Session: 12-11 Code Development

Paper Number: 82507

82507 - Predictive Modelling of Local Film-Cooling Flow on a Turbine Rotor Blade 

In the turbine section of a modern gas turbine engine, components exposed to the main gas path flow rely on cooling air to maintain hardware durability targets. Therefore, monitoring turbine cooling flow is essential to the diagnostic and prognostic efficacy of a condition-based operation and maintenance (CBOM) approach. This study supports CBOM goals by leveraging supervised machine learning to estimate local film-cooling flow rate using surface temperature measured on the pressure side of a rotating turbine blade operating at engine-relevant aerothermal conditions. Throughout the lifetime of a film-cooled turbine component, characteristics of the film-cooling flow (film trajectory, cooling effectiveness, etc.) vary as degradation-driven geometry variations occur, which ultimately affects the relationship between the model input – blade surface temperature – and the model output – local film-cooling flow rate. The present study addresses this complication by testing a data-driven model on multiple turbine blades of the same nominal design, each exhibiting different localized film-cooling flow characteristics. By testing the model in this manner, strategies for mitigating the detrimental effects of film-cooling flow characteristic variations on model performance were investigating, and the corresponding flow rate prediction accuracy is quantified. Results show that by defining feature locations relative to the coolant trajectory, the prediction error for cases that exhibit large variations of film-cooling characteristics can be minimized.

Presenting Author: Eric Deshong Pennsylvania State University

Presenting Author Biography: Eric DeShong is a Ph.D. candidate from the Department of Mechanical Engineering at Penn State University. Eric earned his undergraduate degree in mechanical engineering from Penn State in 2017 before beginning his graduate work at the PSU-START turbine testing facility. His focus is on applying thermo-fluid and data analytics principles to the improvement of gas turbine technology.

Authors:

Eric Deshong Pennsylvania State University
Reid Berdanier Pennsylvania State University
Karen Thole Pennsylvania State University