Computational fluid dynamics (CFD) is a significant tool routinely used indesign and optimization in aerospace industry. Often cases with unsteadyflows must be computed, and the long compute times of standard methods hasmotivated the present work on new implicit methods to replace the standardexplicit schemes. The implementation and numerical experiments were donewith the Swedish national flow solver Edge, developed by FOI,universities, and collaboration partners.The work is concentrated on a Lower-Upper Symmetric Gauss-Seidel (LU-SGS)type of time stepping. For the very anisotropic grids needed forReynolds-Averaged Navier-Stokes (RANS) computations of turbulent boundary layers,LU-SGS is combined with a line-implicit technique.  The inviscid flux Jacobians which contribute to the diagonalblocks of the system matrix are based on a flux splitting method with upwind type dissipation giving  control over diagonal dominance and artificial dissipation.The method is  controlled by several parameters, and comprehensivenumerical experiments were carried out to identify their influence andinteraction so that close to optimal values can be suggested. As an example,the optimal number of iterations carried out in a time-step increases with increased resolution of the computational grid.The numbering of the unknowns is important, and the numberings produced by mesh generators of Delaunay- and advancing front-type wereamong the best.The solver has been parallelized with the Message Passing Interface (MPI) for runs on multi-processor hardware,and its performance scales with the number of processors at least asefficiently as the explicit methods. The new method saves typicallybetween 50 and 80 percent of the runtime, depending on the case, andthe largest computations have reached 110M grid nodes. Theclassical multigrid acceleration for 3D RANS simulations was foundineffective in the cases tested in combination with the LU-SGS solverusing optimal parameters. Finally, preliminary time-accurate simulations for unsteady flows have shown promising results. / <p>QC 20141201</p>

With the algorithm’s suitability for exploiting current petascale and next-generation exascale supercomputers, stable and structure-preserving properties are necessary to develop predictive computational tools. In this dissertation, summation-by-parts (SBP) operators and a new relaxation Runge–Kutta (RRK) scheme are used to construct mimetic and structure-preserving full discretization for non-reactive compressible computational fluid dynamics (CFD) and reaction-diffusion models. In the first chapter, we provide the necessary background and a literature survey that forms the basis of this dissertation. Next, we provide a short overview of entropy stability for general conservation laws. The second chapter covers the analysis of the Eulerian model for compressible and heat-conducting flows. We provide the necessary background of the new system of parabolic partial differential equation (PDE). Then, we present the entropy stability analysis of the model at the continuous level. Subsequently, using the SBP, we construct an entropy-stable discretization of any order for unstructured grids with tensor-product elements. The third chapter discusses the implementation of RRK methods. We start by reviewing the RRK scheme constructed to guarantee conservation or stability with respect to any inner-product norm. Then, we present the extension and generalization of RRK schemes to general convex functionals and their application to compressible fluid flow problems. The final chapter demonstrates the far-reaching capabilities of the SBP operators and RRK schemes presenting the development of a novel fully discrete Lyapunov stable discretization for reaction models with spatial diffusion. Finally, we conclude this dissertation with an overview of our achievements and future research directions.

Computational fluid dynamics (CFD) has become a significant tool routinely used in design and optimization in aerospace industry. Typical flows may be characterized by high-speed and compressible flow features and, in many cases, by massive flow separation and unsteadiness. Accurate and efficient numerical solution of time-dependent problems is hence required, and the efficiency of standard dual-time stepping methods used for unsteady flows in many CFD codes has been found inadequate for large-scale industrial problems. This has motivated the present work, in which major effort is made to replace the explicit relaxation methods with implicit time integration schemes. The CFD flow solver considered in this work is Edge, a node-based solver for unstructured grids based on a dual, edge-based formulation. Edge is the Swedish national CFD tool for computing compressible flow, used at the Swedish aircraft manufacturer SAAB, and developed at FOI, lately in collaboration with external national and international partners. The work is initially devoted to the implementation of an implicit Lower-Upper Symmetric Gauss-Seidel (LU-SGS) type of relaxation in Edge with the purpose to speed up the convergence to steady state. The convergence of LU-SGS has been firstly accelerated by basing the implicit operator on a flux splitting method of matrix dissipation type. An increase of the diagonal dominance of the system matrix was the principal motivation. Then the code has been optimized by means of performance tools Intel Vtune and CrayPAT, improving the run time. It was found that the ordering of the unknowns significantly influences the convergence. Thus, different ordering techniques have been investigated. Finding the optimal ordering method is a very hard problem and the results obtained are mostly illustrative. Finally, to improve convergence speed on the stretched computational grids used for boundary layers LU-SGS has been combined with the line-implicit method. / QC 20120626