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Conservative Solution Transfer Between Anisotropic Meshes for Adaptive Time-Accurate Hybridized Discontinuous Galerkin Methods

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969494" target="_blank" >RIV/49777513:23520/23:43969494 - isvavai.cz</a>

  • Result on the web

    <a href="https://arc.aiaa.org/doi/10.2514/6.2023-1794" target="_blank" >https://arc.aiaa.org/doi/10.2514/6.2023-1794</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.2514/6.2023-1794" target="_blank" >10.2514/6.2023-1794</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Conservative Solution Transfer Between Anisotropic Meshes for Adaptive Time-Accurate Hybridized Discontinuous Galerkin Methods

  • Original language description

    We present a hybridized discontinuous Galerkin (HDG) solver for general time-dependent balance laws. We focus in particular on a coupling of the solution process for unsteady problems with an anisotropic mesh refinement framework. The goal is to properly resolve all relevant unsteady features with the smallest number of mesh elements, and hence to reduce the computational cost of numerical simulations. The crucial step is then to transfer the numerical solution between two meshes since the anisotropic mesh adaptation is producing highly skewed unstructured grids that do not share the same topology as the original mesh where the solution is initially defined. For this purpose, we adopt the Galerkin projection as it preserves the conservation of physically relevant quantities and does not compromise the accuracy of a high-order method. We present numerical experiments verifying these properties of the overall method.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20302 - Applied mechanics

Result continuities

  • Project

    <a href="/en/project/GA21-31457S" target="_blank" >GA21-31457S: Fast flow-field prediction using deep neural networks for solving fluid-structure interaction problems</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    AIAA SCITECH 2023 Forum

  • ISBN

    978-1-62410-699-6

  • ISSN

  • e-ISSN

  • Number of pages

    17

  • Pages from-to

    1-17

  • Publisher name

    American Institute of Aeronautics and Astronautics

  • Place of publication

    Reston, VA, USA

  • Event location

    National Harbor, MD, USA

  • Event date

    Jan 23, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article