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IDENTIFICATION OF SPATIALLY-VARYING PARAMETERS IN DISTRIBUTED PARAMETER SYSTEMS.

机译:分布式参数系统中空间变异参数的识别。

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摘要

Identification of spatially-varying parameters in distributed parameter systems given an observation of the state is as a rule an ill-posed problem in the sense of Hadamard. Even in case when the solution is unique, it does not depend continuously on the data. The identification problem that motivated this work arises in the description of petroleum reservoirs and subsurface aquifers it consists of identifying the spatially-varying parameter (alpha)(x,y) in the diffusion equation u(,t) = ((alpha)u(,x))(,x) + ((alpha)u(,y))(,y) + f given an observation of u at a discrete set of spatial locations.The problem of constructing stable approximate solutions to identification problems in distributed parameter systems is next investigated. The concept of regularization, widely used in solving linear Fredholm integral equations, is developed for the solution of such problems. A general regularization identification theory is presented and applied to the identification of parabolic systems. Two alternative numerical approaches for the minimization of the smoothing functional are investigated: (i) classical Banach space gradient methods and (ii) discretized minimization methods. The latter use finite-dimensional convergent approximations in Sobolev spaces and are based on an appropriate convergence theorem. The performance of the regularization identification method is evaluated by numerical experiments on the identification of spatially-varying diffusivity (alpha) in the diffusion equation.The question of uniqueness of (alpha) (identifiability problem) is first investigated. The analysis is restricted to the one-dimensional version of the above equation i.e. to u(,t) = ((alpha)u(,x))(,x) + f and an observation of u at a single point. The identifiability problem is formulated as an inverse Sturm-Liouville problem for ((alpha)y')' + (lamda)y = 0. It is proved that the eigenvalues and the normalizing constants determine the above Sturm-Liouville operator uniquely. Identifiability and non-identifiability results are obtained for three special cases.
机译:在给定状态的观察下,在分布式参数系统中识别空间变化的参数通常是一个在Hadamard意义上的不适定问题。即使在解决方案是唯一的情况下,它也不会持续依赖于数据。推动这项工作的识别问题出现在石油储层和地下含水层的描述中,它包括识别扩散方程u(,t)=(αu(( ,x))(,x)+(αu(,y))(,y)+ f给定在一组离散的空间位置上对u的观测值。为分布式识别问题构造稳定的近似解的问题接下来研究参数系统。为解决此类问题,开发了广泛用于求解线性Fredholm积分方程的正则化概念。提出了一种通用的正则化辨识理论,并将其应用于抛物线系统的辨识。研究了两种最小化平滑函数的替代数值方法:(i)经典Banach空间梯度方法和(ii)离散最小化方法。后者在Sobolev空间中使用有限维收敛近似,并且基于适当的收敛定理。通过数值实验对扩散方程中空间变化扩散率(α)的识别进行了数值实验,评估了正则化识别方法的性能。首先研究了α的唯一性问题(可识别性问题)。该分析仅限于上述方程的一维形式,即u(,t)=(αu(,x))(,x)+ f以及对u的单点观测。对于(αy')'+(lamy)y = 0,将可识别性问题表示为Sturm-Liouville逆问题。证明了特征值和归一化常数唯一地确定了上述Sturm-Liouville算子。针对三种特殊情况获得了可识别性和不可识别性结果。

著录项

  • 作者

    KRAVARIS, COSTAS.;

  • 作者单位

    California Institute of Technology.;

  • 授予单位 California Institute of Technology.;
  • 学科 Engineering Chemical.
  • 学位 Ph.D.
  • 年度 1984
  • 页码 217 p.
  • 总页数 217
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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