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Challenges to the State-of-the-practice of Solution Convergence Verification

机译:解决解决方案融合验证的实践状态的挑战

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The main goal of solution verification is to assess the convergence of numerical predictions as a function of discretization variables such as element size Ax or time step At. The challenge is to verify that the approximate solutions of the discretized laws-of-conservation or equations-of-motion converge to the solution of the continuous equations. In the case of code verification where the continuous solution of a test problem is known, common practice is to obtain several discrete solutions from successively refined meshes or grids; calculate norms of the solution error; and verify the rate with which discrete solutions converge to the continuous solution. With solution verification, where the continuous solution is unknown, common practice is to obtain several discrete solutions from successively refined meshes or grids; estimate an extrapolation of the continuous solution; verify the rate-of-convergence; and estimate numerical uncertainty bounds. The formalism proposed to verify the convergence of discrete solutions derives from postulating how truncation error behaves in the asymptotic regime of convergence. The contribution of this work is to challenge the commonly accepted view of verification and illustrate a number of difficulties encountered during the analysis of general-purpose codes. Examples are given from the disciplines of computational hydro-dynamics that involve the calculation of smooth or shocked solutions of non-linear, hyperbolic equations such as the 1D Burger equation or 2D Euler equations; and engineering mechanics that involve the calculation of smooth solutions of linear or non-linear, elliptic equations. A non-exhaustive list of topics that warrant further research includes: extending the state-of-the-practice to non-scalar quantities (curves, multiple-dimensional fields); studying the coupling between space and time discretizations; defining a reference mesh for the estimation of solution error; and developing technology to verify adaptive mesh refinement calculations in computational engineering and physics. (Approved for unlimited, public release, LA-UR-06-8078, Unclassified.).
机译:解决方案验证的主要目标是评估数值预测的收敛,作为分散化变量,例如元素大小AX或时间步进。挑战是验证离散定律的近似解的近似解或运动方程是否收敛到连续方程的解决方案。在已知测试问题的连续解决方案的代码验证的情况下,常见的做法是从连续的网格或网格获得几种离散解决方案;计算解决方案错误的规范;并验证离散解决方案收敛到连续解决方案的速率。通过解决方案验证,在持续解决方案未知之外,常见做法是从连续的网格或网格获得几种离散解决方案;估计连续溶液的外推;验证收敛速度;并估计数值不确定性范围。建议验证离散解决方案的收敛性的形式主义源于假设截断误差在融合的渐近制度中的表现。这项工作的贡献是挑战普遍接受的核查观,并说明了在普通目的码分析期间遇到的许多困难。示例是从计算水力动力学的学科给出的,涉及计算非线性,双曲线方程的平滑或震动解,例如1D汉堡方程或2D欧拉方程;和工程力学涉及计算线性或非线性,椭圆方程的光滑溶液。保证进一步研究的非详尽的主题列表包括:将实践状态扩展到非标量数(曲线,多维字段);研究空间和时间离散化之间的耦合;定义参考网格以估计解决方案错误;和开发技术验证计算工程与物理中的自适应网格细化计算。 (批准无限,公开发布,La-UR-06-8078,未分类。)。

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