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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Dynamic Risk Framework for the Optimization of Physical Security Posture of Nuclear Power Plants

This paper describes an ongoing work within the Light Water Reactor Sustainability pathway at Idaho National Laboratory (INL) to optimize security and cost of nuclear power plants. It introduces the dynamic risk assessment tool developed at INL, Event Modeling Risk Assessment using Linked Diagrams (EMRALD). EMRALD was leveraged to optimize the security posture of a nuclear power plant by integrating force-on-force (FOF) simulations and operator mitigation actions including the dynamic and flexible coping strategies (FLEX). To illustrate the methodology, four attack scenarios were modeled in a commercially available FOF simulation tool using a hypothetical nuclear power plant facility. The simulation results provide valuable insights into possible attack outcomes, as well as the probabilistic risk of core damage event given these outcomes. Safety mitigation procedures were modeled in EMRALD dependent on the attack outcomes by considering human operator uncertainties. The results demonstrate that the number of armed responders can be optimized, while still maintaining the same protection level as the initial security posture. The proposed modeling and simulation framework of integrating FLEX equipment with FOF models enables the nuclear power plants to credit FLEX portable equipment in the plant security posture, resulting in an efficient and optimized physical security system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Dynamic Risk Framework for the Physical Security of Nuclear Power Plants

This paper describes ongoing work within the Light Water Reactor Sustainability pathway at Idaho National Laboratory (INL) to optimize the security and cost of nuclear power plants. It introduces the dynamic risk assessment tool developed at INL, Event Modeling Risk Assessment using Linked Diagrams (EMRALD). EMRALD is leveraged to optimize the security posture of a nuclear power plant by integrating force-on-force (FOF) simulations and operator mitigation actions, including dynamic and flexible coping strategies (FLEX). To illustrate the methodology, four attack scenarios are modeled in a commercially available FOF simulation tool using a hypothetical nuclear power plant facility. The simulation results provide valuable insights into possible attack outcomes, as well as the probabilistic risk of a core damage event given these outcomes. Safety mitigation procedures are modeled in EMRALD dependent on the attack outcomes by considering human operator uncertainties. The results demonstrate that the number of armed responders can be optimized, while still maintaining the same protection level as the initial security posture. The proposed modeling and simulation framework of integrating FLEX equipment with FOF models enables the nuclear power plants to credit FLEX portable equipment in the plant security posture, resulting in an efficient and optimized physical security system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

In-pixel automatic threshold calibration for the CMS Endcap Timing Layer readout chip

We present the implementation and verification of an in-pixel automatic threshold calibration circuit for the CMS Endcap Timing Layer (ETL) in the High-Luminosity LHC upgrade. The discriminator threshold of the ETL readout chip (ETROC) needs to be calibrated regularly to mitigate the circuit baseline change. Traditional methods need a lot of communication through a slow control system hence are time-consuming. This paper describes an in-pixel automatic scheme with improvements in operating time and usability. In this scheme, a sample-accumulation circuit is used to measure the average discriminator output. A binary successive approximation and linear combination scan are applied to find the equivalent baseline. The actual calibration procedure has been first implemented in FPGA firmware and tested with the ETROC front-end prototype chip (ETROC0). The calibration circuit has been implemented with Triple Modular Redundancy (TMR) and verified with Single Event Effects (SEEs) simulation. A complete calibration process lasts 35 ms with a 40 MHz clock. In the worst case, the dynamic and static power consumption are estimated to be 300 uW and 10.4 uW, respectively. The circuit design, implemented in a 65 CMOS technology, will be integrated into ETROC2, the next iteration of the ETROC with a 16 x16 pixel matrix.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Energy dependence of light hypernuclei production in heavy-ion collisions from a coalescence and statistical-thermal model perspective

A comparison of light hypernuclei production, from Ultra-relativistic Quantum Molecular Dynamics (UrQMD)+coalescence and the thermal model, in heavy ion collisions over a wide range of beam energies and system sizes is presented. We find that both approaches provide generally similar results, with differences in specific details. Especially the ratios of hypertriton to Λ are affected by both the source radius Δr of the coalescence procedure as well as canonical effects. On the other hand, the double ratio S 3 is almost independent of canonical effects, which is in contrast to coalescence. Thus, both the beam energy dependence and centrality dependence of S 3 can be used to constrain the hypertriton source radius. To do so the currently available data are not yet sufficient. Elliptic flow is shown to be unaffected by the source size of the nuclei and an almost perfect mass scaling of the elliptic flow is observed. Furthermore, our predictions further suggest that the existence of the H-dibaryon(ΛΛ) seems ruled out by ALICE data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Learning to Predict Arbitrary Quantum Processes

We present an efficient machine-learning (ML) algorithm for predicting any unknown quantum process ℰ over 𝑛 qubits. For a wide range of distributions 𝒟 on arbitrary 𝑛-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process ℰ, with a small average error over input states drawn from 𝒟. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to 10 6 and system size up to 50 qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.

quantum computation↗
DOE OSTI2025

Underground Hydrogen Storage: Transforming Subsurface Science into Sustainable Energy Solutions

As the global economy moves toward net-zero carbon emissions, large-scale energy storage becomes essential to tackle the seasonal nature of renewable sources. Underground hydrogen storage (UHS) offers a feasible solution by allowing surplus renewable energy to be transformed into hydrogen and stored in deep geological formations such as aquifers, salt caverns, or depleted reservoirs, making it available for use on demand. This study thoroughly evaluates UHS concepts, procedures, and challenges. This paper analyzes the most recent breakthroughs in UHS technology and identifies special conditions needed for its successful application, including site selection guidelines, technical and geological factors, and the significance of storage characteristics. The integrity of wells and caprock, which is important for safe and efficient storage, can be affected by the operating dynamics of the hydrogen cycle, notably the fluctuations in pressure and stress within storage formations. To evaluate its potential for broader adoption, we also examined economic elements such as cost-effectiveness and the technical practicality of large-scale storage. We also reviewed current UHS efforts and identified key knowledge gaps, primarily in the areas of hydrogen–rock interactions, geochemistry, gas migration control, microbial activities, and geomechanical stability. Resolving these technological challenges, regulatory frameworks, and environmental sustainability are essential to UHS’s long-term and extensive integration into the energy industry. This article provides a roadmap for UHS research and development, emphasizing the need for further research to fully realize the technology’s promise as a pillar of the hydrogen economy.

25 ENERGY STORAGE↗
DOE OSTI2021

Ocean Energy Systems Wave Energy Modeling Task 10.4: Numerical Modeling of a Fixed Oscillating Water Column

This paper reports on an ongoing international effort to establish guidelines for numerical modeling of wave energy converters, initiated by the International Energy Agency Technology Collaboration Program for Ocean Energy Systems. Initial results for point absorbers were presented in previous work, and here we present results for a breakwater-mounted Oscillating Water Column (OWC) device. The experimental model is at scale 1:4 relative to a full-scale installation in a water depth of 12.8 m. The power-extracting air turbine is modeled by an orifice plate of 1–2% of the internal chamber surface area. Measurements of chamber surface elevation, air flow through the orifice, and pressure difference across the orifice are compared with numerical calculations using both weakly-nonlinear potential flow theory and computational fluid dynamics. Both compressible- and incompressible-flow models are considered, and the effects of air compressibility are found to have a significant influence on the motion of the internal chamber surface. Recommendations are made for reducing uncertainties in future experimental campaigns, which are critical to enable firm conclusions to be drawn about the relative accuracy of the numerical models. It is well-known that boundary element method solutions of the linear potential flow problem (e.g., WAMIT) are singular at infinite frequency when panels are placed directly on the free surface. This is problematic for time-domain solutions where the value of the added mass matrix at infinite frequency is critical, especially for OWC chambers, which are modeled by zero-mass elements on the free surface. A straightforward rational procedure is described to replace ad-hoc solutions to this problem that have been proposed in the literature.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Substructure in the stellar halo near the Sun: I. Data-driven clustering in integrals-of-motion space

Context. Merger debris is expected to populate the stellar haloes of galaxies. In the case of the Milky Way, this debris should be apparent as clumps in a space defined by the orbital integrals of motion of the stars. Aims. Our aim is to develop a data-driven and statistics-based method for finding these clumps in integrals-of-motion space for nearby halo stars and to evaluate their significance robustly. Methods. We used data from Gaia EDR3, extended with radial velocities from ground-based spectroscopic surveys, to construct a sample of halo stars within 2.5 kpc from the Sun. We applied a hierarchical clustering method that makes exhaustive use of the single linkage algorithm in three-dimensional space defined by the commonly used integrals of motion energy E, together with two components of the angular momentum, L z and L ⊥ . To evaluate the statistical significance of the clusters, we compared the density within an ellipsoidal region centred on the cluster to that of random sets with similar global dynamical properties. By selecting the signal at the location of their maximum statistical significance in the hierarchical tree, we extracted a set of significant unique clusters. By describing these clusters with ellipsoids, we estimated the proximity of a star to the cluster centre using the Mahalanobis distance. Additionally, we applied the HDBSCAN clustering algorithm in velocity space to each cluster to extract subgroups representing debris with different orbital phases. Results. Our procedure identifies 67 highly significant clusters (> 3σ), containing 12% of the sources in our halo set, and 232 subgroups or individual streams in velocity space. In total, 13.8% of the stars in our data set can be confidently associated with a significant cluster based on their Mahalanobis distance. Inspection of the hierarchical tree describing our data set reveals a complex web of relations between the significant clusters, suggesting that they can be tentatively grouped into at least six main large structures, many of which can be associated with previously identified halo substructures, and a number of independent substructures. This preliminary conclusion is further explored in a companion paper, in which we also characterise the substructures in terms of their stellar populations. Conclusions. Our method allows us to systematically detect kinematic substructures in the Galactic stellar halo with a data-driven and interpretable algorithm. The list of the clusters and the associated star catalogue are provided in two tables available at the CDS.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Free-Surface Liquid Lithium Flow Modeling and Stability Analysis for Fusion Applications

Liquid metal plasma facing components are considered an attractive design choice for fusion devices including pilot plants. Virtual prototyping of such devices includes modeling of free-surface flow of the electrically conductive liquid, which requires computational fluid dynamics (CFD) and magnetohydrodynamics (MHD) simulations. Numerical tools capable of simulating flows and heat transfer in the free-surface MHD flow were developed at PPPL based on the customized ANSYS CFX. Here, MHD is introduced using a magnetic vector potential approach. Free-surface flow capabilities are available in the code and were tested. Special stabilization procedures were derived and applied to improve convergence of the momentum equations with the source terms due to the Lorentz force and surface tension. Important characteristics of the fusion-relevant liquid metal flow is free surface smoothness and stability. Heat flux from the plasma impacts the liquid surface at a very acute angle, so any change of the free surface from axisymmetry can dramatically increase the local heat flux density and thus create excessive evaporation of liquid lithium into the plasma, which is detrimental to operations. Stability analysis of the liquid metal film flow was performed to determine applicable flow regimes. Thin film flow along horizontal wall is considered. Effects of gravity, magnetic field, and surface tension are included in the analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Guide to CFD-Chemical Model for Spent Fuel Storage

This outlines the steps that were used to create the CFD model in Star-CCM+ to simulate the 50 year thermal history of ATR fuel stored in DOE sealed canisters. Then this goes through the steps to set up the processing the model results for the one-way coupled Cantera chemical model. The next part describes the process for setting up the 50-year Cantera model in Python. The goal of this is to document the procedure for quality assurance, as well as in the event another person will need to run the process to generate additional results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine

The continued development of plasma-assisted processing techniques requires a fundamental understanding of plasma-surface interactions. Molecular dynamics (MD) simulations have been employed to complement experimental studies and better understand the properties of such systems. Recently, machine learning (ML) methods have enabled the development of ab initio-based interatomic potentials, which can be generalized to complex combinations of multiple atom types. In this work, we use ML potentials developed using the Deep Potential Molecular Dynamics (DeepMD) framework to provide a model of ion-enhanced etching of Si by Cl atoms. We demonstrate the importance of proper selection of the training data set to the accuracy of the DeepMD model and compare our results to MD results using empirical potentials, as well as to experimental measurements. Exposure of undoped Si at 300 K to thermal Cl atoms yields a steady-state Cl coverage of 1.25 monolayers, which is slightly lower than the value obtained in previous experimental studies. Predictions of Si etch yields by simultaneous Cl atom and Ar + ion impacts as a function of ion energy, neutral to ion flux ratio, and angle of incidence of the ions are in reasonably good agreement with classical MD results and experimental measurements. Finally, etch yields and SiCl x mixed layer thicknesses during simultaneous bombardment of the Si(100) surface by Cl atoms and Cl + ions are in good agreement with experimental data. In conclusion, the present work is a necessary condition for the extension of the DeepMD procedure to more complex systems of interest in plasma-surface interactions.

Artificial neural networks↗
DOE OSTI2022

Discovery of Graphene-Water Membrane Structure: Toward High-Quality Graphene Process

It is widely accepted that solid-state membranes are indispensable media for the graphene process, particularly transfer procedures. But these membranes inevitably bring contaminations and residues to the transferred graphene and consequently compromise the material quality. This study reports a newly observed free-standing graphene-water membrane structure, which replaces the conventional solid-state supporting media with liquid film to sustain the graphene integrity and continuity. Experimental observation, theoretical model, and molecular dynamics simulations consistently indicate that the high surface tension of pure water and its large contact angle with graphene are essential factors for forming such a membrane structure. More interestingly, water surface tension ensures the flatness of graphene layers and renders high transfer quality on many types of target substrates. This report enriches the understanding of the interactions on reduced dimensional material while rendering an alternative approach for scalable layered material processing with ensured quality for advanced manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Numerically modeling stochastic inflation in slow-roll and beyond

In this work, we present a complete numerical treatment of inflationary dynamics under the influence of stochastic corrections from sub-Hubble modes. We discuss how to exactly model the stochastic noise terms arising from the sub-Hubble quantum modes that give rise to the coarse-grained inflaton dynamics in the form of stochastic differential equations. The stochastic differential equations are solved event by event on a discrete time grid. We then compute the power spectrum of curvature perturbations that can be compared with the power spectrum computed in the traditional fashion using the Mukhanov-Sasaki equation by canonically quantizing the inflaton fluctuations. Our numerical procedure helps us to easily extend the formalism to ultra slow-roll inflation and study the possibility of primordial black hole formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Investigation of the Origins of Comets as Revealed through Infrared High-resolution Spectroscopy I. Molecular Abundances

We report and analyze updated molecular abundances in 20 comets obtained by employing modern data reduction procedures and molecular models. Using box and scatter plots, we examine how the different molecular species are distributed among the comet population, while by means of pie charts, we investigate the relative proportions of these molecular species in each comet. We compare these results with the orbital parameters of the selected comets to identify trends related to their dynamical history. With these analyses, we tentatively identify at least three chemical classes based mainly on relative abundances of CO, CH{sub 3}OH, CH{sub 4}, C{sub 2}H{sub 6}, HCN, and NH{sub 3}. The combination of relative abundances and orbital parameters is then compared with recent chemical models of planetary system formation. This approach may help in investigating the origins and evolution of the material in cometary nuclei. Among other aspects, we underline the need to increase our sample size, especially for hypervolatiles (i.e., CH{sub 4} and CO) in Jupiter family comets.

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

The HUNTER Dynamic Human Reliability Analysis Tool: Coupling an External Plant Code

The Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER) is a framework to support dynamic human reliability analysis (HRA) with the aim to develop standalone software to perform the dynamic HRA calculations. Within the HRA, human actions in nuclear power plants (NPPs) are predicated by plant states, and human actions influence the plant. In other words, plant operations are necessarily recursive, and it becomes challenging to model complex human-plant interactions. Consequently, we have linked two software simulations that complement those shortcomings. RELAP5-3D—the Reactor Excursion and Leak Analysis Program (RELAP; Aumiller, Tomlinson, and Bauer 2001) is the foundational thermal-hydraulic software used to model nuclear systems. Using RELAP5-3D, we have simulated the plant operations proceeding according to procedures developed to address emergent situations in NPPs. Plant operations include various actions such as the operator checking plant parameters, as well as actions that are continuously performed over time until a specific parameter reaches certain criteria. That means that HUNTER and RELAP5-3D exchange information with each other and should be carried out simultaneously over time. To simulate plant operations, which represent the actual operator checks of plant parameters and corresponding manual control actions, changes in plant status are identified through simulation and performed according to the criteria and order of the procedure. Thus, the goal of coupling HUNTER with RELAP5-3D is to facilitate synchronous coupling, where human and plant models provide iterative feedback loops that drive the course of actions. The advantage of coupling with RELAP5-3D to serve as the external environment module in HUNTER is the ability to customize the plant model and streamline for particular model applications. In this paper, we will address the key features of the coupling and the coupling structure built to perform the feedback loops.

99 GENERAL AND MISCELLANEOUS↗