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Laplace-domain analysis of fluid line networks with applications to time-domain simulation and system parameter identification.

机译:流体线网络的拉普拉斯域分析及其在时域仿真和系统参数识别中的应用。

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摘要

Networks of closed conduits containing pressurised fluid flow occur in many different instances throughout the natural and man made world. The dynamics of such networks are dependent not only on the complex interactions between the fluid body and the conduit material within each fluid line, but also on the coupling between different lines as they influence each other through their common junctions. The forward modelling (time-domain simulation), and inverse modelling (system parameter identification) of such systems is of great interest to many different research fields. An alternative approach to time-domain descriptions of fluid line networks is the Laplace-domain representation of these systems. A long standing limitation of these methods is that the frameworks for constructing Laplace-domain models have not been suitable for pipeline networks of an arbitrary topology. The objective of this thesis is to fundamentally extend the existing theory for Laplace-domain descriptions of hydraulic networks and explore the applications of this theory to forward and inverse modelling. The extensions are undertaken by the use of graph theory concepts to construct network admittance matrices based on the Laplace-domain solutionsof the fundamental pipeline dynamics. This framework is extended to incorporate a very broad class of hydraulic elements. Through the use of the numerical inverse Laplace transform, the proposed theory forms the basis for an accurate and computationally efficient hydraulic network time-domain simulation methodology. The compact analytic nature of the network admittance matrix representation facilitates the development of two successful and statistically based parameter identification methodologies, one based on an oblique filtering approach combined with maximumlikelihood estimation, and the other based on the expectation-maximisation algorithm.
机译:在整个自然世界和人造世界中,在许多不同的情况下都会发生包含加压流体流的封闭管道网络。这样的网络的动力学不仅取决于在每个流体管线内的流体主体和导管材料之间的复杂相互作用,而且还取决于不同管线之间的耦合,因为它们通过它们的公共连接点相互影响。这种系统的正向建模(时域仿真)和逆向建模(系统参数识别)在许多不同的研究领域中引起了极大的兴趣。流体管线网络的时域描述的另一种方法是这些系统的拉普拉斯域表示。这些方法长期以来的局限性在于,用于构建拉普拉斯域模型的框架不适用于任意拓扑的管道网络。本文的目的是从根本上扩展现有的液压网络拉普拉斯域描述理论,并探索该理论在正向和逆向建模中的应用。这些扩展是通过使用图论概念来构建的,该方法基于基本管道动力学的拉普拉斯域解决方案来构建网络导纳矩阵。该框架被扩展为包含非常广泛的一类液压元件。通过使用数值拉普拉斯逆变换,所提出的理论构成了精确且计算效率高的液压网络时域仿真方法的基础。网络导纳矩阵表示的紧凑分析性质有助于开发两种成功且基于统计的参数识别方法,一种基于斜滤波方法结合最大似然估计,另一种基于期望最大化算法。

著录项

  • 作者

    Zecchin Aaron Carlo;

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  • 年度 2010
  • 总页数
  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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