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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning

X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. Here, we hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the β-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of network design on prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as 2.6 x 10 -4 . The average prediction error of β-phase volume fraction was within 1.6 x 10 -2 (in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Unimodular theory: A little pedagogical vision

Under the generic designation of unimodular theory, two theoretical models of gravity are considered: the unimodular gravity and the TDiff theory. Our approach is primarily pedagogical. We aim to describe these models both from a geometric and a field-theoretical point of view. In addition, we explore connections with the cosmological-constant problem and outline some applications. We do not discuss the application of this theory to the quantization of gravity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

New Directions: GEIA’s 2020 Vision for Better Air Emissions Information

We are witnessing a crucial change in how we quantify and understand emissions of greenhouse gases and air pollutants, with an increasing demand for science-based transparent emissions information produced by robust community efforts. Today’s scientific capabilities, with near-real-time in-situ and remote sensing observations combined with forward and inverse models and a better understanding of the controlling processes, are contributing to this transformation and providing new approaches to derive, verify, and forecast emissions (Tong et al., 2011; Frost et al., 2012) and to quantify their impacts on the environment (e.g., Bond et al., 2013). At the same time, the needs for emissions information and the demands for their accuracy and consistency have grown. Changing economies, demographics, agricultural practices, and energy sources, along with mandates to evaluate emissions mitigation efforts, demonstrate compliance with legislation, and verify treaties, are leading to new challenges in emissions understanding. To quote NOAA Senior Technical Scientist David Fahey, "We are in the Century of Accountability. Emissions information is critical not only for environmental science and decision-making, but also as an instrument of foreign policy and international diplomacy." Emissions quantification represents a key step in explaining observed variability and trends in atmospheric composition and in attributing these observed changes to their causes. Accurate emissions data are necessary to identify feasible controls that reduce adverse impacts associated with air quality and climate and to track the success of implemented policies. To progress further, the international community must improve the understanding of drivers and contributing factors to emissions, and it must strengthen connections among and within different scientific disciplines that characterize our environment and entities that protect the environment and influence further emissions. The Global Emissions InitiAtive, GEIA (http://www.geiacenter.org/), is a center for emissions information exchange and competence building created in 1990 in response to the need for high quality global emissions data (Graedel et al., 1993). While the past two decades have seen considerable progress in developing, improving and assessing emission estimates, emissions continue to be a major contributor to overall uncertainty in atmospheric model simulations. Moving forward, GEIA aims to help build emissions knowledge in a rapidly evolving society by: 1) enhancing understanding, quantification, and analysis of emissions processes; 2) improving access to emissions information; and 3) strengthening the community of emissions groups involved in research, assessment, operations, regulation and policy.

Frost, G. J.↗
DOE OSTI2017

Energy ideals, visions, narratives, and rhetoric: Examining sociotechnical imaginaries theory and methodology in energy research

Sociotechnical imaginaries emerged in the last decade as a potentially fruitful approach to understanding how collective social values inflect on the production of scientific knowledge and the design of technological systems. Yet insights generated to date have focused on the categories experts use to define a society’s idealized organization, either as the direct subject of analysis by documentary analysis or through the ways such categories circumscribe the field of authorized “values” open for adjudication in public engagement events. We argue that sociotechnical imaginaries require a new methodological framework for designing research in order to examine the collective values of citizens as they live their daily lives, rather than focusing on experts and the state in order to understand the shared moral, material, and scientific goals of a society. Drawing inspiration from rhetoric, corpus linguistics, and dialectology, we present the Social Energy Atlas, a new and burgeoning research project that employs such methods for studying emergent narrative patterns and variation at the local level. Furthermore, advancing the theory and practice of studying sociotechnical imaginaries is of tremendous benefit to Energy and Social Science researchers, and it is our intent this commentary encourages further careful development and use of the concept.

14 SOLAR ENERGY↗
DOE OSTI2023

Towards deep computer vision for in-line defect detection in polymer electrolyte membrane fuel cell materials

Polymer Electrolyte Membrane (PEM) fuel cells are a promising source of alternative energy. However, their production is limited by a lack of well-established methods for quality control of their constituent materials like the membrane-electrode assembly during roll-to-roll manufacturing. One potential solution is the implementation of deep learning methods to detect unwanted defects through their detection in scanned images. Here we explore the detection of defects like scratches, pinholes, and scuffs in a sample dataset of PEM optical images using two deep learning algorithms: Patch Distribution Modeling (PaDiM) for unsupervised anomaly detection and Faster-RCNN for supervised object detection. Both methods achieve scores on performance metrics (ROC-AUC and PRO-AUC for PaDiM and AP for Faster-RCNN) that are comparable to their scores on benchmark datasets. These methods also have the potential to detect a wider range of defects compared to IR thermography and previous optical inspection methods. Overall, deep learning shows promise at detecting relevant defects of interest and has the potential to achieve real-time defect detection.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2014

Contribution of capillary electrophoresis to an integrated vision of humic substances size and charge characterizations

The physicochemical properties of three different humic substances (HS) are probed using capillary zone electrophoresis in alkaline carbonate buffers, pH 10. Special attention is drawn to the impact of the electrolyte ionic strength and counter-ion nature, chosen within the alkali-metal series, on HS electrophoretic mobility. Taylor-Aris dispersion analysis provides insights into the hydrodynamic radius (R-H) distributions of HS. The smallest characterized entities are of nano-metric dimensions, showing neither ionic strength- nor alkali-metal-induced aggregation. These results are compared with the entities evidenced in dynamic light scattering measurements, the size of which is two order of magnitude higher, ca. 100 nm. The extended Onsager model provides a reasonable description of measured electrophoretic mobilities in the ionic strength range 1-50 mM, thus allowing the estimation of limiting mobilities and ionic charge numbers for the different HS samples. An unexpected HS electrophoretic mobility increase (in absolute value) is observed in the order Li{sup +} ≤ Na{sup +} ≤ K{sup +} ≤ Cs{sup +} and discussed either in terms of retarding forces or in terms of ion-ion interactions. (authors)

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗