Adaptive Mesh Refinement Strategies for a Novel Modelof Immiscible Fluid Flow in Fractures

In this paper, we consider two Adaptive Mesh Refinement (AMR) methods to simulate flow through fractures using a novel multiphase model. The approach represents the fluid using a two-dimensional parallel-plate model that employs techniques adapted from lattice-Boltzmann simulations to track the fluid interface. Here, we discuss different mesh refinement strategies for the model and compare their performance to that of a uniform grid. Results from the simulations are demonstrated showing excellent agreement between the model and analytical solutions for both unrefined and refined meshes. We also present results from the study that illustrate the behavior of the AMR front-tracking method. The AMR model is able to accurately track the interfacial properties in cases where uniform fine meshes would significantly increase the simulation cost.The ability of the model to dynamically refine the domain is demonstrated by presenting the results from an example with evolving interfaces.

Porous Shallow Water Modeling for Urban Floods in the Zhoushan City, China

Typhoon-induced intense rainfall and urban flooding have endangered the city of Zhoushan every year, urging efficient and accurate flooding prediction. Here, two models (the classical shallow water model that approximates complex buildings by locally refined meshes, and the porous shallow water model that adopts the concept of porosity) are developed and compared for the city of Zhoushan. Specifically, in the porous shallow water model, the building effects on flow storage and conveyance are modeled by the volumetric and edge porosities for each grid, and those on flow resistance are considered by adding extra drag in the flow momentum. Both models are developed under the framework of finite volume method using unstructured triangular grids, along with the Harten-Lax-van Leer-Contact (HLLC) approximate Riemann solver for flux computation and a flexible dry-wet treatment that guarantee model accuracy in dealing with complex flow regimes and topography. The pluvial flooding is simulated during the Super Typhoon Lekima in a 46 km2 mountain-bounded urban area, where efficient and accurate flooding prediction is challenged by local complex building geometry and mountainous topography. It is shown that the computed water depth and flow velocity of the two models agree with each other quite well. For a 2.8-day prediction, the computational cost is 120 min for the porous model using 12 cores of the Intel(R) Xeon(R) Platinum 8173M CPU @ 2.00 GHz processor, whereas it is as high as 17,154 min for the classical shallow water model. It indicates a speed-up of 143 times and sufficient pre-warning time by using the porous shallow water model, without appreciable loss in the quantitative accuracy.

Response Effects Due to Polygonal Representation of Pores in Porous Media Thermal Models

Physics models — such as thermal, structural, and fluid models — of engineering systems often incorporate a geometric aspect such that the model resembles the shape of the true system that it represents. However, the physical domain of the model is only a geometric representation of the true system, where geometric features are often simplified for convenience in model construction and to avoid added computational expense to running simulations. The process of simplifying or neglecting different aspects of the system geometry is sometimes referred to as “defeaturing.” Typically, modelers will choose to remove small features from the system model, such as fillets, holes, and fasteners. This simplification process can introduce inherent error into the computational model.Asimilar event can even take place when a computational mesh is generated, where smooth, curved features are represented by jagged, sharp geometries. The geometric representation and feature fidelity in a model can play a significant role in a corresponding simulation’s computational solution. In this paper, a porous material system — represented by a single porous unit cell — is considered. The system of interest is a two-dimensional square cell with a centered circular pore, ranging in porosity from 1% to 78%. However, the circular pore was represented geometrically by a series of regular polygons with number of sides ranging from 3 to 100. The system response quantity under investigation was the dimensionless effective thermal conductivity, k*, of the porous unit cell. The results show significant change in the resulting k* value depending on the number of polygon sides used to represent the circular pore. In order to mitigate the convolution of discretization error with this type of model form error, a series of five systematically refined meshes was used for each pore representation. Using the finite element method (FEM), the heat equation was solved numerically across the porous unit cell domain. Code verification was performed using the Method of Manufactured Solutions (MMS) to assess the order of accuracy of the implemented FEM. Likewise, solution verification was performed to estimate the numerical uncertainty due to discretization in the problem of interest. Specifically, a modern grid convergence index (GCI) approach was employed to estimate the numerical uncertainty on the systematically refined meshes. The results of the analyses presented in this paper illustrate the importance of understanding the effects of geometric representation in engineering models and can help to predict some model form error introduced by the model geometry.

On the stability of Scott-Zhang type operators and application to multilevel preconditioning in fractional diffusion

We provide an endpoint stability result for Scott-Zhang type operators in Besov spaces. For globally continuous piecewise polynomials these are bounded from $H^{3/2}$ into $B^{3/2}_{2,\infty}$; for elementwise polynomials these are bounded from $H^{1/2}$ into $B^{1/2}_{2,\infty}$. As an application, we obtain a multilevel decomposition based on Scott-Zhang operators on a hierarchy of meshes generated by newest vertex bisection with equivalent norms up to (but excluding) the endpoint case. A local multilevel diagonal preconditioner for the fractional Laplacian on locally refined meshes with optimal eigenvalue bounds is presented.

A coarsening algorithm on adaptive red-green-blue refined meshes

Adaptive meshing is a fundamental component of adaptive finite element methods. This includes refining and coarsening meshes locally. In this work, we are concerned with the red-green-blue refinement strategy in two dimensions and its counterpart-coarsening. In general, coarsening algorithms are mostly based on an explicitly given refinement history. In this work, we present a coarsening algorithm on adaptive red-green-blue meshes in two dimensions without explicitly knowing the refinement history. To this end, we examine the local structure of these meshes, find an easy-to-verify criterion to adaptively coarsen red-green-blue meshes, and prove that this criterion generates meshes with the desired properties. We present a MATLAB implementation built on the red-green-blue refinement routine of the -package (Funken and Schmidt 2018, 2019).

SCALAR: an AMR code to simulate axion-like dark matter models

We present a new code, SCALAR, based on the high-resolution hydrodynamics and N-body code RAMSES, to solve the Schrödinger equation on adaptive refined meshes. The code is intended to be used to simulate axion or fuzzy dark matter models where the evolution of the dark matter component is determined by a coupled Schrödinger-Poisson equation, but it can also be used as a stand-alone solver for both linear and non-linear Schrödinger equations with any given external potential. This paper describes the numerical implementation of our solver and presents tests to demonstrate how accurately it operates.