Dynamic Importance Monte Carlo SPH Vortical Flows With Lagrangian Samples
Authors: Ye, X., Wang, X., Xu, Y., Telea, A.C., Kosinka, J., You, L., Zhang, J.J., Chang, J.
Journal: IEEE Transactions on Visualization and Computer Graphics
Publication Date: 01/01/2025
Volume: 31
Issue: 12
Pages: 10652-10666
eISSN: 1941-0506
ISSN: 1077-2626
DOI: 10.1109/TVCG.2025.3612190
Abstract:We present a Lagrangian dynamic importance Monte Carlo method without non-trivial random walks for solving the Velocity-Vorticity Poisson Equation (VVPE) in Smoothed Particle Hydrodynamics (SPH) for vortical flows. Key to our approach is the use of the Kinematic Vorticity Number (KVN) to detect vortex cores and to compute the KVN-based importance of each particle when solving the VVPE. We use Adaptive Kernel Density Estimation (AKDE) to extract a probability density distribution from the KVN for the the Monte Carlo calculations. Even though the distribution of the KVN can be non-trivial, AKDE yields a smooth and normalized result which we dynamically update at each time step. As we sample actual particles directly, the Lagrangian attributes of particle samples ensure that the continuously evolved KVN-based importance, modeled by the probability density distribution extracted from the KVN by AKDE, can be closely followed. Our approach enables effective vortical flow simulations with significantly reduced computational overhead and comparable quality to the classic Biot-Savart law that in contrast requires expensive global particle querying.
Source: Scopus
Dynamic Importance Monte Carlo SPH Vortical Flows With Lagrangian Samples.
Authors: Ye, X., Wang, X., Xu, Y., Telea, A.C., Kosinka, J., You, L., Zhang, J.J., Chang, J.
Journal: IEEE Trans Vis Comput Graph
Publication Date: 12/2025
Volume: 31
Issue: 12
Pages: 10652-10666
eISSN: 1941-0506
DOI: 10.1109/TVCG.2025.3612190
Abstract:We present a Lagrangian dynamic importance Monte Carlo method without non-trivial random walks for solving the Velocity-Vorticity Poisson Equation (VVPE) in Smoothed Particle Hydrodynamics (SPH) for vortical flows. Key to our approach is the use of the Kinematic Vorticity Number (KVN) to detect vortex cores and to compute the KVN-based importance of each particle when solving the VVPE. We use Adaptive Kernel Density Estimation (AKDE) to extract a probability density distribution from the KVN for the the Monte Carlo calculations. Even though the distribution of the KVN can be non-trivial, AKDE yields a smooth and normalized result which we dynamically update at each time step. As we sample actual particles directly, the Lagrangian attributes of particle samples ensure that the continuously evolved KVN-based importance, modeled by the probability density distribution extracted from the KVN by AKDE, can be closely followed. Our approach enables effective vortical flow simulations with significantly reduced computational overhead and comparable quality to the classic Biot-Savart law that in contrast requires expensive global particle querying.
Source: PubMed
Dynamic Importance Monte Carlo SPH Vortical Flows With Lagrangian Samples
Authors: Ye, X., Wang, X., Xu, Y., Telea, A.C., Kosinka, J., You, L., Zhang, J.J., Chang, J.
Journal: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Publication Date: 12/2025
Volume: 31
Issue: 12
Pages: 10652-10666
eISSN: 1941-0506
ISSN: 1077-2626
DOI: 10.1109/TVCG.2025.3612190
Source: Web of Science
Dynamic Importance Monte Carlo SPH Vortical Flows With Lagrangian Samples.
Authors: Ye, X., Wang, X., Xu, Y., Telea, A.C., Kosinka, J., You, L., Zhang, J.J., Chang, J.
Journal: IEEE transactions on visualization and computer graphics
Publication Date: 12/2025
Volume: 31
Issue: 12
Pages: 10652-10666
eISSN: 1941-0506
ISSN: 1077-2626
DOI: 10.1109/tvcg.2025.3612190
Abstract:We present a Lagrangian dynamic importance Monte Carlo method without non-trivial random walks for solving the Velocity-Vorticity Poisson Equation (VVPE) in Smoothed Particle Hydrodynamics (SPH) for vortical flows. Key to our approach is the use of the Kinematic Vorticity Number (KVN) to detect vortex cores and to compute the KVN-based importance of each particle when solving the VVPE. We use Adaptive Kernel Density Estimation (AKDE) to extract a probability density distribution from the KVN for the the Monte Carlo calculations. Even though the distribution of the KVN can be non-trivial, AKDE yields a smooth and normalized result which we dynamically update at each time step. As we sample actual particles directly, the Lagrangian attributes of particle samples ensure that the continuously evolved KVN-based importance, modeled by the probability density distribution extracted from the KVN by AKDE, can be closely followed. Our approach enables effective vortical flow simulations with significantly reduced computational overhead and comparable quality to the classic Biot-Savart law that in contrast requires expensive global particle querying.
Source: Europe PubMed Central