Optimized Runge-Kutta Methods with Automatic Step Size Control for Compressible Computational Fluid Dynamics

We develop error-control based time integration algorithms for compressible fluid dynamics (CFD) applications and show that they are efficient and robust in both the accuracy-limited and stability-limited regime. Focusing on discontinuous spectral element semidiscretizations, we design new controllers for existing methods and for some new embedded Runge-Kutta pairs. We demonstrate the importance of choosing adequate controller parameters and provide a means to obtain these in practice. We compare a wide range of error-control-based methods, along with the common approach in which step size control is based on the Courant-Friedrichs-Lewy (CFL) number. The optimized methods give improved performance and naturally adopt a step size close to the maximum stable CFL number at loose tolerances, while additionally providing control of the temporal error at tighter tolerances. The numerical examples include challenging industrial CFD applications.

[1]  D. Ketcheson,et al.  General relaxation methods for initial-value problems with application to multistep schemes , 2020, Numerische Mathematik.

[2]  David A. Kopriva,et al.  An Assessment of the Efficiency of Nodal Discontinuous Galerkin Spectral Element Methods , 2013 .

[3]  R. Löhner,et al.  Explicit two‐step Runge‐Kutta methods for computational fluid dynamics solvers , 2020, International Journal for Numerical Methods in Fluids.

[4]  David I. Ketcheson,et al.  Relaxation Runge-Kutta Methods: Conservation and Stability for Inner-Product Norms , 2019, SIAM J. Numer. Anal..

[5]  Matteo Parsani,et al.  NodePy: A package for the analysis of numerical ODE solvers , 2020, J. Open Source Softw..

[6]  Jonathan Mark Pegrum,et al.  Experimental study of the vortex system generated by a Formula 1 front wing , 2006 .

[7]  Gustaf Söderlind,et al.  Adaptive Time-Stepping and Computational Stability , 2006 .

[8]  Matthew G. Knepley,et al.  Mesh algorithms for PDE with Sieve I: Mesh distribution , 2009, Sci. Program..

[9]  Prahladh S. Iyer,et al.  Wall-modeled LES of the NASA Juncture Flow Experiment , 2020 .

[10]  John N. Shadid,et al.  Embedded pairs for optimal explicit strong stability preserving Runge-Kutta methods , 2018, J. Comput. Appl. Math..

[11]  Lisandro Dalcin,et al.  Optimized Explicit Runge-Kutta Schemes for Entropy Stable Discontinuous Collocated Methods Applied to the Euler and Navier–Stokes equations , 2020, AIAA Scitech 2021 Forum.

[12]  Desmond J. Higham,et al.  Embedded Runge-Kutta formulae with stable equilibrium states , 1990 .

[13]  David A. Kopriva,et al.  Implementing Spectral Methods for Partial Differential Equations , 2009 .

[14]  Emil M. Constantinescu,et al.  PETSc/TS: A Modern Scalable ODE/DAE Solver Library , 2018, 1806.01437.

[15]  E. Hairer,et al.  Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems , 2010 .

[16]  Charalampos Tsitouras,et al.  Runge-Kutta pairs of order 5(4) satisfying only the first column simplifying assumption , 2011, Comput. Math. Appl..

[17]  David A. Kopriva,et al.  Implementing Spectral Methods for Partial Differential Equations: Algorithms for Scientists and Engineers , 2009 .

[18]  Patrick Kofod Mogensen,et al.  Optim: A mathematical optimization package for Julia , 2018, J. Open Source Softw..

[19]  Stefano Zampini,et al.  Entropy stable h/p-nonconforming discretization with the summation-by-parts property for the compressible Euler and Navier–Stokes equations , 2019, SN Partial Differential Equations and Applications.

[20]  C. Rumsey,et al.  Goals and Status of the NASA Juncture Flow Experiment , 2016 .

[21]  Matteo Parsani,et al.  Towards an Entropy Stable Spectral Element Framework for Computational Fluid Dynamics , 2016 .

[22]  Randall J. LeVeque,et al.  A wave propagation method for three-dimensional hyperbolic conservation laws , 2000 .

[23]  Carmen Arévalo,et al.  Local error estimation and step size control in adaptive linear multistep methods , 2020, Numerical Algorithms.

[24]  Antony Jameson,et al.  A New Class of High-Order Energy Stable Flux Reconstruction Schemes , 2011, J. Sci. Comput..

[25]  Travis C. Fisher,et al.  High-order entropy stable finite difference schemes for nonlinear conservation laws: Finite domains , 2013, J. Comput. Phys..

[26]  David E. Keyes,et al.  Performance study of sustained petascale direct numerical simulation on Cray XC40 systems , 2020, Concurr. Comput. Pract. Exp..

[27]  Björn Sjögreen,et al.  High order entropy conservative central schemes for wide ranges of compressible gas dynamics and MHD flows , 2018, J. Comput. Phys..

[28]  Lawrence F. Shampine,et al.  An efficient Runge-Kutta (4,5) pair , 1996 .

[29]  Gustaf Söderlind,et al.  Time-step selection algorithms: Adaptivity, control, and signal processing , 2006 .

[30]  David E. Keyes,et al.  On the robustness and performance of entropy stable discontinuous collocation methods for the compressible Navie-Stokes equations , 2019, ArXiv.

[31]  Steven H. Frankel,et al.  Entropy Stable Spectral Collocation Schemes for the Navier-Stokes Equations: Discontinuous Interfaces , 2014, SIAM J. Sci. Comput..

[32]  Lisandro Dalcin,et al.  Relaxation Runge-Kutta Methods: Fully Discrete Explicit Entropy-Stable Schemes for the Compressible Euler and Navier-Stokes Equations , 2019, SIAM J. Sci. Comput..

[33]  R. Lewis,et al.  Low-storage, Explicit Runge-Kutta Schemes for the Compressible Navier-Stokes Equations , 2000 .

[34]  Florian R. Menter,et al.  Drag Prediction of Engine-Airframe Interference Effects with CFX-5 , 2004 .

[35]  Jesse Chan,et al.  Efficient Entropy Stable Gauss Collocation Methods , 2018, SIAM J. Sci. Comput..

[36]  Alan Edelman,et al.  Julia: A Fresh Approach to Numerical Computing , 2014, SIAM Rev..

[37]  J. Dormand,et al.  High order embedded Runge-Kutta formulae , 1981 .

[38]  Clint Dawson,et al.  Time step restrictions for Runge-Kutta discontinuous Galerkin methods on triangular grids , 2008, J. Comput. Phys..

[39]  Matteo Parsani,et al.  Optimized low-order explicit Runge-Kutta schemes for high- order spectral difference method , 2012 .

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

[41]  Björn Sjögreen,et al.  Skew-Symmetric Splitting and Stability of High Order Central Schemes , 2017 .

[42]  Martin Berzins,et al.  Adaptive Finite Volume Methods for Time-Dependent P.D.E.S. , 1995 .

[43]  Martin Berzins,et al.  Temporal Error Control for Convection-Dominated Equations in Two Space Dimensions , 1995, SIAM J. Sci. Comput..

[44]  V. Eijkhout,et al.  PETSc Users Manual (Rev. 3.13) , 2020 .

[45]  F. Krogh,et al.  Solving Ordinary Differential Equations , 2019, Programming for Computations - Python.

[46]  Kjell Gustafsson,et al.  Control theoretic techniques for stepsize selection in explicit Runge-Kutta methods , 1991, TOMS.

[47]  Qing Nie,et al.  DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia , 2017, Journal of Open Research Software.

[48]  Christopher L. Rumsey,et al.  The NASA Juncture Flow Test as a Model for Effective CFD/Experimental Collaboration , 2018, 2018 Applied Aerodynamics Conference.

[49]  Matteo Parsani,et al.  Entropy stable discontinuous interfaces coupling for the three-dimensional compressible Navier-Stokes equations , 2015, J. Comput. Phys..

[50]  L. Shampine,et al.  A 3(2) pair of Runge - Kutta formulas , 1989 .

[51]  G. Karniadakis,et al.  Spectral/hp Element Methods for Computational Fluid Dynamics , 2005 .

[52]  J. Dormand,et al.  A family of embedded Runge-Kutta formulae , 1980 .

[53]  Matteo Parsani,et al.  Optimized Explicit Runge-Kutta Schemes for the Spectral Difference Method Applied to Wave Propagation Problems , 2012, SIAM J. Sci. Comput..

[54]  David I. Ketcheson,et al.  Runge-Kutta methods with minimum storage implementations , 2010, J. Comput. Phys..

[55]  J. Kraaijevanger Contractivity of Runge-Kutta methods , 1991 .

[56]  V. Citro,et al.  Optimal explicit Runge-Kutta methods for compressible Navier-Stokes equations , 2020, Applied Numerical Mathematics.

[57]  Matteo Parsani,et al.  Entropy stable wall boundary conditions for the three-dimensional compressible Navier-Stokes equations , 2014, J. Comput. Phys..

[58]  David I. Ketcheson,et al.  Optimal stability polynomials for numerical integration of initial value problems , 2012, 1201.3035.

[59]  J. Butcher Numerical methods for ordinary differential equations , 2003 .

[60]  Gregor Gassner,et al.  Split form nodal discontinuous Galerkin schemes with summation-by-parts property for the compressible Euler equations , 2016, J. Comput. Phys..

[61]  Chi-Wang Shu Essentially non-oscillatory and weighted essentially non-oscillatory schemes for hyperbolic conservation laws , 1998 .

[62]  ShuChi-Wang,et al.  Efficient implementation of essentially non-oscillatory shock-capturing schemes, II , 1989 .

[63]  David E. Keyes,et al.  On the robustness and performance of entropy stable collocated discontinuous Galerkin methods , 2020, J. Comput. Phys..

[64]  David E. Keyes,et al.  Efficiency of High Order Spectral Element Methods on Petascale Architectures , 2016, ISC.

[65]  David I. Ketcheson,et al.  Time Discretization Techniques , 2016 .

[66]  Martin Almquist,et al.  Elastic wave propagation in anisotropic solids using energy-stable finite differences with weakly enforced boundary and interface conditions , 2021, J. Comput. Phys..

[67]  David Moxey,et al.  Spectral/hp element simulation of flow past a Formula One front wing: validation against experiments , 2019, Journal of Wind Engineering and Industrial Aerodynamics.

[68]  Desmond J. Higham,et al.  Analysis of stepsize selection schemes for Runge-Kutta codes , 1988 .

[69]  Jan Nordström,et al.  Energy stable and high-order-accurate finite difference methods on staggered grids , 2017, J. Comput. Phys..

[70]  Bilel Hadri,et al.  High-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: Scalable SSDC algorithms and flow solver , 2021, J. Comput. Phys..

[71]  Matteo Parsani,et al.  RK-Opt: A package for the design of numerical ODE solvers , 2020, J. Open Source Softw..

[72]  Gustaf Söderlind,et al.  Digital filters in adaptive time-stepping , 2003, TOMS.

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