Bulletin of the American Physical Society
72nd Annual Meeting of the APS Division of Fluid Dynamics
Volume 64, Number 13
Saturday–Tuesday, November 23–26, 2019; Seattle, Washington
Session S19: CFD: Unstructured Grids and Adaptive Mesh Applications |
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Chair: Cheng Huang, U Michigan Room: 401 |
Tuesday, November 26, 2019 10:31AM - 10:44AM |
S19.00001: A unified AMR framework for multiphase flow and fluid-structure interaction problems with both non-subcycling and subcycling Yadong Zeng, Lian Shen In the present work, we developed a unified structured adaptive mesh refinement (SAMR) framework for multiphase flow and fluid-structure interaction problems. The coarsest grid covers the whole computational domain, and is dynamically refined onto higher levels to obtain higher resolution in local regions. Both non-subcycling, where a uniform time step is employed for all variables on composite levels, and subcycling, in which variables on different levels advance via different time steps, are embedded in the SAMR framework. While the former is easier to implement, the latter can significantly reduce the computational time while maintaining the same order of accuracy. For multiphase flow problems, the level set function is used to capture the interface, and re-initialization across different levels is applied. For fluid-structure interaction problems, a direct forcing immersed boundary method is coupled with SAMR. Validation cases are presented to demonstrate the accuracy and robustness of the proposed computational framework. [Preview Abstract] |
Tuesday, November 26, 2019 10:44AM - 10:57AM |
S19.00002: An optimal sparse sensing approach for adaptive mesh refinement in unsteady flows Daniel Foti, Sven Giorno, Karthik Duraisamy Complex physical flows are often characterized by coherent structures, which have a crucial role in turbulent flows. In order to accurately simulate the flow field, the coherent structures need to be adequately resolved with a sufficiently fine mesh. Furthermore, local mesh refinement in areas of interest can be employed to reduce time while preserving accuracy. While mesh adaptation techniques are well-established for steady flows, refinement methodology for unsteady, spatially-evolving flows is less straightforward. Residual and error-minimization based methods require precise definitions for spatio-temporal error, and feature or gradient based methods rely overtly on user intuition of the flow, while adjoint-based methods can become expensive for finite volume methods. We introduce a novel approach for adaptive mesh refinement where selection is obtained similar to a computationally expedient discrete empirical interpolation method using rank-revealing QR. This method seeks optimal locations for grid adaptation from the basis of a proper orthogonal decomposition, which organizes velocity flow field features into optimal orthogonal modes based on energy. The methodology is tested on a series of cases including shock formation and flows dominated by coherent structures. [Preview Abstract] |
Tuesday, November 26, 2019 10:57AM - 11:10AM |
S19.00003: Slug Flow Prediction for Subsea Applications Using Dynamic Anisotropic Mesh Optimisation with Tetrahedral Control-Volume Finite Elements Claire Heaney, Lyes Kahouadji, Lluis Via-Estrem, Asiri Obeysekara, Pablo Salinas, Christopher Pain, Omar Matar We present a three-dimensional Direct Numerical Simulation of two-phase air-water flow inside complex pipe configurations with very large aspect ratios (Length/Diameter >100) for subsea applications. We focus on the challenging slug flow regimes using a dynamically unstructured mesh, which can modify and adapt to the complex air-water interface in order to represent optimally these flows minimising the use of computational resources. The numerical framework consists of a mixed control-volume and finite-element formulation, and a volume-of-fluid method for the interface-capturing based on a compressive control-volume advection method. The resulting slug length and frequency are compared with experimental data for horizontal pipes. [Preview Abstract] |
Tuesday, November 26, 2019 11:10AM - 11:23AM |
S19.00004: LES of Compressible Gas Flow Impinging on a Wall using High Order Schemes in an Unstructured Grid. Douglas Fontes, Michael Kinzel In this work, compressible gas flow impinging on a wall is solved using large eddy simulation in an unstructured mesh. In this kind of flow, complex phenomena such as shock waves, high gradients, turbulence, and wall interaction can arise. In most Computational Fluid Dynamics (CFD) codes, typically with unstructured grids, spatial schemes are limited to first and second order due to the difficulties of obtaining the information of neighboring elements. This limitation results in a higher computational cost to achieve a specific accuracy. High order schemes in unstructured grids, using a flux reconstruction method, have been implemented in an open source code termed as PyFR. Thus, the present effort explores the application of PyFR to perform Large Eddy Simulation (LES) for an impinging jet case discretized with an unstructured mesh. In this study, we will identify turbulent structures and the formation of instabilities. In addition, comparisons will be developed with RANS prediction. All numerical predictions will be compared to an experimental case in order to analyze the numerical accuracy in terms of experimental results. [Preview Abstract] |
Tuesday, November 26, 2019 11:23AM - 11:36AM |
S19.00005: DEM and Coarse-grained modeling of bubble and particle behavior in fluidized beds Oscar Antepara, Ann Almgren, Michele Rosso, Roberto Porcu, Jordan Musser, William Fullmer, Christopher Boyce MFiX-Exa is a new code being developed by the National Energy Technology Laboratory and Lawrence Berkeley National Laboratory as part of the U.S. Department of Energy's Exascale Computing Project. MFiX-Exa originated by combining discrete element method (DEM) modules of the classic MFiX code (https://mfix.netl.doe.gov) with a low Mach number projection method for the continuous fluid phase. The new algorithm is implemented using the AMReX software for massively parallel block-structured applications (https://amrex-codes.github.io). In this work, we present the coarse-grained(CG) DEM technique in which several particles are lumped into a single Lagrangian parcel. The work focuses on comparisons, in terms of computational time and accuracy, between the traditional (single particle) DEM and the CG-DEM. The problem of interest is a cylindrical gas-solid fluidized bed containing several million 1~mm particles. Comparisons are also made to the experimental data which include bubble characteristics, bed height, and particle velocity distributions. The results assess how accurately the models can reproduce the main characteristics of the gas-solid fluidized bed and the reduction of computational time between the models. [Preview Abstract] |
Tuesday, November 26, 2019 11:36AM - 11:49AM |
S19.00006: Wavelet-based adaptive simulations of flapping insects Thomas Engels, Kai Schneider, Julius Reiss, Marie Farge, Dmitry Kolomenskiy We present a novel wavelet-based approach to compute multiscale flows generated by complex, time-dependent geometries, motivated by the spectacular flight capabilities of flying insects. Our framework is inherently based on dynamically evolving grids. To this end, we develop a datastructure based on locally regular Cartesian blocks, which are indexed in a tree-like fashion. The blocks are distributed among MPI processes and allow an efficient parallelization for large scale supercomputers. To avoid solving elliptic problems, we approximate an incompressible fluid using the method of artificial compressibility. Since our grid is locally Cartesian, we use the volume penalization method to include moving obstacles without the need for a boundary-conformal grid. We employ biorthogonal interpolating wavelets as refinement indicators and prediction operators, and combine them with a 4th order finite difference discretization. Using thresholding of wavelet coefficients, we show that the precision of the underlying uniform discretization is maintained on our adaptive grids, while reducing the computational effort. We derive scaling relations for the numerical parameters, and present validation cases to assess its accuracy and performance on massively parallel computer architectures. [Preview Abstract] |
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