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Temperature Distribution Reconstruction by Eigenfunction Interpolation of Boundary Measurement Data

机译:边界测量数据的特征函数插值重构温度分布

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This paper deals with the inverse problem of evaluating the temperature distribution over time in a 3-D composite solid material having an arbitrary geometry. This approach is capable of evaluating the temperature distribution within the domain of the nonhomogeneous object under observation at each time instance. In this paper, we propose to use the eigenfunctions of the heat equation model, representing the heat problem under observation, as a basis for reconstructing the 3-D temperature distribution. This choice of basis functions has the advantage of incorporating the physics of the problem, making the temperature reconstruction inverse problem more robust. Because of the geometry complexity, the eigenfunctions have been computed numerically using a finite-element method. In principle, the method uses temperature measurements in just a few points of the object domain. To consider the practical aspect, here we focus our attention on a noninvasive approach taking the observation points only on the available boundary surfaces. The proper weighting of the eigenfunction basis used as temperature interpolators is achieved inverting the collected measured data. The two critical problems of selecting the best subset of eigenfunctions from the set of infinitely many available ones and the optimization of numbering and positioning the boundary measurement spots are studied as well.
机译:本文涉及一个反问题,即评估具有任意几何形状的3-D复合固体材料随时间的温度分布。该方法能够评估在每个时间实例下所观察到的非均质物体域内的温度分布。在本文中,我们建议使用热方程模型的特征函数(表示观察到的热问题)作为重建3-D温度分布的基础。基函数的这种选择具有结合问题的物理特性的优点,从而使温度重构反问题更加稳健。由于几何形状的复杂性,特征函数已使用有限元方法进行了数值计算。原则上,该方法仅在对象域的几个点上使用温度测量。为了考虑实际方面,在此我们将注意力集中在一种非侵入性方法上,该方法仅将观察点放在可用边界表面上。对用作温度插值器的本征函数基础进行适当的加权,可以反转所收集的测量数据。还研究了从无限多个可用特征集中选择最佳特征函数子集以及对边界测量点进行编号和定位的两个关键问题。

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