2D-supervised fast neural fluid reconstruction technique for time-resolved volumetric flame reconstruction

Abstract

The volumetric tomography reconstruction technique (VTRT) can calculate and restore the three-dimensional physical field based on the multi-view projection information of the flame and similar flow fields, which plays an important role in the research of volumetric targets. However, the reconstruction of three-dimensional scalar/vector fields representing the physical information of the flow field (temperature, density, chemical composition, etc.) requires a significant amount of computational resources and time, especially in the turbulent flame flow field, which requires high spatial and temporal resolutions. Based on the said background, in this work, we proposed a Fast Neural Fluid Reconstruction Technique (Fast-NFRT) based on deep learning. In this investigation, we first test the reconstruction accuracy, speed, and noise robustness of Fast-NFRT using numerically simulated flames. Then Fast-NFRT is used to reconstruct the experimental turbulent jet flame under two different conditions. Finally, with the camera settings preserved, the Fast-NFRT model was tested for transfer learning between the numerical simulation flame and the two experimental jet flames to examine the generalization performance of the reconstruction. It is found that the proposed Fast-NFRT model can achieve a temporal resolution of 50-500 fps with a similar reconstruction fidelity to traditional algebraic reconstruction methods, which demonstrates the capacity of the Fast-NFRT model and its potential in real-time reconstruction applications and dynamic analysis for complex flow dynamics.