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Energy dependent mesh adaptivity of discontinuous isogeometric discrete ordinate methods with dual weighted residual error estimators

机译:具有双加权残差误差估计的非连续等几次离散纵坐标方法的能量依赖网格自适应性

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摘要

Inthispaperahanging-node,discontinuousGalerkin,isogeometricdiscretisationofthemultigroup,discreteordinates (SN) equations is presented in which each energy group has its own mesh. The equations are discretised using NonUniform Rational B-Splines (NURBS), which allows the coarsest mesh to exactly represent the geometry for a wide rangeofengineeringproblemsofinterest; thiswouldnotbethecaseusingstraight-sided???niteelements. Information istransferredbetweenmeshesviatheconstructionofasupermesh. Thisisanon-trivialtaskfortwoarbitrarymeshes, but is signi???cantly simpli???ed here by deriving every mesh from a common coarsest initial mesh. In order to take full advantage of this ???exible discretisation, goal-based error estimators are derived for the multigroup, discrete ordinates equations with both ???xed (extraneous) and ???ssion sources, and these estimators are used to drive an adaptive mesh re???nement (AMR) procedure. The method is applied to a variety of test cases for both ???xed and ???ssion source problems. The error estimators are found to be extremely accurate for linear NURBS discretisations, with degraded performance for quadratic discretisations owing to a reduction in relative accuracy of the ???exact??? adjoint solution required to calculate the estimators. Nevertheless, the method seems to produce optimal meshes in the AMR process for both linear and quadratic discretisations, and is ??? ??100 more accurate than uniform re???nement for the same amount of computational e???ort for a 67 group deep penetration shielding problem.
机译:本文提出了一个悬挂节点,不连续Galerkin,多组等几何离散,离散(SN)方程,其中每个能量组都有自己的网格。使用非均匀有理B样条线(NURBS)离散化方程,这可以使最粗糙的网格精确地代表各种感兴趣的工程问题的几何形状;在这种情况下,将不会使用直面的元素。信息通过超级网格的构造在网格之间传递。这对于两个任意网格来说是很简单的,但是在这里可以通过从一个普通的最粗的初始网格中派生每个网格来显着简化。为了充分利用这种灵活的离散化,针对具有固定(外部)和ssion源的多组离散坐标方程,导出基于目标的误差估计量,并且将这些估计量用于驱动自适应网格刷新(AMR)过程。该方法既适用于固定源问题也适用于各种测试用例。发现误差估计器对于线性NURBS离散化是极其精确的,由于“精确”相对精度的降低,对于二次离散化而言性能降低。计算估计量所需的伴随解。然而,该方法似乎在AMR过程中为线性和二次离散化都产生了最佳网格,并且是?对于67组深层穿透屏蔽问题,在相同数量的计算量的基础上,比统一需求要精确100倍。

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