Yielding and irreversible deformation below the microscale: Surface effects and non-mean-field plastic avalanches

Nanoindentation techniques recently developed to measure the mechanical response of crystals under external loading conditions reveal new phenomena upon decreasing sample size below the microscale. At small length scales, material resistance to irreversible deformation depends on sample morphology. Here we study the mechanisms of yield and plastic flow in inherently small crystals under uniaxial compression. Discrete structural rearrangements emerge as series of abrupt discontinuities in stress-strain curves. We obtain the theoretical dependence of the yield stress on system size and geometry and elucidate the statistical properties of plastic deformation at such scales. Our results show that the absence of dislocation storage leads to crucial effects on the statistics of plastic events, ultimately affecting the universal scaling behavior observed at larger scales.Comment: Supporting Videos available at http://dx.plos.org/10.1371/journal.pone.002041

Aeronautical Engineering: A special bibliography with indexes, supplement 54

This bibliography lists 316 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1975

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

Laser Scanning Microscopy of HTS Films and Devices

The work describes the capabilities of Laser Scanning Microscopy (LSM) as a spatially resolved method of testing high_Tc materials and devices. The earlier results obtained by the authors are briefly reviewed. Some novel applications of the LSM are illustrated, including imaging the HTS responses in rf mode, probing the superconducting properties of HTS single crystals, development of twobeam laser scanning microscopy. The existence of the phase slip lines mechanism of resistivity in HTS materials is proven by LSM imaging.Comment: 17 pages, 21 figures, Submitted to Fizika Nizkikh Temperatur (Low Temperature Physics

On-the-fly Data Assessment for High Throughput X-ray Diffraction Measurement

Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through development of an approach that makes routine data assessment automatic and instantaneous. Through extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large datasets is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize usage of expensive characterization resources by prioritizing measurements of highest scientific impact. We anticipate our approach to become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. With these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition

A bottom-up framework for analysing city-scale energy data using high dimension reduction techniques

Worldwide cities are becoming more sustainable and are being monitored using data collection techniques at various geographical levels. Given the growing volume of data, there is a need to identify challenges associated with the processing, visualization, and analysis of the generated data from an urban scale. This study proposes a framework to investigate the capabilities of dimensionality reduction techniques (t-SNE, and UMAP) applied to city-scale data to identify key features of high consumption and generation areas based on building characteristics. The analysis is performed on measured data from 2735 postcodes consisting of 72000 households/buildings from a city in the Netherlands. The evaluation results showed that the UMAP's algorithm mean sigma quickly approaches a threshold of 0.6 at n_neighbor values of 50 and the low dimensional shape does not change with increasing values. Whereas the t-SNE's mean sigma value increases continuously with the increasing perplexity value, implying that t-SNE is significantly more sensitive to the perplexity parameter. The UMAP algorithm was used to extract information about the high photovoltaic generation and consumption regions. The proposed framework will assist grid operators and energy planners in extracting information from energy consumption data at the neighbourhood level by utilizing high dimensional reduction techniques

Structural dynamics branch research and accomplishments to FY 1992

This publication contains a collection of fiscal year 1992 research highlights from the Structural Dynamics Branch at NASA LeRC. Highlights from the branch's major work areas--Aeroelasticity, Vibration Control, Dynamic Systems, and Computational Structural Methods are included in the report as well as a listing of the fiscal year 1992 branch publications