A High-Order Level-Set Method with Enhanced Stability for Curvature Driven Flows and Surface Diffusion Motion

A high-order explicit level-set method based on the total variation diminishing Runge–Kutta method, a high-order scheme for distance computation and a smoothing scheme has been developed for simulating curvature driven flows and surface diffusion motion. This method overcomes the high-order CFL time restriction. The enhanced stability is achieved by utilizing several techniques, resulting in an accurate and smooth velocity field. In particular, the scheme for distance computation is used to reinitialize the level-set function and to extend the velocity from the zero level-set to the rest of the domain. As such, it greatly reduces the accumulated errors typically observed in the traditional PDE-based methods. The smoothing technique is used to remove the high-frequency oscillations produced by the high-order derivatives of the level-set function and is the key to the stability enhancement. A local treatment scheme was also developed which is crucial in the simulation of merging events. Results on several benchmark problems have demonstrated. Compared with some semi-implicit methods, the developed method is more accurate and has the same, if not better, stability.

[1]  J. Sethian,et al.  Motion by intrinsic Laplacian of curvature , 1999 .

[2]  Ronald Fedkiw,et al.  Chimera grids for water simulation , 2013, SCA '13.

[3]  Steven J. Ruuth,et al.  Implicit-explicit Runge-Kutta methods for time-dependent partial differential equations , 1997 .

[4]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[5]  S. Osher,et al.  Regular Article: A PDE-Based Fast Local Level Set Method , 1999 .

[6]  S. Osher,et al.  Efficient implementation of essentially non-oscillatory shock-capturing schemes,II , 1989 .

[7]  Peter Smereka,et al.  Semi-Implicit Level Set Methods for Curvature and Surface Diffusion Motion , 2003, J. Sci. Comput..

[8]  John S. Lowengrub,et al.  An improved geometry-aware curvature discretization for level set methods: Application to tumor growth , 2006, J. Comput. Phys..

[9]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[10]  M. Trujillo,et al.  Gradient augmented reinitialization scheme for the level set method , 2013 .

[11]  Gilbert Strang,et al.  The Discrete Cosine Transform , 1999, SIAM Rev..

[12]  Steven J. Ruuth Efficient Algorithms for Diffusion-Generated Motion by Mean Curvature , 1998 .

[13]  J. A. Sethian,et al.  Fast Marching Methods , 1999, SIAM Rev..

[14]  Jean-Michel Morel,et al.  Introduction To The Special Issue On Partial Differential Equations And Geometry-driven Diffusion In Image Processing And Analysis , 1998, IEEE Trans. Image Process..

[15]  J. Sethian Curvature and the evolution of fronts , 1985 .

[16]  R. Saye High-order methods for computing distances to implicitly defined surfaces , 2014 .

[17]  J. Bruchon,et al.  3D finite element simulation of the matter flow by surface diffusion using a level set method , 2011 .

[18]  S. Osher,et al.  Computing interface motion in compressible gas dynamics , 1992 .

[19]  M. Buckley Fast computation of a discretized thin-plate smoothing spline for image data , 1994 .

[20]  Howard L. Weinert,et al.  Efficient computation for Whittaker-Henderson smoothing , 2007, Comput. Stat. Data Anal..

[21]  Ebrahim M. Kolahdouz,et al.  A Semi-implicit Gradient Augmented Level Set Method , 2012, SIAM J. Sci. Comput..

[22]  Wei Lu,et al.  A Local Semi-Implicit Level-Set Method for Interface Motion , 2008, J. Sci. Comput..

[23]  David L. Chopp,et al.  Some Improvements of the Fast Marching Method , 2001, SIAM J. Sci. Comput..

[24]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[25]  Åsmund Ervik,et al.  A robust method for calculating interface curvature and normal vectors using an extracted local level set , 2014, J. Comput. Phys..

[26]  Steven J. Ruuth A Diffusion-Generated Approach to Multiphase Motion , 1998 .

[27]  Karl Yngve Lervåg Calculation of interface curvature with the level-set method , 2014 .

[28]  David Salac,et al.  The Augmented Fast Marching Method for Level Set Reinitialization , 2011, 1111.6903.

[29]  Harald Garcke,et al.  A parametric finite element method for fourth order geometric evolution equations , 2007, J. Comput. Phys..

[30]  Francis Filbet,et al.  High Order Semi-implicit Schemes for Time Dependent Partial Differential Equations , 2016, Journal of Scientific Computing.

[31]  D. Chopp Computing Minimal Surfaces via Level Set Curvature Flow , 1993 .

[32]  Martin D. Fox,et al.  Erratum: Distance regularized level set evolution and its application to image segmentation (IEEE Transactions on Image Processingvol (2010) 19:12 (3243-3254)) , 2011 .

[33]  Selim Esedoglu,et al.  Fast and Accurate Redistancing by Directional Optimization , 2014, SIAM J. Sci. Comput..

[34]  David Zhang,et al.  Reinitialization-Free Level Set Evolution via Reaction Diffusion , 2011, IEEE Transactions on Image Processing.

[35]  Damien Garcia,et al.  Robust smoothing of gridded data in one and higher dimensions with missing values , 2010, Comput. Stat. Data Anal..

[36]  Huajian Gao,et al.  A Numerical Study of Electro-migration Voiding by Evolving Level Set Functions on a Fixed Cartesian Grid , 1999 .

[37]  Jing-Song Pan,et al.  Finite element formulation of coupled grain-boundary and surface diffusion with grain-boundary migration , 1997, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[38]  S. Osher,et al.  A level set approach for computing solutions to incompressible two-phase flow , 1994 .

[39]  J. Lowengrub,et al.  Evolving interfaces via gradients of geometry-dependent interior Poisson problems: application to tumor growth , 2005 .