一种快速模拟Burgers方程的综合方案

Danish Rafiq, M. A. Bazaz
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引用次数: 6

摘要

本文提出了一种综合方案,以提高模拟Burgers方程的离线和在线计算时间。利用非线性矩匹配(NLMM)方法得到了全阶模型(FOM)的降阶近似。通过适当的逐步简化,将潜在的非线性Sylvester偏微分方程(PDE)的昂贵模拟简化为非线性代数方程系统。这大大减少了生成标准正交基向量的离线计算成本。采用离散经验插值(DEIM)进一步降低了底层非线性的复杂性,提高了求解简化后系统的在线计算时间。在不同的测试输入下,将由此导出的降阶模型(ROM)与固有正交分解(POD)进行了比较。
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A Comprehensive Scheme for Fast Simulation of Burgers’ Equation
This paper presents a comprehensive scheme to improve the offline as well as online computational time to simulate Burgers’ Equation. A reduced order approximation of the Full Order Model (FOM) is obtained using Non-Linear Moment Matching (NLMM) scheme. The expensive simulation of the underlying nonlinear Sylvester Partial Differential Equation (PDE) is reduced to a system of nonlinear algebraic equations by proper step-by-step simplifications. This reduces the offline computational cost of generating the orthonormal basis vectors substantially. Discrete Empirical Interpolation (DEIM) is used to further reduce the complexity of the underlying nonlinearity which improves the online computation time in solving the reduced system. Reduced Order Model (ROM) thus derived is compared with Proper Orthogonal Decomposition (POD) for different test inputs.
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