A fast, robust, and simple implicit method for adaptive time-stepping on adaptive mesh-refinement grids

被引:0
|
作者
机构
[1] [1,Commerçon, B.
[2] Debout, V.
[3] Teyssier, R.
来源
| 1600年 / EDP Sciences卷 / 563期
关键词
Context. Implicit solvers present strong limitations when used on supercomputing facilities and in particular for adaptive mesh-refinement codes. Aims. We present a new method for implicit adaptive time-stepping on adaptive mesh-refinement grids. We implement it in the radiation-hydrodynamics solver we designed for the RAMSES code for astrophysical purposes and; more particularly; for protostellar collapse. Methods. We briefly recall the radiation- hydrodynamics equations and the adaptive time-stepping methodology used for hydrodynamical solvers. We then introduce the different types of boundary conditions (Dirichlet; Neumann; and Robin) that are used at the interface between levels and present our implementation of the new method in the RAMSES code. The method is tested against classical diffusion and radiation- hydrodynamics tests; after which we present an application for protostellar collapse. Results. We show that using Dirichlet boundary conditions at level interfaces is a good compromise between robustness and accuracy and that it can be used in structure formation calculations. The gain in computational time over our former unique time step method ranges from factors of 5 to 50 depending on the level of adaptive time-stepping and on the problem. We successfully compare the old and new methods for protostellar collapse calculations that involve highly non linear physics. Conclusions. We have developed a simple but robust method for adaptive time-stepping of implicit scheme on adaptive mesh-refinement grids. It can be applied to a wide variety of physical problems that involve diffusion processes. © ESO; 2014;
D O I
暂无
中图分类号
学科分类号
摘要
Journal article (JA)
引用
收藏
相关论文
共 50 条
  • [31] An adaptive BDF2 implicit time-stepping method for the no-slope-selection epitaxial thin film model
    Meng, Xiangjun
    Zhang, Zhengru
    COMPUTATIONAL & APPLIED MATHEMATICS, 2023, 42 (03):
  • [32] An Implicit Scheme for Ohmic Dissipation with Adaptive Mesh Refinement
    Matsumoto, Tomoaki
    PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF JAPAN, 2011, 63 (02) : 317 - 323
  • [33] IMPLICIT ADAPTIVE MESH REFINEMENT FOR DISPERSIVE TSUNAMI PROPAGATION
    Berger, Marsha J.
    Leveque, Randall J.
    SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2024, 46 (04): : B554 - B578
  • [34] A stochastic Galerkin method with adaptive time-stepping for the Navier-Stokes equations
    Sousedik, Bedrich
    Price, Randy
    JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 468
  • [35] Strong convergence of an adaptive time-stepping Milstein method for SDEs with monotone coefficients
    Cónall Kelly
    Gabriel J. Lord
    Fandi Sun
    BIT Numerical Mathematics, 2023, 63
  • [36] A Linear Adaptive time-stepping Method for Solving Vibration Problems with Damping Terms
    Huang, Jianguo
    Sheng, Huashan
    JOURNAL OF COMPUTATIONAL ANALYSIS AND APPLICATIONS, 2017, 23 (03) : 562 - 575
  • [37] An Adaptive Local Time-Stepping Method Applied to Storm Surge Inundation Simulation
    Yu, Pubing
    Ji, Tao
    Wu, Xiuguang
    Chen, Yifan
    Liu, Guilin
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2025, 13 (03)
  • [38] AN EFFICIENT AND ACCURATE ADAPTIVE TIME-STEPPING METHOD FOR THE BLACK-SCHOLES EQUATIONS
    Hwang, Hyeongseok
    Kwak, Soobin
    Nam, Yunjae
    Ham, Seokjun
    Li, Zhengang
    Kim, Hyundong
    Kim, Junseok
    JOURNAL OF THE KOREAN SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS, 2024, 28 (03) : 88 - 95
  • [39] Numerical simulation of diffusion MRI signals using an adaptive time-stepping method
    Li, Jing-Rebecca
    Calhoun, Donna
    Poupon, Cyril
    Le Bihan, Denis
    PHYSICS IN MEDICINE AND BIOLOGY, 2014, 59 (02): : 441 - 454
  • [40] An Efficient and Accurate Adaptive Time-Stepping Method for the Landau-Lifshitz Equation
    Kim, Hyundong
    Kwak, Soobin
    Mohammed, Moumni
    Kang, Seungyoon
    Ham, Seokjun
    Kim, Junseok
    ALGORITHMS, 2025, 18 (01)