To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames. By modeling the luminous process of flames and utilizing 2D projection images for supervision, our experimental validation shows that this model achieves an average structural similarity index of 0.96 between actual images and predicted 2D projections, along with a Peak Signal-to-Noise Ratio of 39.05. Additionally, it saves approximately 34 times the computation time and about 10 times the memory compared to traditional algorithms.
为解决传统 ART 算法在火焰燃烧诊断中耗时且计算量大的问题，受火焰仿真技术的启发，我们提出了一种新颖的火焰表示方法。通过对火焰发光过程建模并利用二维投影图像进行监督，我们的实验验证表明，该模型在实际图像与预测二维投影之间实现了平均结构相似性指数（SSIM）为 0.96，以及峰值信噪比（PSNR）为 39.05。此外，与传统算法相比，该方法计算时间减少约 34 倍，内存需求降低约 10 倍。