Special Issue "Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editor

Prof. Dr. Cemal Basaran
E-Mail Website
Guest Editor
Dept. of Civil, Structural and Environmental Engineering, University at Buffalo, New York, NY, USA
Tel. 7166454375
Interests: unified mechanics theory and its applications

Special Issue Information

Dear Colleagues,

In the last two decades, there has been significant progress in using entropy generation for damage and fracture mechanics and in-situ structural health monitoring of systems, ranging from infrastructures to mechanical and biological systems. Compared to phenomenological damage and fracture mechanics models based on empirical curve fitting a polynomial to a degradation and fracture data, using entropy provides a physics and mathematics-based alternative. Using either thermodynamic entropy or information theory entropy has been shown to be extremely successful in predicting the degradation, fracture, fatigue, and in-situ prognosis of all systems. It was proven by Jaynes [1957] that thermodynamic entropy is identical to the information theory entropy of the probability distribution, except for the presence of Boltzmann’s constant. The following are some examples of some of the most beneficial uses of entropy in the last two decades: thermodynamics entropy has been used as a bridge to unify the laws of Newtonian mechanics and thermodynamics to establish the unified mechanics theory. Information-theory entropy has been used successfully for fault diagnostics and prognostics of systems for in-situ structural health monitoring using various real-time signal feed-back cycles and computations. There is even a new pyroelectric sensor entropy detector to monitor energy conversion process in real time. There is a strong worldwide consensus among leading researchers that using entropy is scientifically the most accurate and reliable method for predicting degradation, fatigue, fracture, failure mechanics, and in-situ structural health monitoring of all systems. This Special Issue of the Entropy is devoted to covering the most recent advances in using entropy damage mechanics, and the structural health monitoring [fault diagnostics] of all systems.

Prof. Dr. Cemal Basaran
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (5 papers)

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Research

Open AccessArticle
Intelligent Analysis Algorithm for Satellite Health under Time-Varying and Extremely High Thermal Loads
Entropy 2019, 21(10), 983; https://doi.org/10.3390/e21100983 - 10 Oct 2019
Abstract
This paper presents a dynamic health intelligent evaluation model proposed to analyze the health deterioration of satellites under time-varying and extreme thermal loads. New definitions such as health degree and failure factor and new topological system considering the reliability relationship are proposed to [...] Read more.
This paper presents a dynamic health intelligent evaluation model proposed to analyze the health deterioration of satellites under time-varying and extreme thermal loads. New definitions such as health degree and failure factor and new topological system considering the reliability relationship are proposed to characterize the dynamic performance of health deterioration. The dynamic health intelligent evaluation model used the thermal network method (TNM) and fuzzy reasoning to solve the problem of model missing and non-quantization between temperature and failure probability, and it can quickly evaluate and analyze the dynamic health of satellite through the collaborative processing of continuous event and discrete event. In addition, the temperature controller in the thermal control subsystem (TCM) is the target of thermal damage, and the effects of different heat load amplitude, duty ratio, and cycle on its health deterioration are compared and analyzed. Full article
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Open AccessFeature PaperArticle
Maximum Entropy Models for Fatigue Damage in Metals with Application to Low-Cycle Fatigue of Aluminum 2024-T351
Entropy 2019, 21(10), 967; https://doi.org/10.3390/e21100967 - 03 Oct 2019
Abstract
In the present work, we propose using the cumulative distribution functions derived from maximum entropy formalisms, utilizing thermodynamic entropy as a measure of damage to fit the low-cycle fatigue data of metals. The thermodynamic entropy is measured from hysteresis loops of cyclic tension–compression [...] Read more.
In the present work, we propose using the cumulative distribution functions derived from maximum entropy formalisms, utilizing thermodynamic entropy as a measure of damage to fit the low-cycle fatigue data of metals. The thermodynamic entropy is measured from hysteresis loops of cyclic tension–compression fatigue tests on aluminum 2024-T351. The plastic dissipation per cyclic reversal is estimated from Ramberg–Osgood constitutive model fits to the hysteresis loops and correlated to experimentally measured average damage per reversal. The developed damage models are shown to more accurately and consistently describe fatigue life than several alternative damage models, including the Weibull distribution function and the Coffin–Manson relation. The formalism is founded on treating the failure process as a consequence of the increase in the entropy of the material due to plastic deformation. This argument leads to using inelastic dissipation as the independent variable for predicting low-cycle fatigue damage, rather than the more commonly used plastic strain. The entropy of the microstructural state of the material is modeled by statistical cumulative distribution functions, following examples in recent literature. We demonstrate the utility of a broader class of maximum entropy statistical distributions, including the truncated exponential and the truncated normal distribution. Not only are these functions demonstrated to have the necessary qualitative features to model damage, but they are also shown to capture the random nature of damage processes with greater fidelity. Full article
Open AccessFeature PaperArticle
Measures of Entropy to Characterize Fatigue Damage in Metallic Materials
Entropy 2019, 21(8), 804; https://doi.org/10.3390/e21080804 - 17 Aug 2019
Abstract
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 [...] Read more.
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. Full article
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Open AccessArticle
A Copula Entropy Approach to Dependence Measurement for Multiple Degradation Processes
Entropy 2019, 21(8), 724; https://doi.org/10.3390/e21080724 - 25 Jul 2019
Abstract
Degradation analysis has been widely used in reliability modeling problems of complex systems. A system with complex structure and various functions may have multiple degradation features, and any of them may be a cause of product failure. Typically, these features are not independent [...] Read more.
Degradation analysis has been widely used in reliability modeling problems of complex systems. A system with complex structure and various functions may have multiple degradation features, and any of them may be a cause of product failure. Typically, these features are not independent of each other, and the dependence of multiple degradation processes in a system cannot be ignored. Therefore, the premise of multivariate degradation modeling is to capture and measure the dependence among multiple features. To address this problem, this paper adopts copula entropy, which is a combination of the copula function and information entropy theory, to measure the dependence among different degradation processes. The copula function was employed to identify the complex dependence structure of performance features, and information entropy theory was used to quantify the degree of dependence. An engineering case was utilized to illustrate the effectiveness of the proposed method. The results show that this method is valid for the dependence measurement of multiple degradation processes. Full article
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Open AccessArticle
Thermodynamics of Fatigue: Degradation-Entropy Generation Methodology for System and Process Characterization and Failure Analysis
Entropy 2019, 21(7), 685; https://doi.org/10.3390/e21070685 - 12 Jul 2019
Cited by 1
Abstract
Formulated is a new instantaneous fatigue model and predictor based on ab initio irreversible thermodynamics. The method combines the first and second laws of thermodynamics with the Helmholtz free energy, then applies the result to the degradation-entropy generation theorem to relate a desired [...] Read more.
Formulated is a new instantaneous fatigue model and predictor based on ab initio irreversible thermodynamics. The method combines the first and second laws of thermodynamics with the Helmholtz free energy, then applies the result to the degradation-entropy generation theorem to relate a desired fatigue measure—stress, strain, cycles or time to failure—to the loads, materials and environmental conditions (including temperature and heat) via the irreversible entropies generated by the dissipative processes that degrade the fatigued material. The formulations are then verified with fatigue data from the literature, for a steel shaft under bending and torsion. A near 100% agreement between the fatigue model and measurements is achieved. The model also introduces new material and design parameters to characterize fatigue. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Author: Prof. Ganesh Subbarayan
Affiliation: School of Mechanical Engineering, Purdue University

Author: Prof. Mohammad Modarres
Affiliation: Mechanical Engineering, University of Maryland at College Park

Author: Prof. Michael Bryant and Dr. Jude Osara
Affiliation: Mechanical Engineering Department, University of Texas at Austin
Tentative title: Thermodynamics of Fatigue: Degradation-Entropy Generation Methodology for System and Process Characterization and Failure Analysis

Author: Prof. Wassim Haddad
Affiliation: School of Aerospace Engineering, Georgia Institute of Technology

Author: Prof. Martin Ostoja-Starzewski
Affiliation: Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign

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