Session: 13-04: Heat transfer modelling

Paper Number: 152695

Transient Temperature Field Inversion of Turbine Based on Physics-Encoded Neural Networks 

In turbomachinery design, the accurate prediction of the life cycle is one of the most challenging issues. With increasing thermal loads, thermomechanical fatigue in the turbine has become a focal point of increasing attention. To incorporate thermally induced stress in the turbine as a part of high pressure turbine heat transfer design, the primary requirement is to predict blade metal temperature accurately. However, current high-temperature measurement technology can only measure the surface temperature or the limited discrete points temperature of the turbine blade and fails to capture the temperature field of the entire metal. Traditionally, turbine designers use numerical simulation to supplement these thermal details; however, the unknown boundary conditions of the solid domain and complicated flow field lead to a lack of fidelity in the results. Therefore, the industry has been continuously seeking mathematical tools to address this problem. The present study proposed a deep learning method based on a physics-encoded neural network. The proposed method can inverse the transient temperature field of the metal during the whole steady flow cycle using limited surface temperature time-series data. Three aspects of testing follow the detailed discussion on the theory of the deep learning method to verify the application and accuracy of the proposed method. A thorough comparison is conducted across different Mach numbers (Ma = 0.2, 0.5, and 0.8), varying measurement data quality (data noise and low temporal resolution), and diverse blade geometries (flat tip, squealer tip, and winglet squealer tip), with the results showing good agreement with the ground truth.

Presenting Author: Mingyang Hao Xi'an Jiaotong University

Presenting Author Biography: Mingyang Hao is Ph. D student in the Institute of Turbomachinery at Xi’an jiaotong University. His research interests are in the balde heat transfer and cooling of gas turbine.

Authors:

Mingyang Hao Xi'an Jiaotong University
Zhigang Li Xi'an Jiaotong University
Jun Li Xi'an Jiaotong University