Highly scalable solution of incompressible Navier-Stokes equations using the spectral element method with overlapping grids

We present a highly-flexible Schwarz overlapping framework for simulating turbulent fluid/thermal transport in complex domains. The approach is based on a variant of the Schwarz alternating method in which the solution is advanced in parallel in separate overlapping subdomains. In each domain, the governing equations are discretized with an efficient high-order spectral element method (SEM). At each step, subdomain boundary data are determined by interpolating from the overlapping region of adjacent subdomains. The data are either lagged in time or extrapolated to higher-order temporal accuracy using a novel stabilized predictor-corrector algorithm. Matrix stability analysis is used to determine the optimal number of corrector iterations. Stability and accuracy are further improved with an optimal mass flux correction to guarantee mass conservation throughout the domain. The method supports an arbitrary number of subdomains. A new multirate time-stepping scheme is developed (a first for incompressible flow simulations) that allows the underlying equations to be advanced with time-step sizes varying as much as an order-of-magnitude between adjacent domains. All the developments maintain the third-order temporal convergence and exponential convergence of the originating SEM framework. This dissertation also presents a mesh optimizer that has been specifically designed for meshes generated for turbulent flow problems. The optimizer supports surface mesh improvement, which minimizes geometrical approximation errors. The smoother is shown to reduce the computational cost of numerical calculations by as much as 40%. Numerous examples illustrate the effectiveness of these new technologies for analyzing challenging turbulence problems that were previously infeasible.Ope

Spectral element mesh generation and improvement methods

Meshing tools for finite element meshes have been studied extensively over the last few decades. However, relatively less attention has been paid to spectral element meshes. This thesis focuses on mesh generation and mesh improvement methods for spectral element meshes. A mesh smoother, based on a combination of Laplacian smoothing and optimization, has been developed and implemented in Nek5000, an open-source spectral element method based incompressible flow solver. The smoother takes a valid mesh as an input and outputs an improved mesh. Comparison of the original and smoothed mesh has shown that mesh smoothing decreases the iteration count of iterative solvers. This reduction is anticipated from an observed decrease in the ratio of the maximum to minimum eigenvalues of the upper Hessenberg matrix generated by the GMRES method. The mesh smoother was tested on various meshes for complicated geometries, and was found to improve the computational efficiency of calculations by up to 20% which is helping save 100,000s of cpu-hours on high-performance computing machines. A mesh skinning tool has also been developed which adds boundary layer resolving elements of user-specified thickness at user-specified surfaces in an existing mesh. This translates into savings in terms of human time and effort since the user can now robustly add boundary layer resolving elements instead of manually meshing the geometry to add these elements. Additionally, tools have been developed that generate meshes for geometries like turbine blades and random-array of cylinders (to simulate flow in vegetated channels), in a matter of seconds. Finally, a tetrahedral (tet) to hexahedron (hex) mesh converter has been implemented, that generates spectral element meshes for any complicated geometry by taking an all-tet mesh and converting it to an all-hex spectral element mesh. This tool has been developed to quickly generate all-hex meshes with minimal user intervention

Multirate Timestepping for the Incompressible Navier-Stokes Equations in Overlapping Grids

We develop a multirate timestepper for semi-implicit solutions of the unsteady incompressible Navier-Stokes equations (INSE) based on a recently-developed multidomain spectral element method (SEM). For {\em incompressible} flows, multirate timestepping (MTS) is particularly challenging because of the tight coupling implied by the incompressibility constraint, which manifests as an elliptic subproblem for the pressure at each timestep. The novelty of our approach stems from the development of a stable overlapping Schwarz method applied directly to the Navier-Stokes equations, rather than to the convective, viscous, and pressure substeps that are at the heart of most INSE solvers. Our MTS approach is based on a predictor-corrector (PC) strategy that preserves the temporal convergence of the underlying semi-implicit timestepper. We present numerical results demonstrating that this approach scales to an arbitrary number of overlapping grids, accurately models complex turbulent flow phenomenon, and improves computational efficiency in comparison to singlerate timestepping-based calculations.Comment: 40 pages, 13 figure

High-Order Mesh Morphing for Boundary and Interface Fitting to Implicit Geometries

We propose a method that morphs high-orger meshes such that their boundaries and interfaces coincide/align with implicitly defined geometries. Our focus is particularly on the case when the target surface is prescribed as the zero isocontour of a smooth discrete function. Common examples of this scenario include using level set functions to represent material interfaces in multimaterial configurations, and evolving geometries in shape and topology optimization. The proposed method formulates the mesh optimization problem as a variational minimization of the sum of a chosen mesh-quality metric using the Target-Matrix Optimization Paradigm (TMOP) and a penalty term that weakly forces the selected faces of the mesh to align with the target surface. The distinct features of the method are use of a source mesh to represent the level set function with sufficient accuracy, and adaptive strategies for setting the penalization weight and selecting the faces of the mesh to be fit to the target isocontour of the level set field. We demonstrate that the proposed method is robust for generating boundary- and interface-fitted meshes for curvilinear domains using different element types in 2D and 3D.Comment: 30 pages, 16 figure

hr-adaptivity for nonconforming high-order meshes with the target matrix optimization paradigm

We present an $hr$-adaptivity framework for optimization of high-order meshes. This work extends the $r$-adaptivity method for mesh optimization by Dobrev et al., where we utilized the Target-Matrix Optimization Paradigm (TMOP) to minimize a functional that depends on each element's current and target geometric parameters: element aspect-ratio, size, skew, and orientation. Since fixed mesh topology limits the ability to achieve the target size and aspect-ratio at each position, in this paper we augment the $r$-adaptivity framework with nonconforming adaptive mesh refinement to further reduce the error with respect to the target geometric parameters. The proposed formulation, referred to as $hr$-adaptivity, introduces TMOP-based quality estimators to satisfy the aspect-ratio-target via anisotropic refinements and size-target via isotropic refinements in each element of the mesh. The methodology presented is purely algebraic, extends to both simplices and hexahedra/quadrilaterals of any order, and supports nonconforming isotropic and anisotropic refinements in 2D and 3D. Using a problem with a known exact solution, we demonstrate the effectiveness of $hr$-adaptivity over both $r-$ and $h$-adaptivity in obtaining similar accuracy in the solution with significantly fewer degrees of freedom. We also present several examples that show that $hr$-adaptivity can help satisfy geometric targets even when $r$-adaptivity fails to do so, due to the topology of the initial mesh.Comment: 29 pages, 15 figure

DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

We introduce DynAMO, a reinforcement learning paradigm for Dynamic Anticipatory Mesh Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost and solution accuracy in numerical methods for partial differential equations. However, traditional adaptive mesh refinement approaches for time-dependent problems typically rely only on instantaneous error indicators to guide adaptivity. As a result, standard strategies often require frequent remeshing to maintain accuracy. In the DynAMO approach, multi-agent reinforcement learning is used to discover new local refinement policies that can anticipate and respond to future solution states by producing meshes that deliver more accurate solutions for longer time intervals. By applying DynAMO to discontinuous Galerkin methods for the linear advection and compressible Euler equations in two dimensions, we demonstrate that this new mesh refinement paradigm can outperform conventional threshold-based strategies while also generalizing to different mesh sizes, remeshing and simulation times, and initial conditions.Comment: 38 pages, 21 figure

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of complex physical phenomenon, as it allocates limited computational budget based on the need for higher or lower resolution, which varies over space and time. We present a novel formulation of AMR as a fully-cooperative Markov game, in which each element is an independent agent who makes refinement and de-refinement choices based on local information. We design a novel deep multi-agent reinforcement learning (MARL) algorithm called Value Decomposition Graph Network (VDGN), which solves the two core challenges that AMR poses for MARL: posthumous credit assignment due to agent creation and deletion, and unstructured observations due to the diversity of mesh geometries. For the first time, we show that MARL enables anticipatory refinement of regions that will encounter complex features at future times, thereby unlocking entirely new regions of the error-cost objective landscape that are inaccessible by traditional methods based on local error estimators. Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics. We show that learned policies generalize to test problems with physical features, mesh geometries, and longer simulation times that were not seen in training. We also extend VDGN with multi-objective optimization capabilities to find the Pareto front of the tradeoff between cost and error.Comment: 24 pages, 18 figure