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arXiv 1306.2881physics.comp-phcs.NAmath.NA
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熵基动量闭合中适应性基变换用于线性动力学方程

Adaptive change of basis in entropy-based moment closures for linear kinetic equations

University of Maryland(马里兰大学);Oak Ridge National Laboratory(橡树岭国家实验室);RWTH-Aachen University(亚琛工业大学)

Graham W. Alldredge, Cory D. Hauck, Dianne P. O'Leary, André L. Tits

AI总结:

本文提出一种在 slab 几何中求解熵基动量闭合问题的数值算法,通过适应性基变换和固定四阶方案提高计算效率,并展示其在基准问题中的有效性。

摘要:

熵基(M_N)动量闭合用于动力学方程是通过一个约束优化问题定义的,必须在空间-时间网格的每个点上求解,因此准确高效地求解这些优化问题至关重要。我们提出了一种完整的实用数值算法,用于求解一维 slab 几何中的双重问题。闭合仅在可实现的动量集上定义,而当接近可实现集的边界时,双重问题变得越来越难以求解,因为 Hessian 矩阵的病态性增加。为了提高 Hessian 的条件数,我们建议使用通过 Hessian 的 Cholesky 分解定义的多项式基变换,从而能够求解接近可实现集边界的問題。我们还建议使用固定四阶方案,而不是自适应四阶方案,因为后者引入了不必要的开销并改变了可计算的可实现集。对于非常病态的问题,我们使用正则化使优化算法更加稳健。我们设计了一个制造解并证明适应性基优化算法减少了对正则化的需要。这很重要,因为我们也展示了正则化会减慢甚至阻止数值模拟在细化空间-时间网格时的收敛。我们还模拟了两个著名的基准问题。在那里,我们发现我们的适应性基、固定四阶算法比替代方案使用更少的正则化,尽管结果数值模拟对正则化策略的敏感性比基的选择更高。

英文摘要:

Entropy-based (M_N) moment closures for kinetic equations are defined by a constrained optimization problem that must be solved at every point in a space-time mesh, making it important to solve these optimization problems accurately and efficiently. We present a complete and practical numerical algorithm for solving the dual problem in one-dimensional, slab geometries. The closure is only well-defined on the set of moments that are realizable from a positive underlying distribution, and as the boundary of the realizable set is approached, the dual problem becomes increasingly difficult to solve due to ill-conditioning of the Hessian matrix. To improve the condition number of the Hessian, we advocate the use of a change of polynomial basis, defined using a Cholesky factorization of the Hessian, that permits solution of problems nearer to the boundary of the realizable set. We also advocate a fixed quadrature scheme, rather than adaptive quadrature, since the latter introduces unnecessary expense and changes the computationally realizable set as the quadrature changes. For very ill-conditioned problems, we use regularization to make the optimization algorithm robust. We design a manufactured solution and demonstrate that the adaptive-basis optimization algorithm reduces the need for regularization. This is important since we also show that regularization slows, and even stalls, convergence of the numerical simulation when refining the space-time mesh. We also simulate two well-known benchmark problems. There we find that our adaptive-basis, fixed-quadrature algorithm uses less regularization than alternatives, although differences in the resulting numerical simulations are more sensitive to the regularization strategy than to the choice of basis.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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