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Proceedings Papers

Application of Machine Learning Based Surrogate Model for Prediction of Sectional Temperature of Radially Cooled Gas Turbine Blades

Rishabh Shrivastava, Nisha Tamar, Amit Grover, Debdulal Das
Proc. ASME. GTINDIA2021, ASME 2021 Gas Turbine India Conference, V001T02A003, December 2–3, 2021
Paper No: GTINDIA2021-76053
DOI: https://doi.org/10.1115/GTINDIA2021-76053
... models to predict the sectional temperature (output) of a radially cooled blade. The surrogate models are developed to predict the output using turbine inlet temperature, hot gas mass flow, cooling air temperature and cooling air mass flow as input to the machine learning (ML) model. All thermal...
View Paper titled, Application of Machine Learning Based Surrogate Model for Prediction of <span class="search-highlight">Sectional</span> <span class="search-highlight">Temperature</span> of Radially Cooled Gas Turbine Blades
Click here to open pdf in another window PDF for Application of Machine Learning Based Surrogate Model for Prediction of <span class="search-highlight">Sectional</span> <span class="search-highlight">Temperature</span> of Radially Cooled Gas Turbine Blades
Topics: Blades, Finite element analysis, Gas turbines, Machine learning, Temperature, Turbine blades