Non-Newtonian fluid simulation and reconstruction from monocular videos

Abstract

In fluid simulation, the parameters of the constitutive model are difficult to predict accurately, which leads to the inconsistency between the simulation results and the visual effects of monocular videos. To solve this problem, a monocular video-guided non-Newtonian fluid reconstruction method is proposed. The model takes non-Newtonian fluid simulation videos as input for training and learns the best low-dimensional latent space representation of a single frame of fluid simulation images. Inter-frame prediction is then performed in this latent domain, employing a convolutional long–short-term memory network to predict latent vector representations of future frames. Finally, the reconstruction parameters of the constitutive model are predicted based on the frame-by-frame latent representation encoding and inter-frame temporal features. In the model verification stage, the real video of the non-Newtonian fluid is used as the input to predict the parameters of the fluid constitutive model, and realize the simulation and reconstruction of the non-Newtonian fluid based on the Cross model. Experimental results showed that the video-guided simulation reconstruction method could obtain fluid flow phenomena that were more consistent with those in the real video than the reconstruction method based on rheometer measurements. Furthermore, it provided higher pixel accuracy and precision at different times and visual effects that were more consistent with the actual fluid flow.