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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 sources NASA NTRS DOE OSTI

DOE OSTI2016

aafnavdsm10hz (a1)

This umbrella contains data from all instrumentation associated with the aircraft’s navigational location, motion, and attitude data. Navigational instrumentation on the Gulfstream-159 includes navigational data recorded from the AIMMS-20 (for other AIMMS produced data see the instrument category MET-AIR), CMigits global positioning system (GPS) & Inertial Navigational System (INS) Tactical System, Differential Survey Module (DSM), and VectorNav VN-200 INS. For a higher-order product that contains navigational data integrated with meteorological data and sampling masks/flags to aid analysis please see the categorical value added product NAVMET-AIR.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

aafnavcmig (a1)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

aafnavdsm1hz (a1)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d -1 (95% CI: 1.06 to 1.27 d -1 ), 0.777 d -1 (0.716 to 0.838 d -1 ), and 0.450 d -1 (0.378 to 0.522 d -1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

A new approach to estimate fecundity rate from inter‐birth intervals

Abstract Accurate estimates of fecundity rate are key to population assessments and effectively direct conservation efforts. We present a new approach to estimate fecundity rate based on the probability of a female giving birth, conditional on a previous birth t years ago, from which an expected inter‐birth interval ( IBI ) can be estimated. We use generalized linear mixed‐effects models to account for individual and temporal variability and apply the approach to individual reproductive histories of bottlenose dolphins ( Tursiops truncatus ) from the east coast of Scotland. We estimate a fecundity rate of 0.222 (95% CI = 0.218–0.253) and an expected IBI of 4.49 yr (95% CI = 3.94–4.93 yr). We use simulated data samples to show that the approach produces estimates with a minimum bias of <3%. Simulations are also used to investigate the effect of the most common data‐driven biases in the estimates of birth intervals and fecundity rate; we recommend longitudinal studies of at least 10 yr and capture probabilities of at least 0.3 when using this methodology. The approach may be modified to incorporate other parameters of interest and should be applicable to any population with comprehensive data on birth intervals.

Arso Civil, Mònica↗

DOE OSTI2018

A fuzzy logistic regression model based on the least squares estimation

To construct the relationship between fuzzy inputs and fuzzy output described by linguistic variable, we develop a fuzzy logistic regression model, which can be applied for various problems, such as clinical research, risk investment and decision making. In this regard, we introduce the integral calculation of distance of cut sets and a fuzzy adjustment term which could prevent a large fuzzy error for a fuzzy output caused by the output degenerating into crisp number when the independent variables are crisp numbers. The parameters of the fuzzy logistic regression model are derived by means of the least squares method. We adopt three criteria involving mean square error, Kim and Bishu criterion and capability index and also perform two ways of prediction including the in-sample forecast as well as the one-leave-out cross validation. The comparisons with five existing methods show that our proposed method has satisfactory performance and the results are illustrated in some case studies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗

DOE OSTI2026

Transient uncertainty quantification and Global Sensitivity Analysis of the open-source Molten Chloride Reactor Experiment (MCRE) using GP-PCA surrogate models

Uncertainties in the thermophysical properties of molten salts impact both the steady-state and transient behavior of Molten Salt Reactors (MSRs). In this work, we aim to quantify the influence of such uncertainties on the transient operation of the Molten Chloride Reactor Experiment (MCRE), utilizing the open-source specifications provided for this reactor. Seven representative transient scenarios are considered. For each scenario, we evaluate the impact of thermophysical property uncertainties on four key multiphysics model output variables of interest (VoIs): maximum power density, maximum fuel temperature, maximum reflector temperature, and average fuel velocity magnitude. In addition, we perform a Global Sensitivity Analysis (GSA) by computing Sobol’ indices for the uncertain input parameters to determine their contribution to the variability of each VoI. Conducting GSA is computationally intensive due to the large number of required evaluations of the high-fidelity multiphysics model. To mitigate this cost, we develop a surrogate modeling framework that combines Gaussian Process (GP) regression with Principal Component Analysis (PCA), enabling efficient sample generation for the GSA. Our results show that for energy-related VoIs, thermal conductivity is the dominant contributor to uncertainty. In contrast, for flow-related VoIs, density and dynamic viscosity are the primary sources of uncertainty. The specific heat of the fuel salt was found to play a secondary role in the transient analyses.

42 - ENGINEERING↗

DOE OSTI2020

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g catalyst in the original data set. Overall, this combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.

25 ENERGY STORAGE↗

DOE OSTI2015

Grain size tuning of nanostructured Cu{sub 2}O films through vapour phase supersaturation control and their characterization for practical applications

A strategy for creating nanostructured films is the alignment of nanoparticles into ordered superstructures as living organisms synthesize biomaterials with superior physical properties using nanoparticle building blocks. We synthesized nanostructured films of Cu{sub 2}O of variable grain size by establishing the condition of supersaturation for creation of nanoparticles of copper which deposited as nanograined films and which was then oxidized. This technique has the advantage of being compatible with conventional vacuum processes for electronic device fabrication. The Cu{sub 2}O film samples consisted of a secondary structure of spherical particles of almost uniform size, each particle being an agglomerate of primary nanocrystals. Fractal analysis of the AFM images of the samples is carried out for studying the aggregation mechanism. Grain size tuning of the nanostructured Cu{sub 2}O films has been studied using XRD, and micro-Raman and photoluminescence spectroscopy.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Normal and outlying populations of the Milky Way stellar halo at [Fe/H] <–2

From detailed abundance analysis of >100 Hamburg/ESO candidate extremely metal-poor (EMP) stars we find 45 with [Fe/H] < –3.0 dex. We identify a heretofore unidentified group: Ca-deficient stars with sub-solar [Ca/Fe] ratios and the lowest neutron-capture abundances; the Ca-deficient group comprises ∼10% of the sample, excluding Carbon stars. Our radial velocity distribution shows that the carbon-enhanced stars with no s-process enhancements, CEMP-no, and which do not show C{sub 2} bands are not preferentially binary systems. Ignoring Carbon stars, approximately 15% of our sample are strong (≥5σ) outliers in one or more elements between Mg and Ni; this rises to ∼19% if very strong (≥10σ) outliers for Sr and Ba are included. Examples include: HE0305–0554 with the lowest [Ba/H] known; HE1012–1540 and HE2323–0256, two (non-velocity variable) C-rich stars with very strong [Mg,Al/Fe] enhancements; and HE1226–1149, an extremely r-process rich star.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

SHORT- AND LONG-TERM RADIO VARIABILITY OF YOUNG STARS IN THE ORION NEBULA CLUSTER AND MOLECULAR CLOUD

We have used the Karl G. Jansky Very Large Array (VLA) to carry out multi-epoch radio continuum monitoring of the Orion Nebula Cluster (ONC) and the background Orion Molecular Cloud (OMC; 3 epochs at Q band and 11 epochs at Ka band). Our new observations reveal the presence of 19 radio sources, mainly concentrated in the Trapezium Cluster and the Orion Hot Core (OHC) regions. With the exception of the Becklin–Neugebauer object and source C (which we identify here as dust emission associated with a proplyd) the sources all show radio variability between the different epochs. We have found tentative evidence of variability in the emission from the massive object related to source I. Our observations also confirm radio flux density variations of a factor >2 on timescales of hours to days in five sources. One of these flaring sources, OHC-E, has been detected for the first time. We conclude that the radio emission can be attributed to two different components: (i) highly variable (flaring) non-thermal radio gyrosynchrotron emission produced by electrons accelerated in the magnetospheres of pre-main-sequence low-mass stars and (ii) thermal emission due to free–free radiation from ionized gas and/or heated dust around embedded massive objects and proplyds. Combining our sample with other radio monitoring at 8.3 GHz and the X-ray catalog provided by Chandra, we have studied the properties of the entire sample of radio/X-ray stars in the ONC/OMC region (51 sources). We have found several hints of a relation between the X-ray activity and the mechanisms responsible for (at least some fraction of) the radio emission. We have estimated a radio flaring rate of ∼0.14 flares day{sup −1} in the dense stellar cluster embedded in the OHC region. This suggests that radio flares are more common events during the first stages of stellar evolution than previously thought. The advent of improved sensitivity with the new VLA and ALMA will dramatically increase the number of stars in young clusters detected at radio wavelengths, which will help us improve our understanding of the origin and nature of the radio emission.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

PERIODOGRAMS FOR MULTIBAND ASTRONOMICAL TIME SERIES

This paper introduces the multiband periodogram, a general extension of the well-known Lomb–Scargle approach for detecting periodic signals in time-domain data. In addition to advantages of the Lomb–Scargle method such as treatment of non-uniform sampling and heteroscedastic errors, the multiband periodogram significantly improves period finding for randomly sampled multiband light curves (e.g., Pan-STARRS, DES, and LSST). The light curves in each band are modeled as arbitrary truncated Fourier series, with the period and phase shared across all bands. The key aspect is the use of Tikhonov regularization which drives most of the variability into the so-called base model common to all bands, while fits for individual bands describe residuals relative to the base model and typically require lower-order Fourier series. This decrease in the effective model complexity is the main reason for improved performance. After a pedagogical development of the formalism of least-squares spectral analysis, which motivates the essential features of the multiband model, we use simulated light curves and randomly subsampled SDSS Stripe 82 data to demonstrate the superiority of this method compared to other methods from the literature and find that this method will be able to efficiently determine the correct period in the majority of LSST’s bright RR Lyrae stars with as little as six months of LSST data, a vast improvement over the years of data reported to be required by previous studies. A Python implementation of this method, along with code to fully reproduce the results reported here, is available on GitHub.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Residual Stresses and Other Properties of Teardrops

The Department of Energy’s 3013 Standard for packaging plutonium-bearing materials for storage up to fifty years specifies a minimum of two individually welded, nested containers herein referred to as the 3013 outer and the 3013 inner.1 Stress corrosion cracking (SCC) is a potential failure mechanism for 3013 inner containers.2,3 The bagless transfer container (BTC), a 3013 inner container used by Hanford and Savanna River Site (SRS) made from 304L stainless steel (SS), poses the greatest concern for SCC.4,5 The Surveillance and Monitoring Program (SMP) use stressed metal samples known as teardrops as screening tools in SCC studies to evaluate factors that could result in cracks in the 3013 containers.6,7 This report provides background information on the teardrops used in the Los Alamos National Laboratory (LANL) SMP studies including method of construction, composition and variability. In addition, the report discusses measurements of residual stresses in teardrops and compares the results with residual stresses in BTCs reported previously.4 Factors affecting residual stresses, including teardrop dimensions and surface finish, are also discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2024

High-quality Extragalactic Legacy-field Monitoring (HELM) with DECam

High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4m Blanco telescope. Since Feb 2019, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the $(u)gri(z)$ bands, over a collective sky area of $\sim 38$ deg${\rm ^2}$. The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependences on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first $\sim 3.5$ years of light curves during 2019A--2022A.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Pump and Treat Impacts on Aquifer Conditions and the Development of System-wide Performance Strategies - 19077

The 200 West pump-and-treat (P and T) system on the Hanford Site's Central Plateau currently treats multiple contaminants within the 200-ZP-1 Operable Unit (OU). Initially designed to support the 200-ZP1 Remedial Action Objectives, the use of the P and T system has evolved over time to address contaminants from other regions of the aquifer as well. The P and T system currently receives water from other regions within the Central Plateau that are biogeochemically distinct, treating groundwater with elevated concentrations of carbon tetrachloride, cyanide, uranium, technetium-99, and nitrate. Given the varying geochemical signatures from current and potential future sources, combined with the operational conditions and amendments used for treatment processes, there is a need to identify the impacts of the P and T system on the aquifer and aquifer impacts on P and T performance. To this end, initial laboratory analyses and geochemical modeling efforts focused on characterizing the baseline geochemical environment for the aquifer in the 200-ZP-1 OU. Several sediment characterization techniques, including geologic descriptions, water and acid extractions, and microbiological measurements were selected to determine key geochemical and microbiological constituents for the baseline, prior to injection of the effluent from the treatment plant. Different geochemical components within sediment samples from the aquifer were identified that may significantly affect how the sediments react to P and T injection water. Batch experiments were also used to identify aquifer impacts on operational performance and support development of some column studies. These experiments were designed to test and evaluate variable P and T effluents using actual effluent solutions from the treatment plant as well as simulated Hanford groundwater at different temperatures to identify the important geochemical and biological reactions that might occur within the aquifer. Initial results were used for geochemical modeling of the effluents and batch experiments. These results, along with the baseline data obtained from the initial sediment characterization, are intended to feed into the design of the column studies and inform conceptual models and modeling efforts for evaluating the fate and transport of introduced contaminants and co-injectates, and their short- and long-term impact on the aquifer-system injection capacity. This evaluation is designed to provide additional information on the major geochemical and microbiological reactions that are driven by the injection of the P and T effluent into the aquifer. Reactive transport modeling of the laboratory experiments and extrapolation to a larger-scale modeling domain provide information that can be used for predictive evaluations of system performance based on P and T operational changes and aquifer mass loading and capacity estimates. This information can be used to address combined subsurface and operational challenges, such as the need for frequent well redevelopment to maintain injection capacity. This information also provides the technical basis for decisions related to the P and T system operations that support remedy optimization efforts and short- and long-term remedy decisions related to multiple OUs on the Hanford Site Central Plateau. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection

Abstract A parameterization scheme is proposed for the subgrid-scale transport of hydrometeors in an assumed probability density function (PDF) scheme. Joint distributions of vertical velocity and hydrometeor mixing ratios are typically unknown, but marginal (1D) PDFs of these variables are available. The parameterization is developed using high-resolution simulations of continental and tropical deep convection. A 3D cloud-resolving model (CRM) providing benchmark solutions has a horizontal grid spacing of 250 m and employs the Morrison microphysics scheme, which treats prognostically mass and number mixing ratios for four types of precipitating hydrometeors (rain, graupel, snow, and ice) as well as cloud droplet number mixing ratio. The subgrid-scale hydrometeor transport scheme assumes input given in the form of marginal PDFs of vertical velocity and hydrometeor mixing ratios; in this study, these marginal distributions are provided by the cloud-resolving model. Conditional sampling and scaling are then applied to the marginal distributions to account for subplume correlations. The parameterized fluxes tested for four episodes of deep convection show good agreement with benchmark fluxes computed directly from the CRM output. The results demonstrate the potential use of the subgrid-scale hydrometeor transport scheme in an assumed PDF scheme to parameterize the covariances of vertical velocity and hydrometeor mixing ratios.

Wong, May↗

DOE OSTI2020

Sensitivity and Uncertainty Quantification of Transition Scenario Simulations

This report documents the first collective attempt at developing and applying capabilities to quantify uncertainties, assess parametric sensitivities, and optimize multiple parameters and metrics in fuel cycle simulations generated by the SA&I Campaign. To do this, external codes that were designed to perform sensitivity analysis and uncertainty quantification (SA&UQ) needed to be coupled to the SA&I Campaign’s nuclear fuel cycle simulators (NFCS). In FY20, two approaches were pursued: 1) coupling Cyclus to an ORNL-internal code called MOT (Metaheuristic Optimization Tool) and 2) coupling DYMOND to the opensource SA&UQ tool kit Dakota. The primary objective of having these NFCS/SA&UQ coupled capabilities is to better inform DOE-NE and other stakeholders on the results generated from the NFCS. For a given set of fuel cycle strategies, policies, and technology assumptions that make up a fuel cycle scenario, these NFCS have traditionally been used by the SA&I Campaign to provide quantitative answers in terms of year-by-year mass flows, infrastructure requirements, costs, etc. With these newly developed coupled capabilities, the SA&I Campaign can now efficiently simulate hundreds or thousands of these scenarios, sample large ranges of parameters and assumptions, and use the unique features of the SA&UQ tools to process the data. This enables providing answers with known and propagated uncertainties, determining the sensitivity of important metrics to different parameters and assumptions, quantifying how much fuel cycle and technology parameters impact each other, and producing optimized fuel cycle strategies for single and multiple variables. To demonstrate these new capabilities, the Cyclus/MOT was used to model several scenarios ranging from simple fleet retirements to transitions to advanced reactors. Specifically, for a transition scenario from LWRs to SFRs and advanced LWRs, uncertainty quantification, sensitivity analysis, and optimization studies were applied to cases involving single and multiple parameter (input) and single and multiple metric (output) variations. In addition, a similar transition scenario was modeled to demonstrate how to optimize the reprocessing capacity parameter to minimize two performance metrics while taking into account uncertainties from two other parameters. Lastly, a depletion module based on SCALE/ORIGEN was added in Cyclus to simulate the third scenario that was designed to quantify the impact of the modeling assumption that all LWR used nuclear fuel have the same burnup. The newly developed DYMOND/Dakota capability was also applied to a transition scenario from the existing fleet to small modular reactors and fast reactors. This particular scenario involves not only explicit isotopic depletion via ORIGEN-2, but also includes multirecycling and utilizing the criticality search feature to determine the fresh fuel composition of recycled fuel, a feature unique to the DYMOND NFCS. A large database of simulations were run with 4 main parameters that were sampled: start date of reprocessing, reprocessing capacity, energy demand growth rate, and advanced reactor share of the fleet. The 4 main metrics were uranium consumption, enrichment requirements, waste generation, and levelized cost of electricity using data from the Cost Basis Report. The demonstrated SA&UQ results include those that inform on how to choose parameters to avoid “failed” scenarios, Sobol’ indices that inform on the importance of various parameters individually and synergistically, and Analysis of Variance (ANOVA) studies that decompose parameter ranges into groups and informs on whether variations are statistically significant.

Feng, B.↗