Large Eddy Simulations of Compressible Magnetohydrodynamic Turbulence

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Large Eddy Simulations of Compressible Magnetohydrodynamic Turbulence Large eddy simulations of compressible magnetohydrodynamic turbulence Dissertation zur Erlangung des mathematisch-naturwissenschaftlichen Doktorgrades “Doctor rerum naturalium” der Georg-August-Universität Göttingen im Promotionsprogramm PROPHYS der Georg-August University School of Science (GAUSS) vorgelegt von Philipp Grete Göttingen, 2016 Betreuungsausschuss Prof. Dr. Dominik Schleicher Departamento de Astronomía, Universidad de Concepción, Chile PD Dr. Wolfram Schmidt Hamburger Sternwarte, Universität Hamburg, Germany Prof. Dr. Laurent Gizon Max-Planck-Institut für Sonnensystemforschung, Göttingen, Germany Institut für Astrophysik, Georg-August-Universität Göttingen, Germany Mitglieder der Prüfungskommision Referent: PD Dr. Wolfram Schmidt Hamburger Sternwarte, Universität Hamburg, Germany Korreferent: Prof. Dr. Dr.h.c. Eberhard Bodenschatz Max-Planck-Institut für Dynamik und Selbstorganisation, Göttingen, Germany Institut für Nicht-Lineare Dynamik, Georg-August-Universität Göttingen, Germany 2. Korreferent: Prof. Dr. Marcus Brüggen Hamburger Sternwarte, Universität Hamburg, Germany Weitere Mitglieder der Prüfungskommission: Prof. Dr. Dominik Schleicher Departamento de Astronomía, Universidad de Concepción, Chile Prof. Dr. Jens Niemeyer Institut für Astrophysik, Georg-August-Universität Göttingen, Germany Prof. Dr. Jörg Büchner Max-Planck-Institut für Sonnensystemforschung, Göttingen, Germany Prof. Dr. Andreas Dillmann Institut für Aerodynamik und Strömungstechnik, DLR, Göttingen, Germany Tag der mündlichen Prüfung: 9. September 2016 2 Contents Contents Abstract 5 1 Introduction 7 1.1 Motivation . 7 1.2 Turbulence . 9 1.2.1 Incompressible hydrodynamic turbulence . 9 1.2.2 Compressibility and presence of magnetic fields . 11 1.2.3 Turbulence in astrophysics . 13 1.3 Numerical methods . 15 1.3.1 Overview . 15 1.3.2 High-resolution methods . 17 1.4 Large eddy simulations . 19 1.4.1 Implicit LES . 21 1.4.2 Explicit LES . 22 1.4.3 Explicit discrete filters . 25 1.4.4 Verification and validation . 30 2 Paper I: Nonlinear closures for scale separation in supersonic MHD turbu- lence 33 3 Paper II: A nonlinear structural subgrid-scale closure for compressible MHD. I. Derivation and energy dissipation properties 45 4 Paper III: A nonlinear structural subgrid-scale closure for compressible MHD. II. A priori comparison on turbulence simulation data 55 5 Paper IV: Comparative statistics of selected subgrid-scale models in large eddy simulations of decaying, supersonic MHD turbulence 71 6 Summary and conclusions 85 Bibliography 91 A Discrete filter approximations 101 Scientific contributions 103 Acknowledgements 105 3 Abstract Supersonic, magnetohydrodynamic (MHD) turbulence is thought to play an important role in many processes — especially in astrophysics, where detailed three-dimensional observations are scarce. Simulations can partially fill this gap and help to understand these processes. However, direct simulations with realistic parameters are often not feasible. Consequently, large eddy simulations (LES) have emerged as a viable alternative. In LES the overall complexity is reduced by simulating only large and intermediate scales directly. The smallest scales, usually referred to as subgrid-scales (SGS), are introduced to the simulation by means of an SGS model. Thus, the overall quality of an LES with respect to properly accounting for small-scale physics crucially depends on the quality of the SGS model. While there has been a lot of successful research on SGS models in the hydrodynamic regime for decades, SGS modeling in MHD is a rather recent topic, in particular, in the compressible regime. In this thesis, we derive and validate a new nonlinear MHD SGS model that explic- itly takes compressibility effects into account. A filter is used to separate the large and intermediate scales, and it is thought to mimic finite resolution effects. In the derivation, we use a deconvolution approach on the filter kernel. With this approach, we are able to derive nonlinear closures for all SGS terms in MHD: the turbulent Reynolds and Maxwell stresses, and the turbulent electromotive force (EMF). We validate the new closures both a priori and a posteriori. In the a priori tests, we use high-resolution reference data of stationary, homogeneous, isotropic MHD turbulence to compare exact SGS quantities against predictions by the closures. The comparison includes, for example, correlations of turbulent fluxes, the average dissipative behavior, and alignment of SGS vectors such as the EMF. In order to quantify the performance of the new nonlinear closure, this comparison is conducted from the subsonic (sonic Mach number Ms ≈ 0:2) to the highly supersonic (Ms ≈ 20) regime, and against other SGS closures. The latter include established closures of eddy-viscosity and scale-similarity type. In all tests and over the entire parameter space, we find that the proposed closures are (significantly) closer to the reference data than the other closures. In the a posteriori tests, we perform large eddy simulations of decaying, supersonic MHD turbulence with initial Ms ≈ 3. We implemented closures of all types, i.e. of eddy-viscosity, scale-similarity and nonlinear type, as an SGS model and evaluated their performance in comparison to simulations without a model (and at higher resolution). We find that the models need to be calculated on a scale larger than the grid scale, e.g. by an explicit filter, to have an influence on the dynamics at all. Furthermore, we show that only the proposed nonlinear closure improves higher-order statistics. 5 1 Introduction 1.1 Motivation Turbulence accompanies everyone — for some people more unconscious, for example, if they rely on turbulent mixing by stirring their cup of coffee with sugar and milk, than for others, who consciously study turbulence. In general, a turbulent flow may be understood as a qualitative description of a system that is dominated by virtually random, chaotic motions with a huge number of excited modes and intermittent features. This behavior makes exact predictions on the evolution of the flow extremely challenging (if not impos- sible). The difficulty stems from the nonlinearity of the fundamental equations, e.g. the (u · r)u term in the incompressible (r · u = 0) Navier-Stokes equations @u 1 + (u · r) u = − rP + νr2u (1.1) @t ρ with velocity u, density ρ, pressure P and dynamic viscosity ν. While exact solutions only exists for a few specific cases, flows can often be described statistically or in an even more simplified qualitative manner. One important qualitative description is the (hydrodynamic) Reynolds number LV Re = ; (1.2) ν which combines a characteristic lengthscale L and velocity V with ν to give a dimen- sionless number, see e.g. textbooks of Frisch (1995), Pope (2000) and Davidson (2004). This Reynolds number is often used to characterize flows given that flows with similar Reynolds number have similar properties. Illustratively, this is the reason why e.g. the real air flow over an airplane wing can be imitated with a model of reduced size that is subject to a flow of higher velocity. Typically, a flow is considered turbulent at Reynolds number of O(103), which is already reached for the initially mentioned cup of coffee under stirring of one revolution per second. While an exact dynamical description of a cup of coffee is barely possible, the situ- ation is astrophysics is even worse. Not only is the dynamical range naturally larger by many orders of magnitudes, but also compressibility and the presence of magnetic fields introduce further complexity. Here, the dynamics for an electrically conducting fluid can be described by the magnetohydrodynamic (MHD) equations, which can be derived by combining Maxwell’s equations with the Navier-Stokes equations. The basic equations 7 1 Introduction of compressible ideal MHD are given by ∂ρ + r · (ρu) = 0; (1.3a) @t ! ∂ρu B2 + r · (ρu ⊗ u − B ⊗ B) + r P + = 0; (1.3b) @t 2 @B − r × (u × B) = 0: (1.3c) @t p with the magnetic field B incorporating units of 1= 4π. Even though they look rather simple, they provide the key to a plethora of phenomena and processes on vastly varying scales. These include, for example, the geo-dynamo, which is responsible for maintain- ing the Earth’s protective magnetic field, the strong collimation of astrophysical jets by magnetic fields, and the magnetorotational instability, which regulates angular momen- tum transport in accretion disks. A more detailed description of selected turbulent astro- physical systems including their characteristic quantities is given in in subsection 1.2.3. All these processes have in common large Reynolds numbers and the exact impact and importance of turbulence is still subject to active research in most processes. The lack of final answers to many questions is partially due to the lack of detailed ob- servations. Naturally, most astrophysical observations provide 2-dimensional information only and at a limited resolution. Here, numerical simulations can complement observa- tions as they provide not only a detailed control over the environment, but also allow for the analysis for full (instantaneous) 3-dimensional data. Unfortunately, direct numerical simulations covering realistic regimes, i.e. the Re > O(109), are not going to be possi- ble for quite some time. Even the largest purely hydrodynamic turbulence simulations reaches only Re ∼ O(104) (Federrath et al. 2016). Thus, alternative approaches that re- duce the complexity while still maintaining the fundamental physics are required. One approach is the so-called
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