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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Novel Approach to Map Permeability Using Passive Seismic Emission Tomography

Newly acquired magnetotelluric data and passive seismic data collected with tightly spaced geophone arrays are combined with historic drilling, active seismic, and potential fields data to generate 3-D permeability maps. A cooperative inversion methodology has been developed using active seismic, magnetotelluric, and gravity data in order to produce more robust velocity models for passive seismic data processing without requiring expensive 3-D active seismic surveys. The cooperative inversion estimates velocities from other geophysical data where no prior seismic velocity information is available at two geothermal sites in Nevada: San Emidio and Crescent Valley.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Automatic Image Point Matching

Sparse Image Point Matching (SIPM) is a foundational technology for photo triangulation, structure from motion (SfM), Simultaneous Location and Mapping (SLAM), and data fusion. The goal of the matching is to automatically generate sets of image coordinates that identify the same feature across images. Ideally, the process should be robust to lighting, scale, perspective, and modality changes. The scope of the image matching topic in the field of remote sensing (RS) is enormous because of the variety of collection platforms, modalities, sensor types, applications, and subjects. In this work, we report the history of and assess the state of the art of visible-spectrum (panchromatic and color) image matching of the Earth’s surface. Work specific to large-format images (LFI) (e.g., metric aerial cameras and Earth-observing satellites) will be highlighted. However, the state of the art in this century will mostly be traced through machine vision research and benchmarks because research specific to LFI is rare.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Current status of research and development program for characterizing fuel debris at Fukushima Daiichi NPS by JAEA

Japan Government and TEPCO submitted a research road map for decommissioning Fukushima Daiichi Nuclear Power Plant. Two projects about debris are in progress: 'Assessment of simulated fuel debris characteristics' and 'Development of technologies for the processing of fuel debris'. The major results concerning the first project are the following 4 points. First, it was suggested that typical phase of oxide of fuel debris is (U,Zr)O{sub 2} and that of metal is Fe{sub 2}(Zr,U) by thermodynamic calculation. Secondly, important properties of fuel debris for developing defueling tools were identified as shape, size, density, hardness, elastic modulus, fracture toughness, thermal conductivity, specific heat (heat capacity), and melting point. Thirdly, the influence of seawater salt and B{sub 4}C/SUS to characteristics of debris was found, such as deposition of magnesium oxide crystal on the surface of fuel debris. The Influence of Pu to thermal properties of fuel debris was found, such as the increase of melting point. Concerning the second project, the major results are the following. First, a draft of the whole image of scenarios was developed. Secondly, the alkaline resolution method using Na{sub 2}O{sub 2} is most likely to be applied as a part of analysis technologies. Thirdly, it was shown that a part of fuel debris rich in U might be soluble in nitric acid. Fourthly, it was shown that all pyrochemical processes examined have potential to be applied for treating fuel debris. The results of the projects will contribute to the decommissioning program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

First in-core simultaneous measurements of nuclear heating and thermal neutron flux obtained with the innovative mobile calorimeter CALMOS inside the OSIRIS reactor

Nuclear heating inside a MTR reactor has to be known in order to design and to run irradiation experiments which have to fulfill target temperature constraints. This measurement is usually carried out by calorimetry. The innovative calorimetric system, CALMOS, has been studied and built in 2011 for the 70 MWth OSIRIS reactor operated by CEA. Thanks to a new type of calorimetric probe, associated to a specific displacement system, it provides measurements along the fissile height and above the core. This development required preliminary modelling and irradiation of mock-ups of the calorimetric probe in the ex-core area, where nuclear heating rate does not exceed 2 W.g{sup -1}. The calorimeter working modes, the different measurement procedures allowed with such a new probe, the main modeling and experimental results and expected advantages of this new technique have been already presented. However, these first in-core measurements were not performed beyond 6 W.g{sup -1}, due to an inside temperature limitation imposed by a safety authority requirement. In this paper, we present the first in-core simultaneous measurements of nuclear heating and conventional thermal neutron flux obtained by the CALMOS device at the 70 MW nominal reactor power. For the first time, this experimental system was operated in nominal in-core conditions, with nominal neutron flux up to 2.7 10{sup 14} n.cm{sup -2}.s{sup -1} and nuclear heating up to 12 W.g{sup -1}. A comprehensive measurement campaign carried out from 2013 to 2015 inside all accessible irradiation locations of the core, allowed to qualify definitively this new device, not only in terms of measurement ability but also in terms of reliability. After a brief reminder of the calorimetric cell configuration and displacement system specificities, first nuclear heating distributions at nominal power are presented and discussed. In order to reinforce the heating evaluation, a systematic comparison is made between results obtained by different methods, the probe calibration coefficient and the zero method. Thermal neutron flux evaluation from the SPND signal processing required a specific TRIPOLI-4 Monte Carlo calculation which has been performed with the precise CALMOS cell geometry. In addition, the Finite Element model for temperatures map prediction inside the calorimetric cell has been upgraded with the recent experimental data obtained up to 12 W.g{sup -1}. The Kc coefficient, taking into account nonlinearities with regard to the calibration, has been reevaluated so as to make relevant measurements up to the nominal reactor power. Finally, the experience feedback acquired until now with this first CALMOS version led us to improvement perspectives. A second device is currently under manufacturing and main technical options chosen for this second version are presented. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Digitizing Today’s Buildings in the Real World: Lessons from Field Demonstrations

Digital twins, created by generating a virtual replica of a building, enable safe evaluation of operational scenarios and applications like fault detection and diagnosis and advanced controls. However, a prerequisite is the creation of a machine-readable digital representation of a building, currently hindered by fragmented information scattered across mechanical drawings, point lists, and natural language sequences. As a result, digital twin development remains labor-intensive, error-prone, and difficult to validate. To address these challenges, two efforts from ASHRAE aim to support the digitalization of buildings. ASHRAE s223 establishes a semantic model of buildings, representing system components, configuration, and data sources. ASHRAE s231 defines a vendor-neutral programming language for expressing their control logic. As the industry evaluates implementing them in their products, understanding the challenges that vendors and implementers may face is crucial. In this paper, we present findings and lessons learned from field demonstrations in five buildings that implemented control applications using ASHRAE s223 and s231. The demonstrations highlight how semantic modeling and formalized control descriptions can significantly reduce software development time, manual point mapping, and hard-coding. Beyond time efficiency, they enable reliable automation by minimizing human interpretation and providing a means for consistency across projects. We describe the processes and best practices for model creation and model usage, from translating heterogeneous building documentation into semantic representations to implementing control logic in real-world systems. Finally, we discuss the challenges that persist, including integration with legacy software environments, gaps in interoperability, and the level of expertise still required to effectively leverage semantic models.

Prakash, Anand Krishnan↗
DOE OSTI2018

Modular Spiking Neural Circuits for Mapping Long Short-Term Memory on a Neurosynaptic Processor

Due to the distributed and asynchronous nature of neural computation through low-energy spikes, brain-inspired hardware systems offer high energy efficiency and massive parallelism. One such platform is the IBM TrueNorth neurosynaptic system. Recently, TrueNorth compatible representation learning algorithms have emerged, achieving close to the state-of-the-art performance in various data sets. However, its application in temporal sequence processing models, such as recurrent neural networks (RNNs), is still only at the proof of concept level. There is an inherent difficulty in capturing temporal dynamics of an RNN using spiking neurons, which is only exasperated by the hardware constraints in connectivity and synaptic weight resolution. This paper presents a design flow that overcomes these difficulties and maps a special case of recurrent networks called long short-term memory (LSTM) onto a spike-based platform. The framework utilizes various approximation techniques, such as activation discretization, weight quantization, and scaling and rounding, spiking neural circuits that implement the complex gating mechanisms, and a store-and-release technique to enable neuron synchronization and faithful storage. While the presented techniques can be applied to map LSTM to any spiking neural network (SNN) simulator/emulator, here we choose the TrueNorth chip as the target platform by adhering to its hardware constraints. Three LSTM applications, parity check, extended Reber grammar, and question classification, are evaluated. The tradeoffs among accuracy, performance, and energy tradeoffs achieved on TrueNorth are demonstrated. Finally, this is compared with the performance on an SNN platform without hardware constraints, which represents the upper bound of the achievable accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations and splice variants identified in cancer cells are translated. Herein we therefore describe a proteogenomic data integration tool (QUILTS) and illustrate its application to whole genome, transcriptome and global MS peptide sequence datasets generated from a pair of luminal and basal-like breast cancer patient derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS process replicates. Despite over thirty sample replicates, only about 10% of all SNV (somatic and germline) were detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNV without a detectable mRNA transcript were also observed demonstrating the transcriptome coverage was also incomplete (~80%). In contrast to germ-line variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than the luminal tumor raising the possibility of differential translation or protein degradation effects. In conclusion, the QUILTS program integrates DNA, RNA and peptide sequencing to assess the degree to which somatic mutations are translated and therefore biologically active. By identifying gaps in sequence coverage QUILTS benchmarks current technology and assesses progress towards whole cancer proteome and transcriptome analysis.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2011

Reversible Parallel Discrete Event Formulation of a TLM-based Radio Signal Propagation Model

Radio signal strength estimation is essential in many applications, including the design of military radio communications and industrial wireless installations. For scenarios with large or richly- featured geographical volumes, parallel processing is required to meet the memory and computa- tion time demands. Here, we present a scalable and efficient parallel execution of the sequential model for radio signal propagation recently developed by Nutaro et al. Starting with that model, we (a) provide a vector-based reformulation that has significantly lower computational overhead for event handling, (b) develop a parallel decomposition approach that is amenable to reversibility with minimal computational overheads, (c) present a framework for transparently mapping the conservative time-stepped model into an optimistic parallel discrete event execution, (d) present a new reversible method, along with its analysis and implementation, for inverting the vector-based event model to be executed in an optimistic parallel style of execution, and (e) present performance results from implementation on Cray XT platforms. We demonstrate scalability, with the largest runs tested on up to 127,500 cores of a Cray XT5, enabling simulation of larger scenarios and with faster execution than reported before on the radio propagation model. This also represents the first successful demonstration of the ability to efficiently map a conservative time-stepped model to an optimistic discrete-event execution.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Probing Conformational Dynamics of Tau Protein by Hydrogen/Deuterium Exchange Mass Spectrometry

Fibrillization of the microtubule-associated protein tau has been recognized as one of the signature pathologies of the nervous system in Alzheimer’s disease, progressive supranuclear palsy, and other tauopathies. The conformational transition of tau in the fibrillization process, tau monomer to soluble aggregates to fibrils in particular, remains unclear. Here we report on the use of hydrogen/deuterium exchange mass spectrometry (HDX-MS) in combination with other biochemical approaches, including Thioflavin S fluorescence measurements, enzyme-linked immunosorbent assay (ELISA), and Western blotting to understand the heparin-induced tau’s fibrillization. HDX-MS studies including anti-tau antibody epitope mapping experiments provided molecular level details of the full-length tau’s conformational dynamics and its regional solvent accessibility upon soluble aggregates formation. The results demonstrate that R3 region in the full-length tau’s microtubule binding repeat region (MTBR) is stabilized in the aggregation process, leaving both N and C terminal regions to be solvent exposed in the soluble aggregates and fibrils. The findings also illustrate the practical utility of orthogonal analytical methodologies for the characterization of protein higher order structure. .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Design optimization of MAPS-based detectors using a data-driven fast simulation approach

A parametric simulation tool for pixel sensors is presented. A realistic pixel response is simulated purely based on measurement input, without requiring detailed knowledge of the underlying manufacturing process. As such, it provides an efficient alternative to the use of Technology Computer-Aided Design simulations, which typically depend on proprietary process information. Due to its parametric approach, the package is fast and thus particularly useful for larger detector systems and high hit rate environments. This work presents measurements, simulation and its validation for the MALTA2 sensor. It is a small collection electrode monolithic active pixel sensor produced in the Tower 180 nm complementary metal-oxide-semiconductor imaging process. Modifications to the sensor’s periphery, mainly in the hit merger, are studied in order to optimize the performance for tracking and calorimetry. This optimization is of special interest as part of the MALTA3 sensor redesign in the 65 nm Tower Partners Semiconductor Co. process.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Excitation energy transfer efficiency: Equivalence of transient and stationary setting and the absence of non-Markovian effects

We analyze efficiency of excitation energy transfer in photosynthetic complexes in transient and stationary setting. In the transient setting, the absorption process is modeled as an individual event resulting in a subsequent relaxation dynamics. In the stationary setting the absorption is a continuous stationary process, leading to the nonequilibrium steady state. We show that, as far as the efficiency is concerned, both settings can be considered to be the same, as they result in almost identical efficiency. We also show that non-Markovianity has no effect on the resulting efficiency, i.e., corresponding Markovian dynamics results in identical efficiency. Even more, if one maps dynamics to appropriate classical rate equations, the same efficiency as in quantum case is obtained.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Influence of rolling reduction and annealing on recrystallization and grain structure in Ta-2.5W alloys

The microstructural evolution of wrought Tantalum - 2.5 wt% Tungsten (Ta-2.5W) alloys during thermomechanical processing is critical for optimizing their mechanical reliability in demanding applications such as aerospace, chemical processing, and nuclear technology. Despite the widespread use of Ta-W alloys, a comprehensive understanding of how rolling reduction, annealing temperature, and elemental inhomogeneity interact to determine recrystallization behavior and grain refinement remains incomplete. Here, in this study, we systematically investigate the effects of cold rolling and subsequent annealing on the microstructure of Ta-2.5W, with particular attention to grain orientation, stored energy, and elemental banding. Our results demonstrate that higher rolling reduction rates lower the onset and completion temperatures for recrystallization, resulting in finer and more homogeneous grain structures. Electron backscatter diffraction (EBSD) analysis reveals that grains with a 〈111〉 parallel to the plate normal possess higher stored energy and nucleate recrystallization more readily than grains with a 〈001〉 parallel to the plate normal. Elemental mapping shows that tungsten inhomogeneity leads to localized bands of accelerated recrystallization and hardness variation. These findings provide new insights into the mechanisms of microstructure refinement in Ta-2.5W alloys, offering guidance for tailoring processing routes to achieve superior performance in demanding engineering environments.

Annealing↗
DOE OSTI2016

A Voxel-Based Approach to Explore Local Dose Differences Associated With Radiation-Induced Lung Damage

Purpose: To apply a voxel-based (VB) approach aimed at exploring local dose differences associated with late radiation-induced lung damage (RILD). Methods and Materials: An interinstitutional database of 98 patients who were Hodgkin lymphoma (HL) survivors treated with postchemotherapy supradiaphragmatic radiation therapy was analyzed in the study. Eighteen patients experienced late RILD, classified according to the Radiation Therapy Oncology Group scoring system. Each patient's computed tomographic (CT) scan was normalized to a single reference case anatomy (common coordinate system, CCS) through a log-diffeomorphic approach. The obtained deformation fields were used to map the dose of each patient into the CCS. The coregistration robustness and the dose mapping accuracy were evaluated by geometric and dose scores. Two different statistical mapping schemes for nonparametric multiple permutation inference on dose maps were applied, and the corresponding P<.05 significance lung subregions were generated. A receiver operating characteristic (ROC)-based test was performed on the mean dose extracted from each subregion. Results: The coregistration process resulted in a geometrically robust and accurate dose warping. A significantly higher dose was consistently delivered to RILD patients in voxel clusters near the peripheral medial-basal portion of the lungs. The area under the ROC curves (AUC) from the mean dose of the voxel clusters was higher than the corresponding AUC derived from the total lung mean dose. Conclusions: We implemented a framework including a robust registration process and a VB approach accounting for the multiple comparison problem in dose-response modeling, and applied it to a cohort of HL survivors to explore a local dose–RILD relationship in the lungs. Patients with RILD received a significantly greater dose in parenchymal regions where low doses (∼6 Gy) were delivered. Interestingly, the relation between differences in the high-dose range and RILD seems to lack a clear spatial signature.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Identifying Potential Areas for Siting Interim Nuclear Waste Facilities Using Map Algebra and Optimization Approaches

The renewed interest in siting new nuclear power plants in the United States has brought to the center stage, the need to site interim facilities for long-term management of spent nuclear fuel (SNF). In this paper, a two-stage approach for identifying potential areas for siting interim SNF facilities is presented. In the first stage, the land area is discretized into grids of uniform size (e.g., 100m x 100m grids). For the continental United States, this process resulted in a data matrix of about 700 million cells. Each cell of the matrix is then characterized as a binary decision variable to indicate whether an exclusion criterion is satisfied or not. A binary data matrix is created for each of the 25 siting criteria considered in this study. Using map algebra approach, cells that satisfy all criteria are clustered and regarded as potential siting areas. In the second stage, an optimization problem is formulated as a p-median problem on a rail network such that the sum of the shortest distance between nuclear power plants with SNF and the potential storage sites from the first stage is minimized. The implications of obtained results for energy policies are presented and discussed.

Facility location↗
DOE OSTI2023

$\text{GPLaSDI}$: Gaussian Process-based interpretable Latent Space Dynamics Identification through deep autoencoder

Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. Here we demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Mapping Strain-rate Dependent Dislocation-Defect Interactions by Atomistic Simulations

Probing the mechanisms of defect-defect interactions at strain rates lower than 106 s-1 is an unresolved challenge to date to molecular dynamics (MD) techniques. Here we propose a novel atomistic approach based on transition state theory and the concept of a strain-dependent effective activation barrier that is capable of simulating the kinetics of dislocation-defect interactions at virtually any strain rate, exemplified within 10-7 to 107 s-1. We apply this approach to the problem of an edge dislocation colliding with a cluster of self-interstitial atoms (SIA) under shear deformation. Using an activation-relaxation algorithm (1), we uncover a unique strain-rate dependent trigger mechanism that allows the SIA cluster to be absorbed during the process leading to dislocation climb. Guided by this finding, we determine the activation barrier of the trigger mechanism as a function of shear strain, and use that in a coarse-graining rate equation formulation for constructing a mechanism map in the phase space of strain-rate and temperature. Our predictions of a crossover from a defect recovery at the low strain rate regime to defect absorption behavior in the high strain-rate regime are validated against our own independent, direct MD simulations at 105 to 107 s-1. Implications of the present approach for probing molecular-level mechanisms in strain-rate regimes previously considered inaccessible to atomistic simulations are discussed.

activation mecanism↗
DOE OSTI2024

Permafrost Carbon: Progress on Understanding Stocks and Fluxes Across Northern Terrestrial Ecosystems

Significant progress in permafrost carbon science made over the past decades include the identification of vast permafrost carbon stocks, the development of new pan-Arctic permafrost maps, an increase in terrestrial measurement sites for CO 2 and methane fluxes, and important factors affecting carbon cycling, including vegetation changes, periods of soil freezing and thawing, wildfire, and other disturbance events. Process-based modeling studies now include key elements of permafrost carbon cycling and advances in statistical modeling and inverse modeling enhance understanding of permafrost region C budgets. By combining existing data syntheses and model outputs, the permafrost region is likely a wetland methane source and small terrestrial ecosystem CO 2 sink with lower net CO 2 uptake toward higher latitudes, excluding wildfire emissions. For 2002–2014, the strongest CO2 sink was located in western Canada (median: -52 g C m -2 y -1 ) and smallest sinks in Alaska, Canadian tundra, and Siberian tundra (medians: -5 to -9 g C m -2 y -1 ). Eurasian regions had the largest median wetland methane fluxes (16–18 g CH4 m -2 y -1 ). Quantifying the regional scale carbon balance remains challenging because of high spatial and temporal variability and relatively low density of observations. More accurate permafrost region carbon fluxes require: (a) the development of better maps characterizing wetlands and dynamics of vegetation and disturbances, including abrupt permafrost thaw; (b) the establishment of new year-round CO 2 and methane flux sites in underrepresented areas; and (c) improved models that better represent important permafrost carbon cycle dynamics, including non-growing season emissions and disturbance effects.

54 ENVIRONMENTAL SCIENCES↗