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首页> 外文期刊>Journal of Materials Engineering and Performance >Path-Weighted Diffusivity Functions for Parameterization of Heat Deposition Processes
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Path-Weighted Diffusivity Functions for Parameterization of Heat Deposition Processes

机译：路径加权扩散函数，用于热沉积过程的参数化

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摘要

General parameterizations are constructed for spatial modulation of heat diffusion patterns according to energy deposition characteristics occurring within a volume of material where there exist inhomogeneous or anisotropic thermal diffusivity. These parameterizations are formulated in terms of path-weighted diffusivity functions. The construction of a general parameterization of energy deposition processes where spatially dependent thermal diffusivity exists is necessary for their inverse analysis. The structure of such a parameterization follows from the concepts of model and data spaces that imply the existence of an optimal parametric representation for a given class of inverse problems. Accordingly, the optimal parametric representation is determined by the characteristics of the available data, which, in principle, can contain both experimental measurements and numerical simulation data. Parameterizations for spatial modulation of heat diffusion follow from the observation that many different types of energy deposition processes can be represented by weighted sums of basis functions whose general forms are that of spatially modulated diffusion. The parameterizations presented are constructed according to a specific definition of the inverse heat deposition problem that provides a rigorous foundation for a highly flexible and general parameterization of energy deposition processes, which is essential for their inverse analysis.
机译：根据在一定体积的材料中发生的能量沉积特征，对空间内的热扩散模式进行空间调制，可以对这些参数进行一般性的参数化，其中存在不均匀或各向异性的热扩散率。这些参数化是根据路径加权扩散函数来表示的。对于能量沉积过程的反分析，必须建立能量沉积过程的一般参数化，在该过程中存在空间相关的热扩散率。这种参数化的结构源于模型和数据空间的概念，这些概念隐含了给定类别的反问题的最佳参数表示形式。因此，最佳参数表示法取决于可用数据的特征，这些数据原则上可以包含实验测量值和数值模拟数据。热扩散的空间调制的参数化来自以下观察：许多不同类型的能量沉积过程可以用基本函数的加权和表示，这些函数的一般形式是空间调制扩散的形式。提出的参数化是根据逆热沉积问题的特定定义构建的，该问题为能量沉积过程的高度灵活和通用的参数化提供了严格的基础，这对于它们的逆分析至关重要。

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