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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Baryon spectrum from superconformal quantum mechanics and its light-front holographic embedding

We describe the observed light-baryon spectrum by extending superconformal quantum mechanics to the light front and its embedding in AdS space. This procedure uniquely determines the confinement potential for arbitrary half-integer spin. To this end, we show that fermionic wave equations in AdS space are dual to light-front supersymmetric quantum-mechanical bound-state equations in physical space-time. The specific breaking of conformal invariance explains hadronic properties common to light mesons and baryons, such as the observed mass pattern in the radial and orbital excitations, from the spectrum generating algebra. Lastly, the holographic embedding in AdS also explains distinctive and systematic features, such as the spin-J degeneracy for states with the same orbital angular momentum, observed in the light-baryon spectrum.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

SpeckleNN: a unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples

With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or `speckles', to extract single-hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high-data-rate facilities like the European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite having only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

TGE: Machine Learning Based Task Graph Embedding for Large-Scale Topology Mapping

Task mapping is an important problem in parallel and distributed computing. The goal in task mapping is to find an optimal layout of the processes of an application (or a task) onto a given network topology. We target this problem in the context of staging applications. A staging application consists of two or more parallel applications (also referred to as staging tasks) which run concurrently and exchange data over the course of computation. Task mapping becomes a more challenging problem in staging applications, because not only data is exchanged between the staging tasks, but also the processes of a staging task may exchange data with each other. We propose a novel method, called Task Graph Embedding (TGE), that harnesses the observable graph structures of parallel applications and network topologies. TGE employs a machine learning based algorithm to find the best representation of a graph, called an embedding, onto a space in which the task-to-processor mapping problem can be solved. We evaluate and demonstrate the effectiveness of TGE experimentally with the communication patterns extracted from runs of XGC, a large-scale fusion simulation code, on Titan.

Choi, Jong Youl↗
DOE OSTI2024

SNNVis: Visualizing Graph Embedding of Evolutionary Optimization for Spiking Neural Networks

While Spiking Neural Networks (SNNs) show a lot of promise, it is difficult to optimize them because applying traditional gradient-based optimization techniques is difficult. Even though evolutionary algorithms (EAs) have been shown to promise to optimize SNNs, understanding the relationship between evolving the characteristics of SNNs and their performance to improve the optimization algorithm is challenging because of the complex characteristics and huge population size. We propose visual analytics with novel graph embedding for evolutionary SNNs to address the challenges. While existing graph embedding techniques have limitations in preserving the specific features of the nodes and edges, our approach maintains them. Also, we develop visual analytics for understanding the relationship between the network performance and the features of nodes and edges and exploring and analyzing the evolving SNNs to build insights into improving the EA.

Chae, Junghoon [ORNL] (ORCID:0000000206016746)↗
DOE OSTI2022

Development and Validation of Resistance-Capacitance Model for Phase Change Material Embedded in Porous Media

A simple and effective way to enhance PCM thermal conductivity is by embedding porous materials with high thermal conductivities. In this work, CFD was used to evaluate the melting of PCM embedded in metal foam supplied with constant heat flux. Good agreement between experimental and numerical results has been found with a mean PCM local temperature deviation of 1.62K and 2.97K in two locations. The CFD simulation time was 56 hours for 6.5 hours of flow time. A computationally efficient resistance capacitance-based model (RCM) was developed to improve simulation speed. The proposed RCM exhibited mean local temperature deviations of 1.86K and 2.96K when compared against the experimental data for two thermocouple positions, however, it was approximately 2.5x10 5 times faster than CFD. The charging time deviation was less than 1% compared to CFD, and no significant change in thermal energy stored was observed since the temperature differences were small enough to result in insignificant sensible heat deviation. A simulation cost-effectiveness index (CEI) considering accuracy and computational affordability was introduced; the CEI of the RCM solver is 10 3 times better compared to CFD for the prediction of local PCM temperature.

25 ENERGY STORAGE↗
DOE OSTI2020

A Digital Twin Approach to Study Additive Manufacturing Processing Using Embedded Optical Fiber Sensors and Numerical Modeling

One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HAPPA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

High-performance computing (HPC) systems are increasingly vulnerable to soft errors, which pose significant challenges in maintaining computational accuracy and reliability. Predicting the resilience of HPC applications to these errors is crucial for robust code protection and detailed resilience analysis. In this study, we present HAppA, a modular platform designed for HPC Application Resilience Analysis. Embedding Large Language Models (LLMs), HAppA addresses understanding the context information of long code sequences typical in HPC applications. HAppA implements a novel code representation module that chunks the code into fixed-size segments and aggregates the embeddings of these segments. Three aggregation methods have been explored: MeanPooling, MaxPooling, and LSTM-based techniques. We built a DAtaset for REsilience analysis using Fault Injection (FI), named DARE. Using our DARE dataset, HAppA is trained for regression prediction tasks. Our evaluation results demonstrate the predictive accuracy of HAppA compared to other models, particularly noting that the LSTM-based aggregation method -- HAppA-LSTM -- achieves a mean squared error (MSE) of 0.078 for SDC prediction, surpassing the existing state-of-the-art PARIS model, which recorded an MSE of 0.1172. Additionally, HAppA with the KeyBERT model extracts a list of keywords representing the source code. A comprehensive importance analysis of these keywords further elucidates the code patterns contributing to the error rate. These findings highlight the effectiveness of HAppA in analyzing the resilience of HPC applications and establish a new benchmark for predictive accuracy in resilience.

Jiang, Hailong [Kent State University]↗
DOE OSTI2016

Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics

Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate marker-free registration approaches have not been developed. The majority of studies instead rely on the utilization of artificial tie points (e.g., reflective tooling balls) placed within a scene to aid in coordinate transformation. We present a technique for generating view-invariant feature descriptors that are intrinsic to the point cloud data and, thus, enable blind marker-free registration in forest environments. To overcome the limitation of initial pose estimation, we employ a voting method to blindly determine the optimal pairwise transformation parameters, without an a priori estimate of the initial sensor pose. To provide embedded error metrics, we developed a set theory framework in which a circular transformation is traversed between disjoint tie point subsets. This provides an upper estimate of the Root Mean Square Error (RMSE) confidence associated with each pairwise transformation. Output RMSE errors are commensurate with the RMSE of input tie points locations. Thus, while the mean output RMSE=16.3cm, improved results could be achieved with a more precise laser scanning system. This study 1) quantifies the RMSE of the proposed marker-free registration approach, 2) assesses the validity of embedded confidence metrics using receiver operator characteristic (ROC) curves, and 3) informs optimal sample spacing considerations for TLS data collection in New England forests. Furthermore, while the implications for rapid, accurate, and precise forest inventory are obvious, the conceptual framework outlined here could potentially be extended to built environments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Multiview marker-free registration of forest terrestrial laser scanner data with embedded confidence metrics

Terrestrial laser scanning has demonstrated increasing potential for rapid comprehensive measurement of forest structure, especially when multiple scans are spatially registered in order to reduce the limitations of occlusion. Although marker-based registration techniques (based on retro-reflective spherical targets) are commonly used in practice, a blind marker-free approach is preferable, insofar as it supports rapid operational data acquisition. To support these efforts, we extend the pairwise registration approach of our earlier work, and develop a graph-theoretical framework to perform blind marker-free global registration of multiple point cloud data sets. Pairwise pose estimates are weighted based on their estimated error, in order to overcome pose conflict while exploiting redundant information and improving precision. The proposed approach was tested for eight diverse New England forest sites, with 25 scans collected at each site. Quantitative assessment was provided via a novel embedded confidence metric, with a mean estimated root-mean-square error of 7.2 cm and 89% of scans connected to the reference node. Lastly, this paper assesses the validity of the embedded multiview registration confidence metric and evaluates the performance of the proposed registration algorithm.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

MO-FG-303-01: FEATURED PRESENTATION and BEST IN PHYSICS (THERAPY): Automating LINAC QA: Design and Testing of An Image Acquisition and Processing System Utilizing a Combination of Radioluminescent Phosphors, Embedded X-Ray Markers and Optical Measurements

Purpose: The recent development of phosphors to visualize radiation beams from linear accelerators (LINAC) offers a unique opportunity for evaluating radiation fields within the context of the treatment space. The purpose of this study was to establish an automated, self-calibrating prototype system for performing quality assurance (QA) measurements. Methods: A thin layer of Gd{sub 2}O{sub 2}S:Tb phosphor and fiducial markers were embedded on several planar faces of a custom-designed phantom. The phantom was arbitrarily placed near iso-center on the couch of a LINAC equipped with on-board megavoltage (MV) and kilovoltage (kV) imagers. A plan consisting of several beams and integrated image acquisitions was delivered. Images of the phantom were collected throughout the delivery. Salient features, such as fiducials, crosshairs and beam edges were then extracted from these images used to calibrate the system, adjust for variations in phantom placement, and perform measurements. Beam edges were visualized by imaging the light generated by the phosphor on the phantom enabling direct comparison with the light field and laser locations. Registration of MV, kV and optical image data was performed using the embedded fiducial markers, enabling comparison of imaging center locations. Measurements specified by TG-142 were calculated and compared with those obtained from a commercially available QA system. Results: The system was able to automatically extract the location of the fiducials, lasers, light field and radiation field from the acquired images regardless of phantom positioning. It was also able to automatically identify the locations of fiducial markers on kV and MV images. All collected measurements were within TG-142 guidelines. The difference between the prototype and commercially available system were less than 0.2 mm. Conclusion: The prototype system demonstrated the capability of accurately and autonomously evaluating various TG-142 parameters independent of operator input and phantom setup.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Fe and Co nanostructures embedded into the Cu(100) surface: Self-Organization and magnetic properties

The self-organization and magnetic properties of small iron and cobalt nanostructures embedded into the first layer of a Cu(100) surface are investigated using the self-learning kinetic Monte Carlo method and density functional theory. The similarities and differences between the Fe/Cu(100) and the Co/Cu(100) are underlined. The time evolution of magnetic properties of a copper monolayer with embedded magnetic atoms at 380 K is discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Efficient three-dimensional Laplace-Fourier domain acoustic-wave simulations using discontinuous finite-difference meshes with embedded boundaries

© 2018 SEG. We develop embedded boundary and discontinuous mesh methods to handle arbirarily shaped topography and accurately simulate acoustic seismic wave propagation in Laplace-Fourier domain. The purpose of the embedded boundary method is to enhance accurate wave simulation near the surface and the discontinuous mesh method is used to achieve considerable savings in both computation time and memory savings relative to fixed mesh schemes.

AlSalem, Hussain↗
DOE OSTI2021

OPERATING EXPERIENCE AND ITS RELATIONSHIP TO EMBEDDED DIGITAL DEVICES IN SAFETY SYSTEMS

Interest in embedded digital devices (EDDs) for monitoring and providing control of components is increasing and the added functionality digital brings such as diagnostics, displays, control functions is welcomed in both the nuclear and other industries. As a result, pure analog-based components continue to disappear from the industrial I&C marketplace. Equipment with EDDs, also known by related terms such as smart devices, intelligent devices, and digital devices of limited functionality, can include sensors, breakers, priority logic modules, time delay relays, pumps, valve actuators, motor control centers, and uninterruptible power supplies. Not only is equipment consisting of analog and older digital technology being replaced with commercial grade products containing EDDs, but EDDs can also be retrofitted to work with existing components. EDDs may be installed physically within a component, such as in a relay. There are also external devices that some would not consider an EDD, but that operate in almost all technical ways identically to one internal to a component such as in a valve controller. In fact, the digital portions may be located hundreds of feet from the actual component.Due to the potential of being used in an identical manner and without alteration from their application in other industries, operating experience (OpE) for EDDs may be much more applicable than in the case of other more system level devices. Clear separation and independence of functionality provided with EDDs may also allow greater applicability of existing OpE from industry for changes that can be shown not to affect its safety function. However, OpE also demonstrates issues with different conditions between other industries and nuclear power plants, such as issues with undeclared changes or undeclared digital devices and the new failure modes they may introduce, issues with the reporting of OpE in industry, and a lack of transparency into OpE for EDDs.Understanding and addressing these issues will be key in the future use of EDDs in nuclear power plants, but failure rate data is difficult to obtain for EDDs, and lessons learned from the use of EDDs are difficult to collect and identify.Key words: embedded digital device, smart device, operating experience, failure modes

Muhlheim, Michael↗
DOE OSTI2023

Substrate-embedded metal meshes for ITO-free organic light emitting diodes

Organic light-emitting diodes (OLEDs) have great potential for use in large-area display and lighting applications, but their widespread adoption for large areas is hindered by the high cost and insufficient performance of indium tin oxide (ITO) anodes. In this study, we introduce an alternative anode material – a silver mesh embedded in glass – to facilitate production of large-area OLEDs. We present a facile, scalable manufacturing technique to create high aspect ratio micromeshes embedded in glass to provide the planar geometry needed for OLED layers. Our phosphorescent green OLEDs achieve a current efficiency of 51.4 cd/A at 1000 cd/m 2 and reach a slightly higher external quantum efficiency compared to a standard ITO/glass reference sample. Notably, these advancements are achieved without any impact on the viewing angle of the OLEDs. These findings represent a promising advancement towards ITO-free, high-efficiency OLEDs for various high performance, large-area applications, such as lighting and displays.

Optics↗
DOE OSTI2025

Improving high temperature resilience of fiber sensor embedded smart components through laser shock peening

This study explores the use of laser shock peening (LSP) to enhance material properties and high-temperature performance of fiber-sensor-fused smart parts fabricated by additive manufacturing (AM) methods. Using embedded fiber sensors as distributed strain gauges, the study demonstrates that LSP can induce compressive strains of up to 130 µε on fiber embedded 1-mm below metal surfaces. The electron backscatter diffraction (EBSD) analysis shows that, with optimized LSP parameters, the metallic matrix undergoes substantial microstructural refinement, resulting in denser structures. Thermal cycling tests showed that the LSP process can increase fiber slippage temperatures by more than 50 °C. This work shows that the LSP process is an effective room-temperature process for enhancing both surface quality and increasing fiber slippage threshold under both thermal and mechanical stress.

Zhong, Shuda [University of Pittsburgh, PA (United↗
DOE OSTI2012

Embedded Sensors and Controls to Improve Component Performance and Reliability Conceptual Design Report

The objective of this project is to demonstrate improved reliability and increased performance made possible by deeply embedding instrumentation and controls (I&C) in nuclear power plant (NPP) components and systems. The project is employing a highly instrumented canned rotor, magnetic bearing, fluoride salt pump as its I&C technology demonstration platform. I&C is intimately part of the basic millisecond-by-millisecond functioning of the system; treating I&C as an integral part of the system design is innovative and will allow significant improvement in capabilities and performance. As systems become more complex and greater performance is required, traditional I&C design techniques become inadequate and more advanced I&C needs to be applied. New I&C techniques enable optimal and reliable performance and tolerance of noise and uncertainties in the system rather than merely monitoring quasistable performance. Traditionally, I&C has been incorporated in NPP components after the design is nearly complete; adequate performance was obtained through over-design. By incorporating I&C at the beginning of the design phase, the control system can provide superior performance and reliability and enable designs that are otherwise impossible. This report describes the progress and status of the project and provides a conceptual design overview for the platform to demonstrate the performance and reliability improvements enabled by advanced embedded I&C.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Dynamic temperature measurements with embedded optical sensors.

This report summarizes LDRD project number 151365, \Dynamic Temperature Measurements with Embedded Optical Sensors". The purpose of this project was to develop an optical sensor capable of detecting modest temperature states (<1000 K) with nanosecond time resolution, a recurring diagnostic need in dynamic compression experiments at the Sandia Z machine. Gold sensors were selected because the visible re ectance spectrum of gold varies strongly with temperature. A variety of static and dynamic measurements were performed to assess re ectance changes at di erent temperatures and pressures. Using a minimal optical model for gold, a plausible connection between static calibrations and dynamic measurements was found. With re nements to the model and diagnostic upgrades, embedded gold sensors seem capable of detecting minor (<50 K) temperature changes under dynamic compression.

Seagle, Christopher T↗
DOE OSTI2014

Embedded Fiber Optic Sensors for Measuring Transient Detonation/Shock Behavior;Time-of-Arrival Detection and Waveform Determination.

The miniaturization of explosive components has driven the need for a corresponding miniaturization of the current diagnostic techniques available to measure the explosive phenomena. Laser interferometry and the use of spectrally coated optical windows have proven to be an essential interrogation technique to acquire particle velocity time history data in one- dimensional gas gun and relatively large-scale explosive experiments. A new diagnostic technique described herein allows for experimental measurement of apparent particle velocity time histories in microscale explosive configurations and can be applied to shocks/non-shocks in inert materials. The diagnostic, Embedded Fiber Optic Sensors (EFOS), has been tested in challenging microscopic experimental configurations that give confidence in the technique's ability to measure the apparent particle velocity time histories of an explosive with pressure outputs in the tenths of kilobars to several kilobars. Embedded Fiber Optic Sensors also allow for several measurements to be acquired in a single experiment because they are microscopic, thus reducing the number of experiments necessary. The future of EFOS technology will focus on further miniaturization, material selection appropriate for the operating pressure regime, and extensive hydrocode and optical analysis to transform apparent particle velocity time histories into true particle velocity time histories as well as the more meaningful pressure time histories.

Chavez, Marcus Alexander↗