Research On Petroleum Spill Simulation Rendering Based On Deep Learning And Smooth Particles

Petroleum is one of the main energy sources in modern society.With the development of social economy,the demand for petroleum increases sharply.However,during the transportation of petroleum products,various oil spill accidents occur frequently,which seriously endangers the natural environment and has a huge impact on the national economy.Therefore,high-precision simulation of the spread of crude oil pollution and rapid formulation of emergency rescue plans are of great significance for ensuring the safety of people’s lives and property.Aiming at the problem that the existing crude oil leakage simulation methods in mountainous areas cannot show the diffusion effect of oil leakage in line with the physical process,this paper firstly proposes a calculation model Fay-SPH,which combines the Fay crude oil diffusion model and the SPH method,to ensure the physical process properties of crude oil leakage.At the same time,SPH is used to show the details of fluid motion.Secondly,to further improve the simulation quality of crude oil fluids,a divergence-free solver based on the Fay model is proposed.This method integrates the Fay model into the pressure solver of the SPH method,and improves the simulation accuracy of the leaked crude oil by accurately solving the physical properties of the crude oil particles.Finally,the above algorithm is used to simulate and compare the leakage and diffusion patterns of oil products on the mountain.The experimental results show that the method in this paper realizes the high-precision simulation of the leakage and diffusion of crude oil on the mountain.Aiming at the problems of long simulation time and slow calculation speed in the simulation of fluid based on the SPH method,this paper proposes a continuous convolutional network Fay Net based on Fay constraints to speed up the simulation.In order to ensure the simulation quality while improving the simulation speed,a lightweight attention module FGAB and triple attention module FTAB are proposed to enhance the ability of the network to extract the feature information of crude oil particles.Through the analysis and research of crude oil leakage simulation experiments and ablation experiments on different terrains,the efficiency of this method in simulating oil leakage is verified.Finally,combined with the functional requirements of the actual project,this paper designs and implements a simulation system for oil leakage and diffusion.The experiment shows the overall function and operation process of the system,which can realize high-precision simulation of the leakage and diffusion process of crude oil,and meet the business requirements of the system.