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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modeling gas migration through clay-based buffer material using coupled multiphase fluid flow and geomechanics with stress-dependent gas permeability

A model for gas migration through clay-based buffer material is developed for modeling gas generation and migration associated with deep geologic nuclear waste disposal. The model is based on a multiphase fluid flow and geomechanics simulator that is adapted to consider enhanced gas flow when gas pressure is high enough to approach the confining stress magnitude. A key feature in the model is a direct coupling between gas permeability and stress, through a non-linear stress-dependent permeability function. The model was first tested and calibrated by modelling two different laboratory gas migration tests on Wyoming (MX-80) bentonite samples. The calibrated model was then applied to model gas migration through a bentonite buffer of a large-scale gas injection test (Lasgit) conducted at the Äspö Hard Rock Laboratory in Sweden. Observed preferential gas migration along interfaces (between compacted blocks and along the canister surface) required explicit representation of such interfaces in the model. The model with the stress-dependent gas permeability accurately captured observed experimental responses in terms of gas breakthrough time, peak gas pressure, and cumulative gas flow rates. The calibrated model was finally applied to simulate migration of hydrogen gas generated within a breached nuclear waste canister over 10,000 years, involving migration of much larger gas volumes. For the considered gas generation rate and host rock properties, the generated gas could migrate through the bentonite buffer and released into the surrounding host rock at a maximum gas pressure somewhat higher than the initial total stress, though a significant amount of hydrogen remained within the buffer. This modelling sets the stage for further detailed analysis of the impact of hydrogen gas generation on the long-term performance of nuclear waste repositories.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

An improved oxygen reduction reaction activity and CO 2 -tolerance of La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ achieved by a surface modification with barium cobaltite coatings

Solid oxide fuel cells (SOFCs) cathode often suffers from the poisoning effect of the contaminants commonly encountered in air such as CO 2 . Here we report an effective approach to enhancing the activity and CO 2 tolerance of the state-of-the-art La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ (LSCF) cathode enabled by a coating of BaCoO 3-δ (BCO), as verified by the electrochemical testings, Raman analyses, and density functional theory calculations. When surface modified with a thin-film BCO coating, LSCF displays a much enhanced ORR activity and an improved durability against CO 2 . For example, anode supported SOFCs with the LSCF cathode coated with BCO coatings show a remarkable peak power density (Pmax) of 0.41 Wcm -2 and a significantly reduced degradation rate in current density of ~0.08% h -1 at 0.8 V and 700 °C for a period of 300 hs when humidified H2 (with 3 vol%H2O) was used as fuel and air with 8 vol% CO 2 as oxidant. The demonstrated performance is improved when compared with those of the cells with a blank LSCF electrode (a Pmax of ~0.36 Wcm -2 and a degradation rate of ~0.15% h -1 ) under the same conditions. Furthermore, the adsorption energy calculations suggests that BCO coating makes CO 2 adsorption much weaker than LSCF (-0.54 eV versus -1.07 eV).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Integration of the FastIC front-end electronics into the Picosec MicroMegas detector

The Picosec MicroMegas collaboration aims to develop gaseous fast-timing detectors; experimentally, intrinsic time resolutions from around 50 ps to better than 20 ps are obtained, depending on the exact detector configuration. Parts of developments focus on exploring various options of fast-timing multi-channel front-end electronics, to meet the data processing demands of experimental applications. One option is the FastIC, which was developed for reading out positive and negative input polarity sensors with intrinsic amplification. In this paper, the first results from reading out the gaseous Picosec MicroMegas detector with the FastIC are presented. In laboratory studies, a basic description of the data processing chain was performed using a function generator. The results from test beam measurements are used to characterise the timing performance and the charge processing of the combination of FastIC and Picosec MicroMegas, as well as to demonstrate the possibility of a multi-channel detector readout. Although the timing-at-threshold level of the FastIC introduces a time walk of around 1.5 ns, time resolutions of around 50 ps have been achieved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

De Novo Design of Proteins That Bind Naphthalenediimides, Powerful Photooxidants with Tunable Photophysical Properties

De novo protein design provides a framework to test our understanding of protein function and build proteins with cofactors and functions not found in nature. Here, we report the design of proteins designed to bind powerful photooxidants and the evaluation of the use of these proteins to generate diffusible small-molecule reactive species. Because excited-state dynamics are influenced by the dynamics and hydration of a photooxidant’s environment, it was important to not only design a binding site but also to evaluate its dynamic properties. Thus, we used computational design in conjunction with molecular dynamics (MD) simulations to design a protein, designated NBP (NDI Binding Protein), that held a naphthalenediimide (NDI), a powerful photooxidant, in a programmable molecular environment. Solution NMR confirmed the structure of the complex. We evaluated two NDI cofactors in this de novo protein using ultrafast pump–probe spectroscopy to evaluate light-triggered intra- and intermolecular electron transfer function. Moreover, we demonstrated the utility of this platform to activate multiple molecular probes for protein labeling.

carbonyls↗
DOE OSTI2020

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computation-guided optimization of split protein systems

Splitting bioactive proteins into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biological systems. However, split proteins often exhibit a high propensity to reconstitute, even without the conditional trigger, limiting their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific or often ineffective. Here, we report a computational design strategy grounded in fundamental protein biophysics to guide experimental evaluation of a sparse set of mutants to identify an optimal functional window. We hypothesized that testing a limited set of mutants would direct subsequent mutagenesis efforts by predicting desirable mutant combinations from a vast mutational landscape. This strategy varies the degree of interfacial destabilization while preserving stability and catalytic activity. We validate our method by solving two distinct split protein design challenges, generating both design and mechanistic insights. Furthermore, this new technology will streamline the generation and use of split protein systems for diverse applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Substructure in the stellar halo near the Sun: II. Characterisation of independent structures

In an accompanying paper, we present a data-driven method for clustering in ‘integrals of motion’ space and apply it to a large sample of nearby halo stars with 6D phase-space information. The algorithm identified a large number of clusters, many of which could tentatively be merged into larger groups. The goal here is to establish the reality of the clusters and groups through a combined study of their stellar populations (average age, metallicity, and chemical and dynamical properties) to gain more insights into the accretion history of the Milky Way. To this end, we developed a procedure that quantifies the similarity of clusters based on the Kolmogorov–Smirnov test using their metallicity distribution functions, and an isochrone fitting method to determine their average age, which is also used to compare the distribution of stars in the colour–absolute magnitude diagram. Also taking into consideration how the clusters are distributed in integrals of motion space allows us to group clusters into substructures and to compare substructures with one another. We find that the 67 clusters identified by our algorithm can be merged into 12 extended substructures and 8 small clusters that remain as such. The large substructures include the previously known Gaia-Enceladus, Helmi streams, Sequoia, and Thamnos 1 and 2. We identify a few over-densities that can be associated with the hot thick disc and host a small metal-poor population. Especially notable is the largest (by number of member stars) substructure in our sample which, although peaking at the metallicity characteristic of the thick disc, has a very well populated metal-poor component, and dynamics intermediate between the hot thick disc and the halo. We also identify additional debris in the region occupied by Sequoia with clearly distinct kinematics, likely remnants of three different accretion events with progenitors of similar masses. Although only a small subset of the stars in our sample have chemical abundance information, we are able to identify different trends of [Mg/Fe] versus [Fe/H] for the various substructures, confirming our dissection of the nearby halo. We find that at least 20% of the halo near the Sun is associated to substructures. When comparing their global properties, we note that those substructures on retrograde orbits are not only more metal-poor on average but are also older. We provide a table summarising the properties of the substructures, as well as a membership list that can be used for follow-up chemical abundance studies for example.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Introducing new resonant soft x-ray scattering capability in SSRL

Resonant soft x-ray scattering (RSXS) is a powerful technique for probing both spatial and electronic structures within solid-state systems. Here, we present a newly developed RSXS capability at beamline 13-3 of the Stanford Synchrotron Radiation Lightsource, designed to enhance materials science research. This advanced setup achieves a base sample temperature as low as 9.8 K combined with extensive angular motions (azimuthal ϕ and flipping χ), enabling comprehensive exploration of reciprocal space. Two types of detectors—an Au/GaAsP Schottky photodiode and a charge-coupled device detector with over 95% quantum efficiency—are integrated to effectively capture scattered photons. Extensive testing has confirmed the enhanced functionality of this RSXS setup, including its temperature and angular performance. The versatility and effectiveness of the system have been demonstrated through studies of various materials, including superlattice heterostructures and high-temperature superconductors.

Kuo, Cheng-Tai [SLAC National Accelerator Laborato↗
DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Shifting Layer Heights for Closed-Loop Contours in Additive Manufacturing

Traditional slicing for 3D printing involves a planar cross-sectioning process to create layers. These layers are stacked one on top of the next representing a butt-joint, which is typically the weakest direction for loading a 3D printed part. Because the layers are printed sequentially, no material or reinforcement connects from one layer to the next. This clean layer line between layers is easy to break or separate. To fix this, the authors have implemented a new approach to shift the height of every other closed-loop contour toolpath such that a clean layer line no longer exists. This bead shifting approach involves moving every other contour by half a layer height, after the cross-sectioning step, so that a straight line cannot be drawn between layers. To maintain the original object geometry, a half height layer is printed for the first and last layer to create a flat top and bottom surface. This functionality has been implemented and tested as part of ORNL Slicer 2, an open-source toolpathing software package.

Roschli, Alex↗
DOE OSTI2025

PNNL-Predictive-Phenomics/ProteoMeter

ProteoMeter is a Python package that assists in the statistical analysis of global proteomics, protein post-translation modification (PTM), and limited proteolysis (LiP) data. It contains batch correction, normalization, and statistical testing methods, as well as functions that "roll up" peptide-level data to the single-site level. It has a robust user configuration system, allowing it to flexibly integrate different types of experiment designs. For basic usage, a simple configuration file provides the essential functionality. Advanced users have access to the entire statistical pipeline for fine-tuning analyses. Processed data is easily exported to many common spreadsheet and data-frame formats.

Rozum, Jordan [Pacific Northwest National Lab]↗
DOE OSTI2022

The Virtual Test Bed (VTB) repository: a library of multiphysics reference reactor models using NEAMS tools

With the next generation of nuclear reactors under development, modeling and simulation (MS) tools are being developed by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in order to support their design, licensing, and future operation. Mirroring the physical test beds currently under construction (i.e., EBR-II and ZPPR), the Virtual Test Bed (VTB) was launched by the National Reactor Innovation Center (NRIC) in collaboration with NEAMS to support the advanced reactor community. This collaborative effort, which involves multiple teams at both Idaho National Laboratory and Argonne National Laboratory aims to use NEAMS tools to model a wide range of reactor designs. Those models are automatically tested to ensure their continued functionality as the tools are further developed. Examples are extensively documented, each acting as a tutorial for applying the relevant NEAMS tools to that reactor design. Currently, five advanced reactor types (with a total of eight specific design variants) are simulated by a variety of different models. These models range from steady-state, core multiphysics simulations to integrated plant analysis during loss-of flow transients. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Beam study on low dispersion CEBAF arcs

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab employs recirculating linac SRF technology to generate a high polarization 12 GeV electron beam for nuclear physics users. New opportunities to study multipass energy recovery have also emerged with the proposal of a 5-pass energy recovery demonstration, ER@CEBAF. New beam optics with minimized beta functions have been developed and tested to avoid collective beam instabilities for multi-pass beams and meet the beam requirements of the nuclear physics community. To enable energy recovery for a common arc beam transport of five passes, achromatic and isochronous arc optics conditions were satisfied by re-designing the transverse optics of CEBAF. This paper focuses on beam studies conducted to study the newly-designed, low-dispersion, lowest energy arcs for CEBAF operations and ER@CEBAF.

Neththikumara, I.↗
DOE OSTI2020

RETRO RX

RETRO Rx is an R&D 100 Gold Award-winning suite of tools that provides a new class of analytics in the space between traditional epidemiological models and unanalyzed spreadsheets of data. Unlike traditional models, these tools provide analysts, scientists, practitioners, and decision makers a way to easily and effectively do sophisticated analyses using a simple user interface. AIDO guides users at the early stages of a disease outbreak by comparing the current parameters to historic outbreaks. RED Alert constructs a broad historical picture of disease incidence (recent outbreak included) to detect disease reemergence at its earliest stages, locally and globally. RETRO Rx compiles data and presents analysis in a clear, concise visual format to support disease mitigation and prevention planning. Both are available currently as web-based apps. A prototype of the RED Alert mobile app with enhanced functionality is available for beta testing and AIDO mobile app development is planned.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Software-defined network for end-to-end networked science at the exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end- to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Challenges and Potential of Waveform Modeling for Crustal Scale Predictions

Waveform modeling is crucial to improving our understanding of observed seismograms. Forward simulation of wavefields provides quantitative methods of testing interactions between complicated source functions and the propagation medium. Here, we discuss three experiments designed to improve under standing of high frequency seismic wave propagation. First, we compare observed and predicted travel times of crustal phases for a set of real observed earthquakes with calculations and synthetic seismograms. Second, we estimate the frequency content of a series of nearly co-located earthquakes of varying magnitude for which we have a relatively well- known 1D velocity model. Third, we apply stochastic perturbations on top of a 3D tomographic model and qualitatively assess how those variations map to differences in the seismograms. While different in scope and aim, these three vignettes illustrate the current state of crustal scale waveform modeling and the potential for future studies to better constrain the structure of the crust.

58 GEOSCIENCES↗
DOE OSTI2023

Spread Spectrum Time Domain Reflectometry (SSTDR) and Frequency Domain Reflectometry (FDR) for Detection of Cable Anomalies Using Machine Learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗