论文标题
非线性差分 - 代数方程的自动解耦和索引感知型号降低
Automatic Decoupling and Index-aware Model-Order Reduction for Nonlinear Differential-Algebraic Equations
论文作者
论文摘要
我们将索引感知的模型级还原方法扩展到具有特殊的非线性项f(ex)的非线性差分 - 代数方程的系统,其中e是一个单数矩阵。例如,在管道网络中气体流量的空间离散化中,出现了这种非线性差异代数方程。在实践中,由于复杂性和系统大小,在数值模拟中使用时,现实生活过程的数学模型会构成挑战。模型订购降低旨在通过生成较低的计算成本来消除该问题以模拟的模型,但准确地代表了原始的大规模系统行为。但是,由于隐藏的约束,很难直接减少和模拟非线性差分 - 代数方程,从而影响了数值集成方法和模型级还原技术的选择。我们提出了将索引感知模型级还原方法扩展到非线性差分 - 代数方程,而无需任何线性化。提出的模型订购方法涉及将非线性微分 - 代数方程式自动解耦为非线性普通微分方程和代数方程。这允许将标准模型订购技术应用于这两个部分,而不必担心索引。相同的过程也可以用于使用标准集成方案模拟非线性差异代数方程。我们说明了我们提出的针对管道网络中气流模型引起的非线性差分差异方程的方法的性能。
We extend the index-aware model-order reduction method to systems of nonlinear differential-algebraic equations with a special nonlinear term f(Ex), where E is a singular matrix. Such nonlinear differential-algebraic equations arise, for example, in the spatial discretization of the gas flow in pipeline networks. In practice, mathematical models of real-life processes pose challenges when used in numerical simulations, due to complexity and system size. Model-order reduction aims to eliminate this problem by generating reduced-order models that have lower computational cost to simulate, yet accurately represent the original large-scale system behavior. However, direct reduction and simulation of nonlinear differential-algebraic equations is difficult due to hidden constraints which affect the choice of numerical integration methods and model-order reduction techniques. We propose an extension of index-aware model-order reduction methods to nonlinear differential-algebraic equations without any kind of linearization. The proposed model-order reduction approach involves automatic decoupling of nonlinear differential-algebraic equations into nonlinear ordinary differential equations and algebraic equations. This allows applying standard model-order reduction techniques to both parts without worrying about the index. The same procedure can also be used to simulate nonlinear differential-algebraic equations using standard integration schemes. We illustrate the performance of our proposed method for nonlinear differential-algebraic equations arising from gas flow models in pipeline networks.