Comparison of different nonlinear solvers for 2D time-implicit stellar hydrodynamics

Time-implicit schemes are attractive since they allow numerical time steps that are much larger than those permitted by the CourantFriedrich-Lewy criterion characterizing time-explicit methods. This advantage comes, however, at a cost: the solution of a system of nonlinear equations is required at each time step. In this work, the nonlinear system results from the discretization of the hydrodynamical equations with the Crank-Nicholson scheme. We compare the cost of different methods, based on Newton-Raphson iterations, to solve this nonlinear system, and benchmark their performances against time-explicit schemes. Since our general scientific objective is to model stellar interiors, we use as test cases two realistic models for the convective envelope of a red giant and a young Sun. Focusing on 2D simulations, we show that the best performances are obtained with the quasi-Newton method proposed by Broyden. Another important concern is the accuracy of implicit calculations. Based on the study of an idealized problem, namely the advection of a single vortex by a uniform flow, we show that there are two aspects: i) the nonlinear solver has to be accurate enough to resolve the truncation error of the numerical discretization; and ii) the time step has be small enough to resolve the advection of eddies. We show that with these two conditions fulfilled, our implicit methods exhibit similar accuracy to time-explicit schemes, which have lower values for the time step and higher computational costs. Finally, we discuss in the conclusion the applicability of these methods to fully implicit 3D calculations.

[1]  Alex Pothen,et al.  What Color Is Your Jacobian? Graph Coloring for Computing Derivatives , 2005, SIAM Rev..

[2]  K. Kifonidis,et al.  On multigrid solution of the implicit equations of hydrodynamics - Experiments for the compressible Euler equations in general coordinates , 2012 .

[3]  Andreas Griewank,et al.  Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.

[4]  Henk A. van der Vorst,et al.  Bi-CGSTAB: A Fast and Smoothly Converging Variant of Bi-CG for the Solution of Nonsymmetric Linear Systems , 1992, SIAM J. Sci. Comput..

[5]  Gerard L. G. Sleijpen,et al.  Stability control for approximate implicit timestepping schemes with minimal residual iterations , 1999 .

[6]  Patrick Amestoy,et al.  Hybrid scheduling for the parallel solution of linear systems , 2006, Parallel Comput..

[7]  Wolfgang Gentzsch,et al.  High-Performance Computing and Networking , 1994, Lecture Notes in Computer Science.

[8]  Patrick Amestoy,et al.  A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling , 2001, SIAM J. Matrix Anal. Appl..

[9]  R. LeVeque Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-Dependent Problems (Classics in Applied Mathematics Classics in Applied Mathemat) , 2007 .

[10]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[11]  Tamara G. Kolda,et al.  An overview of the Trilinos project , 2005, TOMS.

[12]  Randall J. LeVeque,et al.  Finite difference methods for ordinary and partial differential equations - steady-state and time-dependent problems , 2007 .

[13]  D. Keyes,et al.  Jacobian-free Newton-Krylov methods: a survey of approaches and applications , 2004 .

[14]  I. Baraffe,et al.  Towards a new generation of multi-dimensional stellar evolution models: development of an implicit hydrodynamic code , 2011, 1103.1524.

[15]  Y. Saad,et al.  GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems , 1986 .

[16]  H. C. Yee,et al.  Entropy Splitting and Numerical Dissipation , 2000 .

[17]  M. Powell,et al.  On the Estimation of Sparse Jacobian Matrices , 1974 .

[18]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[19]  E. Sturler,et al.  Multilevel sparse approximate inverse preconditioners for adaptive mesh refinement , 2009 .

[20]  C. G. Broyden A Class of Methods for Solving Nonlinear Simultaneous Equations , 1965 .