Fast optimization-based conservative remap of scalar fields through aggregate mass transfer

Abstract We develop a fast, efficient and accurate optimization-based algorithm for the high-order conservative and local-bound preserving remap (constrained interpolation) of a scalar conserved quantity between two close meshes with the same connectivity. The new formulation is as robust and accurate as the flux-variable flux-target optimization-based remap (FVFT-OBR) [1] , [2] yet has the computational efficiency of an explicit remapper. The coupled system of linear inequality constraints, resulting from the flux form of remap, is the main efficiency bottleneck in FVFT-OBR. While advection-based remappers use the flux form to directly enforce mass conservation, the optimization setting allows us to treat mass conservation as one of the constraints. To take advantage of this fact, we consider an alternative mass-variable mass-target (MVMT-OBR) formulation in which the optimization variables are the net mass updates per cell and a single linear constraint enforces the conservation of mass. In so doing we change the structure of the OBR problem from a global linear-inequality constrained QP to a singly linearly constrained QP with simple bounds. Using the structure of the MVMT-OBR problem, and the fact that in remap the old and new grids are close, we are able to develop a simple, efficient and easily parallelizable optimization algorithm for the primal MVMT-OBR QP. Numerical studies on a variety of affine and non-affine grids confirm that MVMT-OBR is as accurate and robust as FVFT-OBR, but has the same computational cost as the explicit, state-of-the-art FCR.

[1]  Roger Fletcher,et al.  New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds , 2006, Math. Program..

[2]  Mikhail Shashkov,et al.  Constrained-Optimization Based Data Transfer : A New Perspective on Flux Correction , 2011 .

[3]  Raphaël Loubère,et al.  The repair paradigm: New algorithms and applications to compressible flow , 2006 .

[4]  Laurie A. Hulbert,et al.  A Globally and Superlinearly Convergent Algorithm for Convex Quadratic Programs with Simple Bbounds , 1993, SIAM J. Optim..

[5]  Pavel B. Bochev,et al.  Constrained-Optimization Based Data Transfer , 2012 .

[6]  J. Peponis Formulation , 1997, Karaite Marriage Contracts from the Cairo Geniza.

[7]  Pavel Váchal,et al.  Optimization-based synchronized flux-corrected conservative interpolation (remapping) of mass and momentum for arbitrary Lagrangian-Eulerian methods , 2010, J. Comput. Phys..

[8]  Len G. Margolin,et al.  Second-order sign-preserving conservative interpolation (remapping) on general grids , 2003 .

[9]  P. Colella Multidimensional upwind methods for hyperbolic conservation laws , 1990 .

[10]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[11]  Shirley Dex,et al.  JR 旅客販売総合システム(マルス)における運用及び管理について , 1991 .

[12]  C. W. Hirt,et al.  An Arbitrary Lagrangian-Eulerian Computing Method for All Flow Speeds , 1997 .

[13]  J. S. Peery,et al.  Multi-Material ALE methods in unstructured grids , 2000 .

[14]  Pavel B. Bochev,et al.  Formulation, analysis and numerical study of an optimization-based conservative interpolation (remap) of scalar fields for arbitrary Lagrangian-Eulerian methods , 2011, J. Comput. Phys..

[15]  Raphaël Loubère,et al.  A subcell remapping method on staggered polygonal grids for arbitrary-Lagrangian-Eulerian methods , 2005 .

[16]  D. Benson Computational methods in Lagrangian and Eulerian hydrocodes , 1992 .

[17]  R. LeVeque High-resolution conservative algorithms for advection in incompressible flow , 1996 .