Real-Time Simulation Method of Flame Animation Based on the Deep Stripping Algorithm and Texture Mapping

Abstract

With the gradual integration of computers into people’s work and life, it is no longer an unattainable goal to use the power of science and technology to simulate various states of matter. In the future, virtual simulation modeling technology will become more and more high-end with the improvement of the needs of the times. However, nowadays, there are many problems in generating real-time image animation. For traditional simulation methods, huge computation and realistic rendering have become technical bottlenecks. This topic takes the generation of flame animation as an example to illustrate. On the basis of the depth stripping algorithm and texture mapping method, not only particle system modeling is introduced, but also a new N-S equation is improved for research. The research results show the following. (1) The model constructed by this method has superior performance and has been greatly improved; after iteration, the error value is as low as 0.03 and the accuracy value is as high as 98%. (2) After 500 simulations, there are 399 kinds of static and 698 kinds of dynamic flames, respectively; it takes 24.01 s and 98.21 s to generate 500 incomplete animations. (3) Compared with the particle system and deep learning model for flame animation recognition and detection, the highest false alarm rate of this model is 4.6%, and the lowest is 0.9%, and the experimental effect is stable; the highest recognition of flame animation can reach 99.2%, and the lowest is 98.5%. (4) Generally speaking, experts and ordinary people have a high evaluation on the effect of this model; except for the first group, the scores are all over 80%, and the highest score can reach 85.936%. Finally, the experimental results are good, which proves the effectiveness and feasibility of this method, which has certain research contribution value. However, for the follow-up work, the algorithm and model have room for further improvement.