Cost reduction for data acquisition based on data fusion: Reconstructing the surface temperature of a turbine blade

Abstract

Turbine cooling is an effective way to improve the comprehensive performance and service life of gas turbines. In recent decades, there has been rapid growth in research into external cooling and internal cooling methods. As a result, there is a significant amount of experimental and numerical data. However, due to their multi-source nature, the datasets have different degrees of fidelity and different data structures, which hinder the effective use of the data. Besides, high-fidelity (HF) data often have high acquisition costs, which hinder their application in aerospace. A novel form of data fusion is introduced in this paper. We integrate multi-source data using special algorithms to produce more reliable data. A deep-learning neural network with the PointNet architecture is designed to establish two surrogate models: a high-fidelity model (HF model) trained by experimental data and a low-fidelity model (LF model) based on Reynolds-averaged Navier–Stokes simulation data. Both models predict results with less than 1% reference errors compared to their respective ground truth at most data points. In addition, we explore the role of transfer learning in multi-fidelity modeling. A fusion algorithm based on a Gaussian function and a weighted average strategy is proposed to combine the values from the HF model and the LF model. The presented results show that the fusion data are more accurate than computational fluid dynamics data, successfully meeting the goal of reducing the cost of data acquisition.