High-Entropy Alloys for Advanced Nuclear Applications

The expanded compositional freedom afforded by high-entropy alloys (HEAs) represents a unique opportunity for the design of alloys for advanced nuclear applications, in particular for applications where current engineering alloys fall short. This review assesses the work done to date in the field of HEAs for nuclear applications, provides critical insight into the conclusions drawn, and highlights possibilities and challenges for future study. It is found that our understanding of the irradiation responses of HEAs remains in its infancy, and much work is needed in order for our knowledge of any single HEA system to match our understanding of conventional alloys such as austenitic steels. A number of studies have suggested that HEAs possess ‘special’ irradiation damage resistance, although some of the proposed mechanisms, such as those based on sluggish diffusion and lattice distortion, remain somewhat unconvincing (certainly in terms of being universally applicable to all HEAs). Nevertheless, there may be some mechanisms and effects that are uniquely different in HEAs when compared to more conventional alloys, such as the effect that their poor thermal conductivities have on the displacement cascade. Furthermore, the opportunity to tune the compositions of HEAs over a large range to optimise particular irradiation responses could be very powerful, even if the design process remains challenging

PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

Adaptive Data Mining Approach for Pcb Defect Detection and Classification

Objective: To develop a model for PCB defect detection and classification with the help of soft computing technique. Methodology: To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. The proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, pre-processing, feature selection and reduction is carried out by measuring of confidence with the adaptive genetic algorithm. Prediction and classification is carried out by using neural network classifier. A genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. Findings: The system is implemented using MatLab 2013b. The resulting analysis shows that the proposed approach is capable of detecting and classifying defects in PCB board

SMALL POLARONS IN REAL CRYSTALS - CONCEPTS AND PROBLEMS

Much of small polaron theory is based on highly idealized models, often essentially a continuum description with a single vibrational frequency. These models ignore much of the wealth of experimental data, which find interpretation in many atomistic simulations. We review here a range of properties of small polarons in real, rather than model, systems. The phenomena fall into three main classes: (i) the mechanisms and dynamics of self-trapping of polarons; (ii) static properties-the relative energies of large and small polarons, the optical transitions expected, their effect on positions of other ions and on lattice vibrations, their population in thermal equilibrium, and so on; (iii) small polaron hopping and diffusion. We discuss the key concepts and methods of calculation of polarons, and explore the properties of self-trapped holes and excitons in ionic crystals, and those of an excess electron in liquid water

Computational characterization and prediction of metal-organic framework properties

In this introductory review, we give an overview of the computational chemistry methods commonly used in the field of metal-organic frameworks (MOFs), to describe or predict the structures themselves and characterize their various properties, either at the quantum chemical level or through classical molecular simulation. We discuss the methods for the prediction of crystal structures, geometrical properties and large-scale screening of hypothetical MOFs, as well as their thermal and mechanical properties. A separate section deals with the simulation of adsorption of fluids and fluid mixtures in MOFs

Characterization of Radiation Effects and \u3ci\u3eAb Initio\u3c/i\u3e Modeling of Defects in a High Entropy Alloy for Nuclear Power Application

A novel Co-free NiFeMnCr HEA was synthesized for both ion and neutron irradiation studies. 3 and 5.8MeV heavy ion irradiations were conducted at room temperature and 400 – 700 °C from 0.03 to 10dpa. Post-irradiation examination included x-ray diffraction, nanoindentation hardness and transmission electron microscopy. The HEA exhibited quantitatively superior radiation resistance than conventional alloys, including suppressed void swelling and solute segregation.Neutron irradiation was conducted at 60 °C from 0.1 to 1dpa. Microhardness, electrical resistivity and positron annihilation spectroscopy measurements were performed at room temperature before and after isochronal annealing up to 700°C on the neutron-irradiated samples. The HEA retains fundamental stability after neutron irradiation. Compared with metals and conventional alloys, the HEA showed similar annealing trend of hardness and vacancy-type of defects. On the other hand, this HEA showed unique annealing trend of electrical resistivity. The large radiation induced resistivity increase (\u3e10 μΩ∙cm) did not recover up to 700 °C, suggesting short range ordering phenomena may be critical in radiation effects of HEA.In parallel, ab initio modeling was conducted to establish a solid foundation for multiscale modeling of HEA as well as to reveal unique defect physics of HEA. Magnetic structure was computed based on coherent potential. Vacancy energetics were computed by Vienna Ab initio Simulation Package (VASP). Modeling results shows that it is reasonable to neglect magnetic interactions. The statistical distribution of vacancy formation energy is weakly dependent upon either the chemical species of the atom site associated with the vacancy, or local chemical environment. The calculated migration energy values show a large spread, varying between 0.55 to 1.68eV, although the mean value is comparable to that of conventional austenitic alloys. Finally, positron lifetime of bulk HEA, mono-vacancy and small vacancy clusters were computed by a finite element based ab initio package to facilitate the interpretation of experimental results from positron annihilation spectroscopy

A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly

The objective of this research is to test the hypothesis that manufacturing complexity can reliably predict product quality in mixed-model automotive assembly. Originally, assembly lines were developed for cost efficient mass-production of standardized products. Today, in order to respond to diversified customer needs, companies have to allow for an individualization of their products, leading to the development of the Flexible Manufacturing Systems (FMS). Assembly line balancing problems (ALBP) consist of assigning the total workload for manufacturing a product to stations of an assembly line as typically applied in the automotive industry. Precedence relationships among tasks are required to conduct partly or fully automated Assembly Line Balancing. Efforts associated with manual precedence graph generation at a major automotive manufacturer have highlighted a potential relationship between manufacturing complexity (driven by product design, assembly process, and human factors) and product quality, a potential link that is usually ignored during Assembly Line Balancing and one that has received very little research focus so far. The methodology used in this research will potentially help develop a new set of constraints for an optimization model that can be used to minimize manufacturing complexity and maximize product quality, while satisfying the precedence constraints. This research aims to validate the hypothesis that the contribution of design variables, process variables, and human-factors can be represented by a complexity metric that can be used to predict their contribution on product quality. The research will also identify how classes of defect prevention methods can be incorporated in the predictive model to prevent defects in applications that exhibit high level of complexity. The manufacturing complexity model is applied to mechanical fastening processes which are accountable for the top 28% of defects found in automotive assembly, according to statistical analysis of historical data collected over the course of one year of vehicle production at a major automotive assembly plant. The predictive model is validated using mechanical fastening processes at an independent automotive assembly plant. This complexity-based predictive model will be the first of its kind that will take into account design, process, and human factors to define complexity and validate it using a real-world automotive manufacturing process. The model will have the potential to be utilized by design and process engineers to evaluate the effect of manufacturing complexity on product quality before implementing the process in a real-world assembly environment

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

How Water's Properties Are Encoded in Its Molecular Structure and Energies.

How are water's material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth's living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies-water's solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid. It has more solid phases than other materials. Its supercooled liquid has divergent thermodynamic response functions. Its glassy state is neither fragile nor strong. Its component ions-hydroxide and protons-diffuse much faster than other ions. Aqueous solvation of ions or oils entails large entropies and heat capacities. We review how these properties are encoded within water's molecular structure and energies, as understood from theories, simulations, and experiments. Like simpler liquids, water molecules are nearly spherical and interact with each other through van der Waals forces. Unlike simpler liquids, water's orientation-dependent hydrogen bonding leads to open tetrahedral cage-like structuring that contributes to its remarkable volumetric and thermal properties

Roadmap on inorganic perovskites for energy applications

Authors thank EPSRC (EP/P007821/1) and Low Emissions Resources Global for support.Inorganic perovskites exhibit many important physical properties such as ferroelectricity, magnetoresistance and superconductivity as well their importance as Energy Materials. Many of the most important energy materials are inorganic perovskites and find application in batteries, fuel cells, photocatalysts, catalysis, thermoelectrics and solar thermal. In all these applications, perovskite oxides, or their derivatives offer highly competitive performance, often state of the art and so tend to dominate research into energy material. In the following sections, we review these functionalities in turn seeking to facilitate the interchange of ideas between domains. The potential for improvement is explored and we highlight the importance of both detailed modelling and in situ and operando studies in taking these materials forward.Publisher PDFPeer reviewe