AN ENTROPIC THEORY OF DAMAGE WITH APPLICATIONS TO CORROSION-FATIGUE STRUCTURAL INTEGRITY ASSESSMENT

This dissertation demonstrates an explanation of damage and reliability of critical components and structures within the second law of thermodynamics. The approach relies on the fundamentals of irreversible thermodynamics, specifically the concept of entropy generation due to materials degradation as an index of damage. All failure mechanisms that cause degradation, damage accumulation and ultimate failure share a common feature, namely energy dissipation. Energy dissipation, as a fundamental measure for irreversibility in a thermodynamic treatment of non-equilibrium processes, leads to and can be expressed in terms of entropy generation. The dissertation proposes a theory of damage by relating entropy generation to energy dissipation via generalized thermodynamic forces and thermodynamic fluxes that formally describes the resulting damage. Following the proposed theory of entropic damage, an approach to reliability and integrity characterization based on thermodynamic entropy is discussed. It is shown that the variability in the amount of the thermodynamic-based damage and uncertainties about the parameters of a distribution model describing the variability, leads to a more consistent and broader definition of the well know time-to-failure distribution in reliability engineering. As such it has been shown that the reliability function can be derived from the thermodynamic laws rather than estimated from the observed failure histories. Furthermore, using the superior advantages of the use of entropy generation and accumulation as a damage index in comparison to common observable markers of damage such as crack size, a method is proposed to explain the prognostics and health management (PHM) in terms of the entropic damage. The proposed entropic-based damage theory to reliability and integrity is then demonstrated through experimental validation. Using this theorem, the corrosion-fatigue entropy generation function is derived, evaluated and employed for structural integrity, reliability assessment and remaining useful life (RUL) prediction of Aluminum 7075-T651 specimens tested

Energy Conversion Alternatives Study (ECAS), General Electric Phase 1. Volume 3: Energy conversion subsystems and components. Part 1: Bottoming cycles and materials of construction

Energy conversion subsystems and components were evaluated in terms of advanced energy conversion systems. Results of the bottoming cycles and materials of construction studies are presented and discussed

Exploration of the High Entropy Alloy Space as a Constraint Satisfaction Problem

High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or Compositionally Complex Alloys (CCAs) are alloys that contain multiple principal alloying elements. While many HEAs have been shown to have unique properties, their discovery has been largely done through costly and time-consuming trial-and-error approaches, with only an infinitesimally small fraction of the entire possible composition space having been explored. In this work, the exploration of the HEA composition space is framed as a Continuous Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy thermodynamic spaces. The algorithm is used to discover regions in the HEA Composition-Temperature space that satisfy desired phase constitution requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA thermodynamic database. The database is first validated by comparing phase stability predictions against experiments and then the CSA is deployed and tested against design tasks consisting of identifying not only single phase solid solution regions in ternary, quaternary and quinary composition spaces but also the identification of regions that are likely to yield precipitation-strengthened HEAs.Comment: 14 pages, 13 figure

On the Thermodynamics of Degradation

All materials when subjected to fatigue loading are prone to failure if the number of cycles exceeds a certain level. Prediction of the number of cycles to failure is, therefore, of utmost importance in nearly all engineering applications. The existing methods for evaluating the fatigue life are tedious, expensive, and extremely time consuming as fatigue often takes many thousands to millions of cycles until failure occurs. Therefore, methods that can readily estimate the number of cycles to failure are highly desirable. In this work, innovative solutions to fatigue problems are presented and their practical significance is discussed. The premise of this research is that the energy dissipation due to hysteresis effect manifests itself as heat, raising the temperature of the specimen. The temperature evolution during fatigue can be utilized as an index to assess the useful fatigue life and fast prediction of premature failure. Specifically, fatigue experiments of two types of metals (Aluminum 6061-T6 and Stainless Steel 304L) show that the slope of the temperature at the beginning of the test is intimately related to the fatigue life, thereby, it provides a fast prediction technique to assess failure. An experimental investigation was conducted to study the effect of surface cooling on the improvement of fatigue life. Experiments show that the surface cooling has significant effect on the fatigue life. For example, 1000% improvement is observed for Steel 4145 undergoing rotating-bending fatigue. It is proposed that the concept of self-organization within the context of irreversible thermodynamics can be used to gain insight into the observed phenomenon. Further, it is shown that fatigue degradation and thermodynamic entropy generation are intimately connected and that their relationship can be used for prediction of failure and making fundamental advances in the study of fatigue without having to resort to traditional approaches that depend on empirical models

Manufacturing High Entropy Alloys: Pathway to Industrial Competitiveness

High entropy alloys (HEAs) provide a transformative opportunity to design materials that are custom tailored to the distinct needs of a given application, thereby shifting the paradigm from “apply the material you have” to “engineer the material you need.” HEAs will enable high-performance manufactured goods that are competitive in the international marketplace through extraordinary material properties and unique property combinations. HEAs deliver new choices to manufacturers to create alternatives to materials that are rare, hazardous, expensive, or subject to international restrictions or conflict. The potential benefits of HEAs span diverse fields and applications, and show promise to not only accelerate economic growth and domestic competitive advantage, but also address pressing societal challenges. These include solid state cooling, liquefied natural gas handling, nuclear degradation- resistant materials, corrosion-resistant heat exchangers, and efficiency gains from high temperature performance that advance national energy goals; high-performance aerospace materials and ultra- hardness ballistics that support national security; and strong, corrosion-resistant medical devices and advances in magnetic resonance imaging that are essential to national health priorities. Research advances are setting the stage to realize each of these vital areas. However, research advances made to-date to produce lab-scale prototypes do not lend themselves to manufacturing at scale. For Americans to fully benefit from HEAs, the emerging technologies must be translated into products manufactured at scale in the United States. However, manufacturers and HEA experts who are working to bridge this gap are encountering cross-cutting barriers in manufacturing processes, testing, data, and access to the necessary resources. Through strategic public- and private- sector research and investment, these barriers can be overcome. The United States has invested in both HEA research and advanced materials resources, such as material sample creation at the Ames Laboratory Materials Preparation Center, material characterization at Oak Ridge National Laboratory’s Neutron User Facilities, and modeling and analysis through the National Institute of Standards and Technology’s Material Genome Initiative. A vast array of research and expertise has been fostered at federal laboratories and universities, yielding promising alloys, manufacturing processes, and analysis methods.National Science Foundation, Grant No. 1552534https://deepblue.lib.umich.edu/bitstream/2027.42/146747/1/Manufacturing-HEAs.pdf-1Description of Manufacturing-HEAs.pdf : Main articl

Entropic Approaches for Assessment of Metal Fatigue Damage

Prognostics and Health Management (PHM), a promising technique assessing individual life of engineering systems, requires metrics that indicate the current level of degradation and aging. However, traditional methods of fatigue life estimation have a restriction to apply to PHM due to scale dependency of measurements. An alternative to the conventional fatigue assessment is the entropic approach, initially de-rived from the second law of thermodynamics. The entropic approach is scale-independent and able to monitor degradation and aging from the early periods of life. The entropic endurance indicates a certain level of damage that a component can tolerate before failure. Not only the thermodynamic theory but also information and statistical mechanics laws introducing entropy apply to the various modes of energy dissipations. This dissertation introduces the extension of the entropic approaches as the representation of damage by empirically examining the theoretical basis of three en-tropic theorems. Metallic coupons were fatigue tested to confirm the applicability of three entropic measures: irreversible thermodynamic entropy, information (Shannon) entropy, and Jeffreys divergence, by measuring variables used to compute energy dissipations during fatigue. In addition to the entropic approaches to damage, short-term loading process (STLP) is designed to minimize the difficulties associated with acoustic emission background noise when used to measure information entropy of the generated signals. Without damaging the material, high-frequency/low-amplitude loading is expected to generate acoustic signals through quiet background noise excitation loading to infer the current damage status. The results of this research help identifying multiple damage measurement methods and will broaden understanding and selecting practical applications, and reduce the prognosis uncertainty in PHM applications

Spark Plasma Sintered High-Entropy Alloys: An Advanced Material for Aerospace Applications

High-entropy alloys (HEAs) are materials of high property profiles with enhanced strength-to-weight ratios and high temperature-stress-fatigue capability as well as strong oxidation resistance strength. HEAs are multi-powder-based materials whose microstructural and mechanical properties rely strongly on stoichiometry combination of powders as well as the consolidation techniques. Spark plasma sintering (SPS) has a notable processing edge in processing HEAs due to its fast heating schedule at relatively lower temperature and short sintering time. Therefore, major challenges such as grain growth, porosity, and cracking normally encountered in conventional consolidation like casting are bypassed to produce HEAs with good densification. SPS parameters such as heating rate, temperature, pressure, and holding time can be utilized as design criteria in software like Minitab during design of experiment (DOE) to select a wide range of values at which the HEAs may be produced as well as to model the output data collected from mechanical characterization. In addition to this, the temperature-stress-fatigue response of developed HEAs can be analyzed using finite element analysis (FEA) to have an in-depth understanding of the detail of inter-atomic interactions that inform the inherent material properties

Fatigue Behavior of High-Entropy Alloys

High-entropy alloys (HEAs) refer to alloys composed of five or more elements in equal or near-equal amounts or in an atomic concentration range of 5 to 35 atomic percent (at%). Different elemental ratios will affect the microstructures of HEAs and provide them with unique properties. Based on past research, HEAs have exhibited superior performance, relative to most conventional alloys, with respect to many properties, such as strength, toughness, corrosion resistance, magnetic behavior, etc. Among them, fatigue behavior has been a topic of focus, due to its importance in industrial applications. In this article, we summarized the research progress in the HEA-fatigue behavior in the past ten years, including experimental results and theoretical studies in subdivisions, such as high-cycle fatigue, low-cycle fatigue, fatigue-crack growth, fatigue mechanisms, etc. The influence of the processing and test methods on HEAs is described. The accuracy of several commonly used prediction models is also outlined. Finally, unresolved issues and suggestions on the direction of future research efforts are presented.Comment: Submitted to Progress in Material Science on January 8, 201

Gas Turbines

This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well

Alloys innovation through machine learning:a statistical literature review

This review systematically analyzes over 200 publications to explore the growing role of data-driven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other computational approaches used in recent years. These methods harness the power of advanced simulation techniques and data analytics to expedite materials’ discovery, predict properties, and optimize performance. Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration. Machine Learning techniques are widely applied across various alloys, with a pronounced emphasis on steel and High Entropy Alloys. Notably, researchers primarily investigate the physical, mechanical, and catalytic properties of materials. In terms of methodology, while 68% of the examined papers rely on a single machine learning model, the remainder employ a range of 2 to 12 models, with Neural Network being the most prevalent choice. However, a notable concern arises as 53% of these papers do not share their dataset, and a staggering 81% do not provide access to their code. Paramount importance of adopting a systematic approach when scrutinizing machine learning methodologies is underscored. Analysis shows lack of consistency and diversity in the methods employed by researchers in the field of alloy development, highlighting the potential for improvement through standardization. The critical analysis of the literature not only reveals prevailing trends and patterns but also shines a light on the inherent limitations within the traditional trial-and-error paradigm