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时间分数阶对流—扩散方程反问题研究
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摘要
时间分数阶对流-扩散方程(简称TFADE)可以用来模拟与时间相关的反常扩散,它是由传统的对流-扩散方程(简称ADE)演变而来的。虽然对于反常扩散正问题已有不少研究,但对于反常扩散模型参数识别的反问题研究却鲜见于文献。
     本文基于对时间分数阶扩散方程和时间分数阶对流-扩散方程正问题的数值求解方法研究,主要探讨扩散系数和分数微分阶数的数值反演问题。
     论文前两章简要介绍分数阶微分算子的发展历史和现状及趋势,给出三种分数阶微积分的定义,分数阶导数的性质及其区别与联系,进一步推导了函数eλ1的Caputo分数阶导数的表达式。
     第三章主要讨论有限域上时间分数阶扩散方程正问题的数值求解。基于对Caputo意义下时间导数的离散,对于两种形式的扩散系数分别提出了正问题求解的隐式差分格式,证明了差分格式的稳定性和收敛性,并给出了一个数值算例分析。
     第四章着重探讨时间分数阶扩散方程的参数反演问题。应用最佳摄动量算法和同伦正则化算法,对确定分数微分阶数与扩散系数的反问题进行了数值反演模拟。通过数值算例,讨论了正则参数(同伦参数)、数值微分步长等参数选取对反演结果的影响。计算结果表明,一维时间分数阶扩散方程中同时确定分数微分阶数与扩散系数的反问题具有数值唯一性。
     第五章讨论了时间分数阶对流-扩散方程的正问题及参数反演问题。对于多项式形式的扩散系数推导出了求解正问题的隐式差分格式,利用最佳摄动量算法对扩散系数与分数微分阶数的同时反演进行数值模拟,并讨论了对流系数、正则参数选取对反演结果的影响。
Time fractional advection-diffusion equation (TFADE in short), which is different from traditional convection-diffusion equation (ADE in short), can be utilized to simulate time-related abnormal diffusion process. There are quite a few researches on the anomalous diffusion, but it is still rare in the literature we have for studies on inverse problems of TFADE.
     This thesis will deal with inverse problems of determining fractional order and diffusion coefficient based on numerical methods to the forward problem of time fractional diffusion equation (TFDE in short) and TFADE.
     In the first two chapters, surveys of history of fractional calculus, research background and development, and three definitions of fractional derivative, and relationships of them are introduced, respectively.
     Chapter 3 mainly deals with solving of the forward problem of TFDE in a finite domain by finite difference method, where the fractional derivative of time is discreted by Caputo formula. An implicit difference scheme is presented, and stability and convergence of the difference scheme is analyzed. Finally, a numerical example is given to support the numerical scheme.
     In Chapter 4, numerical inversions for parameters of TFDE are carried out by applying optimal perturbation regularization algorithm and homotopy regularization algorithm, respectively. Several numerical simulations are performed, and impacts of regularization parameter (homotopy parameter), and numerical differential step on the inversion algorithms are discussed. Inversion results show that the inverse problem here is of numerical uniqueness.
     Chapter 5 is devoted to study numerical method to TFADE and corresponding inverse problems of determining fractional order and diffusion coefficient. An implicit difference scheme is presented in the case of the diffusion coefficient taking on polynomials. Numerical simulations are carried out also by the optimal perturbation regularization algorithm, and impacts of choices of regularization parameter, and advection velocity on the inversion algorithm are discussed.
引文
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