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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exploration of a Potential Desirability of Outcome Ranking Endpoint for Complicated Intra-Abdominal Infections Using 9 Registrational Trials for Antibacterial Drugs

Abstract Background Desirability of outcome ranking (DOOR) is a novel approach to clinical trial design that incorporates safety and efficacy assessments into an ordinal ranking system to evaluate overall outcomes of clinical trial participants. Here, we derived and applied a disease-specific DOOR endpoint to registrational trials for complicated intra-abdominal infection (cIAI). Methods Initially, we applied an a priori DOOR prototype to electronic patient-level data from 9 phase 3 noninferiority trials for cIAI submitted to the US Food and Drug Administration between 2005 and 2019. We derived a cIAI-specific DOOR endpoint based on clinically meaningful events that trial participants experienced. Next, we applied the cIAI-specific DOOR endpoint to the same datasets and, for each trial, estimated the probability that a participant assigned to the study treatment would have a more desirable DOOR or component outcome than if assigned to the comparator. Results Three key findings informed the cIAI-specific DOOR endpoint: (1) a significant proportion of participants underwent additional surgical procedures related to their baseline infection; (2) infectious complications of cIAI were diverse; and (3) participants with worse outcomes experienced more infectious complications, more serious adverse events, and underwent more procedures. DOOR distributions between treatment arms were similar in all trials. DOOR probability estimates ranged from 47.4% to 50.3% and were not significantly different. Component analyses depicted risk-benefit assessments of study treatment versus comparator. Conclusions We designed and evaluated a potential DOOR endpoint for cIAI trials to further characterize overall clinical experiences of participants. Similar data-driven approaches can be utilized to create other infectious disease–specific DOOR endpoints.

Immunology↗
DOE OSTI2016

2015 In-Situ Gamma-Ray Assay of the West Cell Line in the 235-F Plutonium Fuel Form Facility

In November and December 2015, scientists from SRNL took a series of in-situ gamma-ray measurements through the windows in front of Cells 6-9 on the west line of the PuFF facility using a shielded, 120% high-purity germanium detector. The detector efficiency was estimated using a combination of MCNP simulations and empirical measurements. Where possible, the distribution of the Pu-238 in the cells was determined using the Germanium Gamma-ray Imager (GeGI). This distribution was then fed into the MCNP model to quantify the Pu-238 in each cell. Data analysis was performed using three gamma rays emitted by Pu-238 (99.85 keV, 152.7 keV, and 766.4 keV) providing three independent estimates of the mass of Pu-238 holdup in each of the cells.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Probability Density Function Control for Stochastic Nonlinear Systems using Monte Carlo Simulation

This paper presents a fast implementation framework of output probability density function (PDF) control for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. The statistical properties of the system outputs can be adjusted by shaping the dynamic output probability density function to track the reference stochastic distribution. However, the dynamic probability density function evolution is very difficult to obtain analytically even if the system model and the stochastic distributions of the noises are known. Motivated by Monte Carlo simulation, the dynamic probability density function can be estimated by sampling data which forms the contribution of this paper. In particular, the sampling points are generated following the stochastic distribution of the noise for each instant. These points go through the system would generate the histogram for system outputs, then the dynamic model can be established based on the dynamic histogram which reflects the randomness and the nonlinear dynamics of the investigated system. Based on the established model, the output probability density function tracking can be achieved and the simulation results and discussions show the effectiveness and benefits of the presented framework.

Zhang, Qichun↗
DOE OSTI2017

Radiochemical study of the kinematics of multi-nucleon transfer reactions in 48 Ca + 248 Cm collisions 10% above the Coulomb barrier

In this paper, the kinematics of multi-nucleon transfer reactions in 48 Ca + 248 Cm collisions at 262 MeV (center of target) was investigated by using a stacked-foil technique and radiochemical separations of trans-curium elements. Trans-curium isotopes were identified by α-particle spectroscopy. For Fm isotopes, by comparing the centroids of the measured post-neutron emission isotope distributions with the most probable primary mass number predicted by Volkov's generalized Q g g systematics, the missing mass (number of evaporated neutrons) is estimated. The latter is compared with that deduced from the measured centroid of the laboratory angular distribution peaked closely to the grazing angle and the centroid of the range distribution, being used to determine the average total kinetic energy loss (TKEL) and the average excitation energy. The latter agrees within the uncertainties with the missing mass so that a consistent picture of the reaction mechanism emerges. For products closer to the target Z, e.g., Cf and Bk, the distributions of kinetic energies are much broader than for Fm, reflecting the fact that in the former, values of TKEL reach from quasi-elastic scattering all the way to deep inelastic scattering. Finally, the measured laboratory angular distribution and the average laboratory kinetic energy of the Fm isotopes, being the prototypes for multi-nucleon transfer products, are benchmark values for the design of electromagnetic separators to be constructed for the separation and detection of unknown neutron-rich transactinides produced in this nuclear reaction type.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2015

Hadron mass corrections in semi-inclusive deep-inelastic scattering

We found that the spin-dependent cross sections for semi-inclusive lepton-nucleon scattering are derived in the framework of collinear factorization, including the effects of masses of the target and produced hadron at finite Q 2 . At leading order the cross sections factorize into products of parton distribution and fragmentation functions evaluated in terms of new, mass-dependent scaling variables. Furthermore, the size of the hadron mass corrections is estimated at kinematics relevant for current and future experiments, and the implications for the extraction of parton distributions from semi-inclusive measurements are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation

Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modeling building height at scale are hindered by two pervasive issues. First, there is no consistent approach to quantify what a high-rise building is at a macro scale, leaving researchers unable to accurately compare results across geographies and domains. Second, high-rise buildings represent a small fraction of the built environment, implying data imbalance challenges that negatively affect current approaches. This is a problem of practical relevance since information on high-rise buildings is important for studies on urban heat islands, population dynamics, and pollution dispersion. Here, we introduce a novel approach to map building height which first identifies two distinct distributions within the built environment, with one being composed of low-rise buildings and one composed of high-rise buildings. We then develop an ensemble scheme where discrete specialist models are trained for each subset of low-rise buildings and high-rise buildings to infer building height from morphology features. For experiments mapping heights of 4.85 million buildings in Japan, we show an increase of 34 % in accuracy within 3m error when compared to the current state-of-the-art when modeling high-rise buildings, which based on KNN experimentation we define as any building > 12m . Our findings show that such an ensemble framework outperforms the current state-of-the-art approaches, which is especially relevant in relation to inferring height for high-rise buildings, a prominent issue of existing approaches for mapping the built environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 2. Quantitative Moisture Dynamics Estimation Model

Abstract The long‐term containment of high‐level radioactive waste in geological disposal repositories relies on Engineered Barrier Systems (EBS), with bentonite clay emerging as a candidate material due to its unique properties. Understanding moisture dynamics within bentonite buffers is crucial for EBS performance, as it directly influences the material's swelling capacity, thermal and hydraulic conductivity, mechanical properties, and long‐term evolution under complex thermal‐hydrological‐mechanical (THM) processes. This study develops an advanced Electrical Resistivity Tomography (ERT)‐based framework to quantitatively monitor moisture dynamics under THM conditions. Our framework extends the Waxman‐Smits model to incorporate the coupled effects of temperature, water content, fluid chemistry, and mechanical changes on bentonite's electrical properties. Utilizing HotBENT‐Lab data from our companion paper, which includes electrical conductivity, CT density, and thermocouple measurements, this study offers a novel methodological framework bridging different scales of the model. Our results show that the extended model can estimate water content from ERT data, capturing spatial and temporal variations in moisture distribution within bentonite columns. However, the model tends to overestimate water content compared to CT density‐derived measurements. We address this discrepancy by incorporating a simplified swelling effect model, which improves agreement between ERT and CT density‐based water content estimates. We also discuss model limitations, including simplified treatment of swelling and micropore effects, and propose a conceptual framework for transitioning from laboratory to field applications, addressing challenges such as parameter scalability, field validation methods, and integration of diverse data sources. This ERT‐based framework can potentially advance real‐world moisture monitoring of bentonite‐based EBS in nuclear waste repositories. Plain Language Summary Safely containing high‐level radioactive waste depends on barriers made from materials like bentonite clay, which is effective because it swells and seals in the waste. To ensure these barriers work well over time, it's important to understand how moisture moves through the clay. Our study developed a new method using ERT to monitor moisture levels in bentonite under conditions that mimic those in actual storage sites, including changes in temperature, water content, and mechanical stress. This study improved an existing model to better account for how these factors affect the clay, allowing us to create more accurate moisture maps. Initially, the proposed model overestimated the amount of water in the clay, but its accuracy was improved by factoring in how the clay swells when wet. This study also identified some limitations of the model and suggested ways to adapt it for use in real‐world waste storage sites. This new approach could lead to better monitoring and safety checks for nuclear waste storage systems, helping to ensure long‐term containment. Key Points This work develops an ERT‐based framework extending the Waxman‐Smits model to monitor bentonite moisture dynamics during coupled THM processes The extended model accurately estimates water content from Electrical Resistivity Tomography data, incorporating swelling effects to improve precision This work proposes a conceptual framework for transitioning from laboratory to field applications, advancing EBS monitoring in nuclear waste repositories

Chen, Hang↗
DOE OSTI2018

A Fast Scalable Quasi-Static Time Series Analysis Method for PV Impact Studies Using Linear Sensitivity Model

Understanding the impact of distributed photovoltaic (PV) resources on various elements of the distribution feeder is imperative for their cost effective integration. A year-long quasi-static time series (QSTS) simulation at 1-second granularity is often necessary to fully study these impacts. However, the significant computational burden associated with running QSTS simulations is a major challenge to their adoption. In this paper, we propose a fast scalable QSTS simulation algorithm that is based on a linear sensitivity model for estimating voltage-related PV impact metrics of a three-phase unbalanced, nonradial distribution system with various discrete step control elements including tap changing transformers and capacitor banks. Here, the algorithm relies on computing voltage sensitivities while taking into account all the effects of discrete controllable elements in the circuit. Consequently, the proposed sensitivity model can accurately estimate the state of controllers at each time step and the number of control actions throughout the year. For the test case of a real distribution feeder with 2969 buses (5469 nodes), 6 load/PV time series power profiles, and 9 voltage regulating elements including controller delays, the proposed algorithm demonstrates a dramatic time reduction, more than 180 times faster than traditional QSTS techniques.

14 SOLAR ENERGY↗
DOE OSTI2015

Stellar velocity dispersion and anisotropy of the Milky Way inner halo

We measure the three components of velocity dispersion, σ{sub R}, σ{sub θ}, σ{sub ϕ}, for stars within 6 < R < 30 kpc of the Milky Way using a new radial velocity sample from the MMT telescope. We combine our measurements with previously published data so that we can more finely sample the stellar halo. We use a maximum likelihood statistical method for estimating mean velocities, dispersions, and covariances assuming only that velocities are normally distributed. The alignment of the velocity ellipsoid is consistent with a spherically symmetric gravitational potential. From the spherical Jeans equation, the mass of the Milky Way is M(R⩽12 kpc)=1.3×10{sup 11} M{sub ⊙} with an uncertainty of 40%. We also find a region of discontinuity, 15 ≲ R ≲ 25 kpc, where the estimated velocity dispersions and anisotropies diverge from their anticipated values, confirming the break observed by others. We argue that this break in anisotropy is physically explained by coherent stellar velocity structure in the halo, such as the Sgr stream. To significantly improve our understanding of halo kinematics will require combining radial velocities with future Gaia proper motions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Measurement of sputtered beryllium yield and angular distribution during nanostructure growth in a helium plasma

Here, the angular distribution and sputtering yield of beryllium exposed to helium plasma are estimated from analysis of line-integrated 2D imaging of Be-I line emission in a steady-state linear plasma device. As the surface nanostructure forms during plasma exposure on a ~100 s timescale (corresponding to a fluence of order 10 20 /cm 2 ) from nearly mono-energetic ion bombardment, a narrowing of the beryllium sputtering angle and a significant (~5×) drop in sputtering yield are observed. Lastly, these trends are found to be qualitatively consistent with modeling taking into account the effect of the surface morphology on sputtering yield and angular distribution.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Distributed quantum sensing enhanced by continuous-variable error correction

Abstract A distributed sensing protocol uses a network of local sensing nodes to estimate a global feature of the network, such as a weighted average of locally detectable parameters. In the noiseless case, continuous-variable (CV) multipartite entanglement shared by the nodes can improve the precision of parameter estimation relative to the precision attainable by a network without shared entanglement; for an entangled protocol, the root mean square estimation error scales like 1/ M with the number M of sensing nodes, the so-called Heisenberg scaling, while for protocols without entanglement, the error scales like 1 / M . However, in the presence of loss and other noise sources, although multipartite entanglement still has some advantages for sensing displacements and phases, the scaling of the precision with M is less favorable. In this paper, we show that using CV error correction codes can enhance the robustness of sensing protocols against imperfections and reinstate Heisenberg scaling up to moderate values of M . Furthermore, while previous distributed sensing protocols could measure only a single quadrature, we construct a protocol in which both quadratures can be sensed simultaneously. Our work demonstrates the value of CV error correction codes in realistic sensing scenarios.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Hierarchical Bayesian estimation for adsorption isotherm parameter determination

Estimation of isotherm model parameters from experimental data is necessary in adsorption process modeling. To facilitate isotherm modeling, databases that store experimental data exist. Nevertheless, data inconsistency among different researchers must be resolved to find a single set of isotherm parameters from multiple data sets. Herein, we propose a hierarchical Bayesian estimation method to quantify the discrepancy by a multiplicative factor while simultaneously obtaining the probability distributions of a single set of isotherm parameters. This approach also allows us to identify outliers, which are data sets that are not in agreement with other sets. The proposed approach is demonstrated for the case of CO 2 adsorption in the metal-organic framework UiO-66.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Median statistics estimate of the galactic rotational velocity

We compile a complete collection of 18 recent (from 2000 to 2017) measurements of Θ 0 , the rotational velocity of the Milky Way at R 0 (the radial distance of the Sun from the Galactic center). These measurements use tracers that are believed to more accurately reflect the systematic rotation of the Milky Way. Unlike other recent compilations of Θ 0 , our collection includes only independent measurements. We find that these 18 measurements are distributed in a mildly non-Gaussian fashion and a median statistics estimate indicates Θ 0 = 220 ± 10 kms –1 (2σ error) as the most reliable summary, at R 0 = 8.0 ± 0.3 kpc (2σ error).

(cosmology:) cosmological parameters↗
DOE OSTI2026

Powered By ERAD [Slides]

Energy Resilience Analysis for Distribution Power System (ERAD) is a free, open-source Python toolkit for estimating the energy and service impacts of hazards like earthquakes and flooding. It uses a graph-based approach to capture high resolution connectivity among the grid, critical services, and customers and rapidly compute household level metrics and aggregated statistics across large distribution systems. It uses asset fragility curves that relate hazard severity to survival probability for power system equipment including cables, transformers, substations, etc. The tool is designed to be modular and extensible, allowing it to interface with third-party hazard simulators and integrate into broader resilience analysis workflows. ERAD enables researchers, students, communities, distribution utilities, and other stakeholders to understand hazard impacts and evaluate the effectiveness of different programs to improve energy resilience. The webinar was hosted by NLR researcher Aadil Latif.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Non-Invasive Approach for Elucidating the Spatial Distribution of In-Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage (Task 3 Report Laboratory Characterization of Stress Dependent Wavespeed)

This report presents a summary of Task 3 (Laboratory Experimental Characterization of Stress Dependent Wavespeed in Rocks and Application for In-Situ Stress Estimation) of the project titled “A Non-Invasive Approach for Elucidating the Spatial Distribution of In-Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage”. The main objective of this component of the project was to employ non-destructive techniques to determine how compressional (P)-wave and shear (S)-wave propagation velocities are related to triaxial-stress conditions in laboratory test samples of targeted reservoir rocks. The overarching principle is that such laboratory measurements can provide a basis whereby field measurements of wave-propagation velocities in rocks can be used to ascertain the stress state that would be expected to generate the observed wave-propagation behavior.

58 GEOSCIENCES↗
DOE OSTI2025

Assessing hydrogen supply chains: An integrated review of leakage and energy efficiency studies

This paper examines hydrogen leakage and efficiency across the supply chain for liquid, gaseous, and mixed hydrogen systems. These factors are crucial for assessing hydrogen's role in mitigating emissions and facilitating a clean energy transition. Drawing on a comprehensive review of existing literature and model-based analysis, the study compiles leakage rates and efficiency metrics at each stage of the supply chain: production, storage, transmission, distribution, and end-use. These data inform system scenarios that estimate the impact of leakage on overall performance and climate benefits. The analysis also identifies persistent data gaps, particularly for liquid and mixed system configurations, and outlines priorities for future research. A comparison of hydrogen system types shows that gaseous pathways generally achieve the highest efficiencies (28 %–39 %) and the lowest leakage rates (∼4.5 %) across the supply chain. Liquid hydrogen systems, while favorable for long-distance and high-volume transport due to their higher energy density, exhibit lower efficiency (∼28 %) and a greater leakage potential (∼12 %). Mixed systems, which combine gaseous and liquid elements (e.g., pipeline transmission followed by liquefaction and truck distribution), show compounded energy losses and moderate-to-high leakage rates (6.8 %–9.4 %), highlighting trade-offs associated with added system complexity. The study highlights opportunities for technological advancements, including optimizing liquefaction, enhancing insulation for storage and transportation, and refining refueling equipment. These improvements are crucial for maximizing the climate benefits of hydrogen. The results offer actionable insights for researchers, industry, and policymakers working to develop low-leakage, high-efficiency hydrogen infrastructure.

08 HYDROGEN↗
DOE OSTI2024

Recovered supernova Ia rate from simulated LSST images

Aims.TheVera C. RubinObservatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods.We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg 2 region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results.We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.

Astronomy & Astrophysics↗
DOE OSTI2016

Multi-level methods and approximating distribution functions

Biochemical reaction networks are often modelled using discrete-state, continuous-time Markov chains. System statistics of these Markov chains usually cannot be calculated analytically and therefore estimates must be generated via simulation techniques. There is a well documented class of simulation techniques known as exact stochastic simulation algorithms, an example of which is Gillespie’s direct method. These algorithms often come with high computational costs, therefore approximate stochastic simulation algorithms such as the tau-leap method are used. However, in order to minimise the bias in the estimates generated using them, a relatively small value of tau is needed, rendering the computational costs comparable to Gillespie’s direct method. The multi-level Monte Carlo method (Anderson and Higham, Multiscale Model. Simul. 10:146–179, 2012) provides a reduction in computational costs whilst minimising or even eliminating the bias in the estimates of system statistics. This is achieved by first crudely approximating required statistics with many sample paths of low accuracy. Then correction terms are added until a required level of accuracy is reached. Recent literature has primarily focussed on implementing the multi-level method efficiently to estimate a single system statistic. However, it is clearly also of interest to be able to approximate entire probability distributions of species counts. We present two novel methods that combine known techniques for distribution reconstruction with the multi-level method. We demonstrate the potential of our methods using a number of examples.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗