Fire in SRRN: Next-Gen 3D Temperature Field Reconstruction Technology

TL;DR

This paper presents SRRN, a novel 3D temperature field reconstruction method using differentiable rendering and neural networks, achieving high accuracy and robustness in simulated and real flame experiments.

Contribution

Introduces SRRN, a new neural network-based approach for 3D temperature field reconstruction utilizing differentiable rendering and flame radiation characteristics.

Findings

Maximum RMS error of 10.17 in simulations

Maximum relative error of 4.86% in real flame experiment

Robustness to Gaussian and salt-and-pepper noise

Abstract

In aerospace and energy engineering, accurate 3D combustion field temperature measurement is critical. The resolution of traditional methods based on algebraic iteration is limited by the initial voxel division. This study introduces a novel method for reconstructing three-dimensional temperature fields using the Spatial Radiation Representation Network (SRRN). This method utilizes the flame thermal radiation characteristics and differentiable rendering in graphics, and combines it with a multi-layer perceptron to achieve a functional representation of the flame temperature field. The effectiveness of SRRN is evaluated through simulated temperature field reconstruction experiments with different levels of complexity. The maximum root mean square error is 10.17, which proves the robustness of the algorithm to Gaussian noise and salt-and-pepper noise. We conducted a butane flame temperature field reconstruction experiment, and the maximum relative error between the reconstruction result and the thermocouple measurement value was 4.86%, confirming that the algorithm can achieve accurate reconstruction.