A Novel Temperature Reconstruction Method for Acoustic Pyrometry Under Strong Temperature Gradients and Limited Measurement Points

Acoustic pyrometry (AP) is a promising methodology for high-quality temperature field reconstruction, which is widely used in the monitoring of atmosphere, room, and furnace. However, most of the existing acoustic reconstruction algorithms are developed and utilized in relatively uniform temperature distributions. Furthermore, their ability of tracking hotspots are rarely discussed. This paper first proposed the coefficient of heating effect (C_HE) to quantitatively assess the intrinsic characteristics of the reconstructed temperature field. Aiming to accurately reconstruct the temperature fields under strong gradients and limited measurement points, this paper presents a novel temperature reconstruction method based on the adaptive hybrid kernel (AHK) and the adaptive grid evolution strategy (AGES). The proposed AGES-AHK method implements adaptive hybrid kernel adjustments on AGES-optimized nonuniform grids, achieving significant improvements in both reconstruction fidelity and hotspot characterization. The reconstruction results show that at C_HE levels below 15, the AGES-AHK method achieved the normalized root mean square error (NRMSE) of less than 3.7%, the hotspot position deviation D_h of less than 2.3% and the hotspot temperature error E_h of less than 15%, improving reconstruction accuracy by more than 33% compared to the basis method. Qualitative and quantitative analyses demonstrate the AGES-AHK method’s superior performance in challenging conditions.