Stochastic domain decomposition for time dependent adaptive mesh generation

The efficient generation of meshes is an important component in the numerical solution of problems in physics and engineering. Of interest are situations where global mesh quality and a tight coupling to the solution of the physical partial differential equation (PDE) is important. We consider parabolic PDE mesh generation and present a method for the construction of adaptive meshes in two spatial dimensions using stochastic domain decomposition that is suitable for an implementation in a multi- or many-core environment. Methods for mesh generation on periodic domains are also provided. The mesh generator is coupled to a time dependent physical PDE and the system is evolved using an alternating solution procedure. The method uses the stochastic representation of the exact solution of a parabolic linear mesh generator to find the location of an adaptive mesh along the (artificial) subdomain interfaces. The deterministic evaluation of the mesh over each subdomain can then be obtained completely independently using the probabilistically computed solutions as boundary conditions. The parallel performance of this general stochastic domain decomposition approach has previously been shown. We demonstrate the approach numerically for the mesh generation context and compare the mesh obtained with the corresponding single domain mesh using a representative mesh quality measure.

[1]  T. Tang,et al.  Simulating three-dimensional free surface viscoelastic flows using moving finite difference schemes , 2010 .

[2]  S. Aachen Stochastic Differential Equations An Introduction With Applications , 2016 .

[3]  George Beckett,et al.  A moving mesh finite element method for the two-dimensional Stefan problems , 2001 .

[4]  Ronald D. Haynes,et al.  Generating Equidistributed Meshes in 2D via Domain Decomposition , 2013, ArXiv.

[5]  Peter K. Jimack,et al.  A Moving-Mesh Finite Element Method and its Application to the Numerical Solution of Phase-Change Problems , 2013 .

[6]  Ronald D. Haynes,et al.  A Stochastic Domain Decomposition Method for Time Dependent Mesh Generation , 2016 .

[7]  Juan A. Acebrón,et al.  A New Probabilistic Approach to the Domain Decomposition Method , 2007 .

[8]  William H. Press,et al.  Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .

[9]  Ioannis Karatzas,et al.  Brownian Motion and Stochastic Calculus , 1987 .

[10]  Xiaoyong Zhan,et al.  Adaptive Moving Mesh Modeling for Two Dimensional Groundwater Flow and Transport Weizhang Huang and , 2004 .

[11]  Carl de Boor,et al.  Good approximation by splines with variable knots + , 2011 .

[12]  Martin J. Gander,et al.  Domain Decomposition Approaches for Mesh Generation via the Equidistribution Principle , 2012, SIAM J. Numer. Anal..

[13]  J. A. White,et al.  On Selection of Equidistributing Meshes for Two-Point Boundary-Value Problems , 1979 .

[14]  S. Kakutani 131. On Brownian Motions in n-Space , 1944 .

[15]  Huazhong Tang,et al.  An adaptive moving mesh method for two-dimensional relativistic magnetohydrodynamics , 2012 .

[16]  M. V. Tretyakov,et al.  Stochastic Numerics for Mathematical Physics , 2004, Scientific Computation.

[17]  Changqiu Jin,et al.  A Three Dimensional Gas-Kinetic Scheme with Moving Mesh for Low-Speed Viscous Flow Computations , 2010 .

[18]  YIRANG YUAN,et al.  THE CHARACTERISTIC FINITE ELEMENT ALTERNATING-DIRECTION METHOD WITH MOVING MESHES FOR THE TRANSIENT BEHAVIOR OF A SEMICONDUCTOR DEVICE , 2011 .

[19]  陈荣亮 Parallel one-shot Lagrange-Newton-Krylov-Schwarz algorithms for shape optimization of steady incompressible flows , 2012 .

[20]  Weizhang Huang,et al.  A two-dimensional moving finite element method with local refinement based on a posteriori error estimates , 2003 .

[21]  A. M. Winslow Numerical Solution of the Quasilinear Poisson Equation in a Nonuniform Triangle Mesh , 1997 .

[22]  Tao Tang,et al.  An adaptive mesh redistribution algorithm for convection-dominated problems , 2002 .

[23]  Two . dimensional Brownian Motion and Harmonic Functions , 2022 .

[24]  H. G. Burchard,et al.  Splines (with optimal knots) are better , 1974 .

[25]  Grant D. Lythe,et al.  Exponential Timestepping with Boundary Test for Stochastic Differential Equations , 2003, SIAM J. Sci. Comput..

[26]  Juan Torres A New Probabilistic Approach to the Domain Decomposition Method , 2007, CSE 2007.

[27]  S. Kakutani 143. Two-dimensional Brownian Motion and Harmonic Functions , 1944 .

[28]  Chris J. Budd,et al.  Monge-Ampére based moving mesh methods for numerical weather prediction, with applications to the Eady problem , 2013, J. Comput. Phys..

[29]  Shaoping Quan,et al.  Simulations of multiphase flows with multiple length scales using moving mesh interface tracking with adaptive meshing , 2011, J. Comput. Phys..

[30]  Piero Lanucara,et al.  Domain Decomposition Solution of Elliptic Boundary-Value Problems via Monte Carlo and Quasi-Monte Carlo Methods , 2005, SIAM J. Sci. Comput..

[31]  Robert D. Russell,et al.  Anr-Adaptive Finite Element Method Based upon Moving Mesh PDEs , 1999 .

[32]  Ronald D. Haynes,et al.  Parallel stochastic methods for PDE based grid generation , 2013, Comput. Math. Appl..

[33]  Robert D. Russell,et al.  Adaptive Moving Mesh Methods , 2010 .