A new stacking model method to solve an inverse flow and heat coupling problem for aero-engine turbine blades

Abstract

The whole temperature distribution of a turbine blade is an important information for heat transfer design for turbine, but it is very difficult and expensive to measure the whole temperature filed, especially for the internal points of a blade. It is necessary to reconstruct the whole temperature distribution by calculating reversely unknown parameters based on sparse temperature values on the blade surface. This paper proposes a new stacking model method for solving inverse heat transfer problems of boundary conditions and it can be applied to cope with multi-input and multi-output regression problems. In order to improve the prediction performance of the stacking model, the Random Forest Regression, the XGBoost Regression and the K-Nearest Neighbors Regression are selected as the base estimators, and the Linear Regression is selected as the meta estimator. An application example for an aero-engine turbine blade is modeled and solved, in which the boundary condition parameters of the exhaust flows are reversely calculated and temperature distributions of a turbine blade surface can be obtained. Numerical experiments have demonstrated that the stacking model outperforms individual base models. This research is of considerable significance to heat transfer design, experimental research and decision support for real-time remote monitoring of aero-engines.