Three-Dimensional Temperature Field Reconstruction Based on Tucker Decomposition and Acoustic Thermometry

Accurate temperature measurement in coal-fired power plants is crucial for optimizing combustion and achieving deep load regulation. While acoustic temperature measurement is an efficient and stable method, its practical application is limited to two-dimensional (2D) temperature fields, leading to poor reconstruction of complex 3D temperature fields due to limited measurement points. In this work, we propose a novel 3D temperature field reconstruction algorithm based on Tucker decomposition and acoustic thermometry. The key innovation lies in the use of Tucker decomposition to extract essential features from 3D time-of-flight (TOF) data, enabling efficient reconstruction of 3D temperature fields from a small number of single-layer TOF measurements. Our method achieves faster reconstruction speeds (approximately 4 s) and higher accuracy, reducing reconstruction errors by over 10% compared to traditional acoustic thermometry. Additionally, the algorithm demonstrates strong anti-noise capabilities and applicability to temperature fields beyond the a priori conditions, making it a valuable tool for combustion optimization and load adjustment in coal-fired power plants.