036/jgpp.15.6_v15n6tp01
Blechschmidt D, Mimic D. Performance Prediction of Cooled Compressors Using Neural Networks. International Journal of Gas Turbine, Propulsion and Power Systems. 2024 Nov;15(6):1-9. doi: 10.38036/jgpp.15.6_v15n6tp01
Blechschmidt, Dominik ; Mimic, Dajan. / Performance Prediction of Cooled Compressors Using Neural Networks. In: International Journal of Gas Turbine, Propulsion and Power Systems. 2024 ; Vol. 15, No. 6. pp. 1-9.
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