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Measures of Entropy to Characterize Fatigue Damage in Metallic Materials

机译:表征金属材料疲劳损伤的熵测度

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This paper presents the entropic damage indicators for metallic material fatigue processes obtained from three associated energy dissipation sources. Since its inception, reliability engineering has employed statistical and probabilistic models to assess the reliability and integrity of components and systems. To supplement the traditional techniques, an empirically-based approach, called physics of failure (PoF), has recently become popular. The prerequisite for a PoF analysis is an understanding of the mechanics of the failure process. Entropy, the measure of disorder and uncertainty, introduced from the second law of thermodynamics, has emerged as a fundamental and promising metric to characterize all mechanistic degradation phenomena and their interactions. Entropy has already been used as a fundamental and scale-independent metric to predict damage and failure. In this paper, three entropic-based metrics are examined and demonstrated for application to fatigue damage. We collected experimental data on energy dissipations associated with fatigue damage, in the forms of mechanical, thermal, and acoustic emission (AE) energies, and estimated and correlated the corresponding entropy generations with the observed fatigue damages in metallic materials. Three entropic theorems—thermodynamics, information, and statistical mechanics—support approaches used to estimate the entropic-based fatigue damage. Classical thermodynamic entropy provided a reasonably constant level of entropic endurance to fatigue failure. Jeffreys divergence in statistical mechanics and AE information entropy also correlated well with fatigue damage. Finally, an extension of the relationship between thermodynamic entropy and Jeffreys divergence from molecular-scale to macro-scale applications in fatigue failure resulted in an empirically-based pseudo-Boltzmann constant equivalent to the Boltzmann constant.
机译:本文介绍了从三个相关的能量耗散源获得的金属材料疲劳过程的熵损伤指标。自成立以来,可靠性工程已采用统计和概率模型来评估组件和系统的可靠性和完整性。作为对传统技术的补充,近来流行了一种基于经验的方法,称为失效物理(PoF)。 PoF分析的前提是必须了解故障过程的机理。从热力学第二定律引入的熵(无序和不确定性的度量)已成为表征所有机械降解现象及其相互作用的基本且有希望的度量标准。熵已被用作预测损坏和故障的基本且与比例无关的度量。在本文中,研究了三种基于熵的度量标准,并证明了它们在疲劳损伤中的应用。我们以机械,热和声发射(AE)能量的形式收集了与疲劳损伤相关的能量耗散的实验数据,并估计了相应的熵生成并将其与金属材料中观察到的疲劳损伤相关联。三个熵定理(热力学,信息和统计力学)支持用于估计基于熵的疲劳损伤的方法。经典的热力学熵为疲劳破坏提供了一定程度的熵强度。 Jeffreys在统计力学和AE信息熵方面的差异也与疲劳损伤密切相关。最后,热力学熵和Jeffreys发散从分子尺度到宏观尺度在疲劳失效中的应用之间的关系的扩展导致了基于经验的伪玻尔兹曼常数,等效于玻尔兹曼常数。

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