arXiv Analytics

Sign in

arXiv:2411.01095 [physics.flu-dyn]AbstractReferencesReviewsResources

Advanced Theoretical Analysis of Stability and Convergence in Computational Fluid Dynamics for Computer Graphics

Rômulo Damasclin Chaves dos Santos

Published 2024-11-02Version 1

Mathematical modeling of fluid dynamics for computer graphics requires high levels of theoretical rigor to ensure visually plausible and computationally efficient simulations. This paper presents an in-depth theoretical framework analyzing the mathematical properties, such as stability, convergence, and error bounds, of numerical schemes used in fluid simulation. Conditions for stability in semi-Lagrangian and particle-based methods were derived, demonstrating that these methods remain stable under certain conditions. Furthermore, convergence rates for Navier-Stokes discretizations were obtained, showing that numerical solutions converge to analytical solutions as spatial resolution and time step decrease. Furthermore, new theoretical results were introduced on the maintenance of incompressibility and conservation of vorticity, which are crucial for the physical accuracy of simulations. The findings serve as a mathematical foundation for future research in adaptive fluid simulation, guiding the development of robust simulation techniques for real-time graphics applications.

Related articles: Most relevant | Search more
arXiv:2203.02498 [physics.flu-dyn] (Published 2022-03-04)
Computational Fluid Dynamics and Machine Learning as tools for Optimization of Micromixers geometry
arXiv:2301.09834 [physics.flu-dyn] (Published 2023-01-24)
Implementation of the Critical Wave Groups Method with Computational Fluid Dynamics and Neural Networks
arXiv:2312.14684 [physics.flu-dyn] (Published 2023-12-22)
Uncertainty Quantification in Computational Fluid Dynamics: Physics and Machine Learning Based Approaches