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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 48418

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Prof. Dr. Cemal Basaran

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Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY 14260-4300, USA
Interests: unified mechanics theory; mechanics of electronic materials; damage mechanics; fatigue; cyclic loading, fracture mechanics; degradation; thermodynamics; viscoplasticity; graphene; nanomechanics

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

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Published Papers (11 papers)

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Editorial

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4 pages, 178 KiB

Open AccessEditorial

Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring

by Cemal Basaran

Entropy 2020, 22(10), 1178; https://doi.org/10.3390/e22101178 - 19 Oct 2020

Cited by 8 | Viewed by 2697

Abstract

This special issue is dedicated to entropy-based fatigue, fracture, failure prediction and structural health monitoring[...] Full article

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

Research

Jump to: Editorial

10 pages, 4526 KiB

Open AccessArticle

Effective Surface Nano-Crystallization of Ni₂FeCoMo_0.5V_0.2 Medium Entropy Alloy by Rotationally Accelerated Shot Peening (RASP)

by Ningning Liang, Xiang Wang, Yang Cao, Yusheng Li, Yuntian Zhu and Yonghao Zhao

Entropy 2020, 22(10), 1074; https://doi.org/10.3390/e22101074 - 24 Sep 2020

Cited by 9 | Viewed by 2763

Abstract

The surface nano-crystallization of Ni₂FeCoMo_0.5V_0.2 medium-entropy alloy was realized by rotationally accelerated shot peening (RASP). The average grain size at the surface layer is ~37 nm, and the nano-grained layer is as thin as ~20 μm. Transmission electron microscopy analysis revealed that deformation twinning and dislocation activities are responsible for the effective grain refinement of the high-entropy alloy. In order to reveal the effectiveness of surface nano-crystallization on the Ni₂FeCoMo_0.5V_0.2 medium-entropy alloy, a common model material, Ni, is used as a reference. Under the same shot peening condition, the surface layer of Ni could only be refined to an average grain size of ~234 nm. An ultrafine grained surface layer is less effective in absorbing strain energy than a nano-grain layer. Thus, grain refinement could be realized at a depth up to 70 μm in the Ni sample. Full article

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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20 pages, 4513 KiB

Open AccessArticle

Low Cycle Fatigue Life Prediction Using Unified Mechanics Theory in Ti-6Al-4V Alloys

by Noushad Bin Jamal M, Aman Kumar, Chebolu Lakshmana Rao and Cemal Basaran

Entropy 2020, 22(1), 24; https://doi.org/10.3390/e22010024 - 23 Dec 2019

Cited by 31 | Viewed by 5277

Abstract

Fatigue in any material is a result of continuous irreversible degradation process. Traditionally, fatigue life is predicted by extrapolating experimentally curve fitted empirical models. In the current study, unified mechanics theory is used to predict life of Ti-6Al-4V under monotonic tensile, compressive and cyclic load conditions. The unified mechanics theory is used to derive a constitutive model for fatigue life prediction using a three-dimensional computational model. The proposed analytical and computational models have been used to predict the low cycle fatigue life of Ti-6Al-4V alloys. It is shown that the unified mechanics theory can be used to predict fatigue life of Ti-6Al-4V alloys by using simple predictive models that are based on fundamental equation of the material, which is based on thermodynamics associated with degradation of materials. Full article

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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22 pages, 6420 KiB

Open AccessArticle

Prediction of Fatigue Crack Growth Rate Based on Entropy Generation

by Roslinda Idris, Shahrum Abdullah, Prakash Thamburaja and Mohd Zaidi Omar

Entropy 2020, 22(1), 9; https://doi.org/10.3390/e22010009 - 19 Dec 2019

Cited by 13 | Viewed by 4869

Abstract

This paper presents the assessment of fatigue crack growth rate for dual-phase steel under spectrum loading based on entropy generation. According to the second law of thermodynamics, fatigue crack growth is related to entropy gain because of its irreversibility. In this work, the temperature evolution and crack length were simultaneously measured during fatigue crack growth tests until failure to ensure the validity of the assessment. Results indicated a significant correlation between fatigue crack growth rate and entropy. This relationship is the basis in developing a model that can determine the characteristics of fatigue crack growth rates, particularly under spectrum loading. Predictive results showed that the proposed model can accurately predict the fatigue crack growth rate under spectrum loading in all cases. The root mean square error in all cases is 10⁻⁷ m/cycle. In conclusion, entropy generation can accurately predict the fatigue crack growth rate of dual-phase steels under spectrum loading. Full article

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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49 pages, 15571 KiB

Open AccessArticle

On the Development of Mechanothermodynamics as a New Branch of Physics

by Leonid A. Sosnovskiy and Sergei S. Sherbakov

Entropy 2019, 21(12), 1188; https://doi.org/10.3390/e21121188 - 2 Dec 2019

Cited by 23 | Viewed by 4302

Abstract

This paper aims to substantiate and formulate the main principles of the physical discipline-mechanothermodynamics that unites Newtonian mechanics and thermodynamics. Its principles are based on using entropy as a bridge between mechanics and thermodynamics. Mechanothermodynamics combines two branches of physics, mechanics and thermodynamics, to take a fresh look at the evolution of complex systems. The analysis of more than 600 experimental results allowed for determining a unified mechanothermodynamical function of limiting states (critical according to damageability) of polymers and metals. They are also known as fatigue fracture entropy states. Full article

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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16 pages, 8388 KiB

Open AccessArticle

An Entropy-Based Failure Prediction Model for the Creep and Fatigue of Metallic Materials

by Jundong Wang and Yao Yao

Entropy 2019, 21(11), 1104; https://doi.org/10.3390/e21111104 - 12 Nov 2019

Cited by 27 | Viewed by 4103

Abstract

It is well accepted that the second law of thermodynamics describes an irreversible process, which can be reflected by the entropy increase. Irreversible creep and fatigue damage can also be represented by a gradually increasing damage parameter. In the current study, an entropy-based failure prediction model for creep and fatigue is proposed based on the Boltzmann probabilistic entropy theory and continuum damage mechanics. A new method to determine the entropy increment rate for creep and fatigue processes is proposed. The relationship between entropy increase rate during creep process and normalized creep failure time is developed and compared with the experimental results. An empirical formula is proposed to describe the evolution law of entropy increase rate and normalized creep time. An entropy-based model is developed to predict the change of creep strain during the damage process. Experimental results of metals and alloys with different stresses and at different temperatures are adopted to verify the proposed model. It shows that the theoretical predictions agree well with experimental data. Full article

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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16 pages, 4960 KiB

Open AccessArticle

Intelligent Analysis Algorithm for Satellite Health under Time-Varying and Extremely High Thermal Loads

by En-Hui Li, Yun-Ze Li, Tian-Tian Li, Jia-Xin Li, Zhuang-Zhuang Zhai and Tong Li

Entropy 2019, 21(10), 983; https://doi.org/10.3390/e21100983 - 10 Oct 2019

Cited by 12 | Viewed by 2816

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 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

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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24 pages, 11061 KiB

Open AccessFeature PaperArticle

Maximum Entropy Models for Fatigue Damage in Metals with Application to Low-Cycle Fatigue of Aluminum 2024-T351

by Colin Young and Ganesh Subbarayan

Entropy 2019, 21(10), 967; https://doi.org/10.3390/e21100967 - 3 Oct 2019

Cited by 19 | Viewed by 3401

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 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

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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21 pages, 4060 KiB

Open AccessFeature PaperArticle

Measures of Entropy to Characterize Fatigue Damage in Metallic Materials

by Huisung Yun and Mohammad Modarres

Entropy 2019, 21(8), 804; https://doi.org/10.3390/e21080804 - 17 Aug 2019

Cited by 36 | Viewed by 5449

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 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

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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20 pages, 2901 KiB

Open AccessArticle

A Copula Entropy Approach to Dependence Measurement for Multiple Degradation Processes

by Fuqiang Sun, Wendi Zhang, Ning Wang and Wei Zhang

Entropy 2019, 21(8), 724; https://doi.org/10.3390/e21080724 - 25 Jul 2019

Cited by 11 | Viewed by 4841

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 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

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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24 pages, 3664 KiB

Open AccessArticle

Thermodynamics of Fatigue: Degradation-Entropy Generation Methodology for System and Process Characterization and Failure Analysis

by Jude A. Osara and Michael D. Bryant

Entropy 2019, 21(7), 685; https://doi.org/10.3390/e21070685 - 12 Jul 2019

Cited by 43 | Viewed by 6281

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 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

(This article belongs to the Special Issue Entropy Based Fatigue, Fracture, Failure Prediction and Structural Health Monitoring)

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