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A Markov Matrix Iterative Splitting Algorithm for Incompressible Flow
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DOI：10.1016/j.compfluid.2025.106943.png)
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Abstract
En 中文
• Developed and tested a novel Monte Carlo Method for the Incompressible Navier-Stokes Equations • The method is efficient and highly parallelizable, with error dropping linearly and CPU time scaling (almost) linearly with spatial resolution. • Operator-splitting with Markov matrices yields an accurate and versatile Monte Carlo Method. • Considering a Multi-Level Monte Carlo approach accelerates steady-state convergence. • Non-conforming obstacles are handled by tagging some Random Walkers within the mask.
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3
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333
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