FMAR Net: 3D reconstruction of flame temperature and soot volume fraction

Flame 3D reconstruction is an important research direction in the field of combustion diagnosis, which has significant value for studying and understanding combustion laws and promoting the practical application of combustion phenomena. In the field of flame 3D reconstruction, traditional algebraic solving methods may gradually accumulate errors during the solving process, while the universality of current deep learning reconstruction methods still needs to be improved. This article designs FMAR network based on multi-angle channels architecture for three-dimensional reconstruction of flame temperature and soot volume fraction. The experimental results show that FMAR network has achieved the effect of releasing input angle constraint and enhancing the universality, while still maintaining the advantage of high accuracy of deep learning methods.