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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,171 records · Page 65

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NASA NTRS1985

A search in the COS-B data base for correlated time variability in regions containing objects of interest

As is well known, association of the gamma-ray sources with celestial objects is, in general, difficult on a pure positional basis, while correlated time variability could obviously provide such proof. This technique can be employed on regions of the gamma-ray sky containing interesting objects of known variability at some wavelength even in the absence of a recognized gamma-ray excess with the aim to extract a weak but predictable signal from the surrounding noise. This technique is applied here on a longer variability time scale, generally of the order of days. Photons coming from the sky regions centered on the various celestial objects considered were selected with energies 100 MeV and with arrival directions within an energy-dependent area of radius of approx 6 deg at 100 MeV. In order to construct a time profile of such photons, their arrival times were grouped in bins of dimensions defined by the available photons number and by the value of the period searched for.

Caraveo, P. A.↗

DOE OSTI2024

Development and Implementation of a Bag Degradation Predictive Tool at Los Alamos National Laboratory

Nuclear material packaging involves many complex variables derived from the stored material's properties, the storage environment, and the synergistic interaction of said material and its environment on the containment boundary. In the Plutonium Facility (PF-4) at Los Alamos National Laboratory (LANL) the typical containment boundary found, starting from the stored material itself to the outermost containment layer, consists of the material contained within a stainless steel (typically 304) slip-lid (or other equivalent stainless steel packaging layer) wrapped in an sPVC bag-out bag contained within a facility approved outer container stored within the boundaries of an approved facility (i.e., a building designed and approved to store nuclear material). This packaging configuration, though occasionally deviated from in the past, represents the current procedurally enforced, expected containment structure for all interim nuclear material containment outside of an engineered control barrier (e.g., a glovebox). Over the life of containerization research at LANL, packaging engineers have become increasingly concerned with the degradation of the sPVC bag-out bag, which can cause corrosion to the outermost packaging layer as well as possible exposure to alpha contamination if the bag-out bag layer is degraded to the point of containment failure. A bag-out bag degradation predictive tool (BDT) is therefore needed to assess the current and future nuclear material inventory for possible bag degradation in order to continue to ensure safe operations for the facility and its workers to deliver on the vital national security mission of 30 PPY.

42 ENGINEERING↗

DOE OSTI2022

The WRF-Solar Ensemble Prediction System: Development, Test, and Validation

Providing reliable probabilistic solar radiation information is needed to improve management of the uncertainty and variability of solar generation. Thus, guidance on how to develop skillful and accurate ensemble forecasts is essential and it will ultimately contribute to integration of high amounts of solar energy on the grid. A team from the National Renewable Energy Laboratory and the National Center for Atmospheric Research had been collaborating to develop the WRF-Solar ensemble prediction system (WRF-Solar EPS) in the past three years to produce probabilistic solar irradiance forecasts and better predict solar energy by quantifying forecast uncertainty. The WRF-Solar EPS basically generates ensemble members for solar irradiance based on stochastic perturbations to provide intraday and day-ahead probabilistic forecasts. This study will present main research steps in developing the WRF-Solar EPS including: (a) tangent linear analysis for identifying key input variables of six WRF-Solar modules significantly related to predicting of cloud and solar irradiance, (b) combining stochastic perturbation technique with the WRF-Solar model, and (c) ensemble calibration method to decrease error and uncertainty of ensemble-based solar forecasts. The capability of WRF-Solar EPS is now updated to the most recent version of standard WRF model. This presentation will summarize comprehensive results from the evaluation of forecasts against the National Solar Radiation Data Base as well as ground-measured observations. Moreover, we will introduce the user's guide for WRF-Solar EPS (e.g., parameters to configure stochastic perturbations) and future extension of this research.

day-ahead forecast↗

DOE OSTI2020

Recent California tree mortality portends future increase in drought-driven forest die-off

Vegetation tolerance to drought depends on an array of site-specific environmental and plant physiological factors. This tolerance is poorly understood for many forest types despite its importance for predicting and managing vegetation stress. We analyzed the relationships between precipitation variability and forest die-off in California's Sierra Nevada and introduce a new measure of drought tolerance that emphasizes plant access to subsurface moisture buffers. We applied this metric to California's severe 2012-2015 drought, and show that it predicted the patterns of tree mortality. We then examined future climate scenarios, and found that the probability of droughts that lead to widespread die-off increases threefold by the end of the 21st century. Our analysis shows that tree mortality in the Sierra Nevada will likely accelerate in the coming decades and that forests in the Central and Northern Sierra Nevada that largely escaped mortality in 2012-2015 are vulnerable to die-off.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models: Preprint

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Lab based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Field surveyed data is critical to assess the performance of component lifetime. Using a documented field survey protocol, 13 field surveys where conducted. Each measurement is encoded with it's spatial location in respect to the other modules. By combining field survey data on degradation predictors with real time satellite weather data, data-driven predictive models of backsheet degradation were trained. LOESS models were constructed to investigate the spatial dependence of measurements. It was found that micro-climatic effects like treelines, ground surface changes, and elevation changes effected the magnitude and variance of the measurements. A GAM model was created to predict the value of degradation based on measured predictors. The model includes variables on the climate of the system and the location of each measurement in the PV mounting structure. The model performed well with an adj:R2 of 0:95 for yellowness index prediction. The model was cross-validated using k-folds.

backsheet↗

DOE OSTI2024

Winter precipitation predictability in Central Southwest Asia and its representation in seasonal forecast systems

In Central Southwest Asia (CSWA; 22°N to 40°N and 30°E to 70°E), winter (November to February) precipitation contributes up to 70% of the annual mean, but substantial interannual variations exist. Dynamical models exhibit subpar predictability in this region, but the limits of their skills are not well established. Here, we identify the tropical and extratropical forcings that explain ~75% of area-averaged seasonal variability in CSWA winter precipitation. Tropical forcing comes from the indirect El Niño-Southern Oscillation (ENSO) pathway, the leading mode of tropical Indian Ocean precipitation variability. This mode is coupled with ENSO-related Pacific Ocean sea surface temperature variability. A direct ENSO influence on CSWA does not extend beyond its Indian Ocean connection. Extratropical forcing comes from a large-scale mode of internal atmospheric variability. The spatial structure, variability of tropical forcing, and teleconnection with CSWA winter precipitation are skillfully depicted in two seasonal forecasting systems: the fifth-generation seasonal forecasting system (SEAS5) and Seamless System for Prediction and Earth System Research (SPEAR). Extratropical forcing’s spatial structure is also produced skillfully in the two modelling systems; however, the representation of its interannual variability and teleconnection requires improvement. While SEAS5 displays skill in representing extratropical forcing influence on CSWA winter precipitation and marginal skill in reproducing interannual variability, SPEAR has negligible ability in both areas. Consequently, these models have limited predictive skills over CSWA in winter. While improvements in representing extratropical forcing may be inherently limited as it arises from internal atmospheric variability, further research is needed to establish its predictability limits fully.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Preferential adsorption of noble gases in zeolitic tuff with variable saturation: A modeling study of counter-intuitive diffusive-adsorptive behavior

Noble gas transport through geologic media has important applications in the prediction and characterization of measured gas signatures related to underground nuclear explosions (UNEs). Retarding processes such as adsorption can cause significant species fractionation of radionuclide gases, which has implications for measured and predicted signatures used to distinguish radioxenon originating from civilian nuclear facilities or from UNEs. Accounting for the effects of variable water saturation in geologic media on tracer transport is one of the most challenging aspects of modeling gas transport because there is no unifying relationship for the associated tortuosity changes between different rock types, and reactive transport processes such as adsorption that are affected by the presence of water likewise behave differently between gas species. In this study, we perform numerical diffusive-adsorptive transport simulations to estimate gas transport parameters associated with bench-scale laboratory diffusion cell experiments measuring breakthrough in zeolitic and non-zeolitic rocks for a gaseous mixture of xenon, krypton, and SF 6 at varying degrees of water saturation (S w ). Counter-intuitive transport behavior was observed in the zeolitic rock experiments whereby breakthrough concentrations were significantly higher when the core was partially saturated (S w = 17 %) than under dry (S w = 0 %) conditions. Breakthrough of xenon was especially retarded in the dry core – likely due to comparatively high affinity of xenon for zeolitic adsorption sites – and estimated effective diffusion coefficients for all gases were approximately an order of magnitude lower than what is predicted by porosity-tortuosity models. We propose the counter-intuitive behavior observed is because water infiltration of zeolite nanopores reduces both the adsorptive capacity of the rock and the tortuosity of connected flow paths. We developed a two-site competitive kinetic Langmuir adsorption reaction for the porous media transport simulator in order to constrain transport parameters within zeolitic tuff, where differential adsorption to zeolite and non-zeolite pores was observed. We determined that liquid saturation-dependent diffusive-adsorptive transport is affected by subtle and at times competing processes that are specific to different gases, which have a significant overall influence on effective transport parameters.

58 GEOSCIENCES↗

NASA NTRS1982

Solar disturbances and mesospheric odd nitrogen

The odd nitrogen (NOx) abundance of the high latitude mesosphere exhibits a large variability in response to energetic particle precipitation. Model calculations for a very large particle event predict increases in the NOx mixing ratio that can exceed a factor of ten over time periods of the order of one week in the summer hemisphere. A more typical sized event can still lead to an enhancement of a factor of three in summer. Relative changes in the winter hemisphere are less dramatic due to a larger ambient NOx mixing ratio predicted here. However, a factor of two increase in NOx is possible during a very large event. Owing to the large variability, it is difficult to define a physically meaningful mean state profile in the high latitude mesosphere. Rather, the NOx abundance likely displays an erratic behavior which is correlated with the level of solar activity.

Orsini, N.↗

NASA NTRS1991

Micro-mechanical analysis of damage growth and fracture in discontinuous fiber reinforced metal matrix composites

An experimental verification is presented for a new two parameter fracture model based on the equivalent remote biaxial stresses (ERBS). A detailed comparison is made between the new theory and the constant K(sub IC) approach of linear elastic fracture mechanics (LEFM). Fracture is predicted through a failure curve representing the change in a variable fracture toughness K(sub IC) with the ERBS ratio B(sub E). The nonsingular term (T) in the series expansion of the near crack-tip transverse stress is included in the model. Experimental results for polymethyl methacrylate (PPMA) show that the theory can account for the effects of geometry on fracture toughness as well as indicate the initiation of crack branching. It is shown that the new criterion predicts failure for PMMA with a 95 percent confidence zone which is nearly three times smaller than that of the LEFM K(sub IC) approach.

Goree, James G.↗

DOE OSTI2024

Modes of Variability in E3SM and CESM Large Ensembles

An adequate characterization of internal modes of climate variability (MoV) is a prerequisite for both accurate seasonal predictions and climate change detection and attribution. Assessing the fidelity of climate models in simulating MoV is therefore essential; however, doing so is complicated by the large intrinsic variations in MoV and the limited span of the observational record. Large ensembles (LEs) provide a unique opportunity to assess model fidelity in simulating MoV and quantify intermodel contrasts. Here, in this work, these goals are pursued in four recently produced LEs: the Energy Exascale Earth System Model (E3SM) versions 1 and 2 LEs, and the Community Earth System Model (CESM) versions 1 and 2 LEs. In general, the representation of global coupled modes is found to improve across successive E3SM and CESM versions in conjunction with the fidelity of the base state climate while the patterns of extratropical modes are well simulated across the ensembles. Various persistent shortcomings for all MoV are however identified and discussed. The results both demonstrate the successes of these recent model versions and suggest the potential for continued improvement in the representation of MoV with advances in model physics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗

NASA NTRS2003

The Madden-Julian Oscillation and its Impact on Northern Hemisphere Weather Predictability during Wintertime

The Madden-Julian Oscillation (MJO) is known as the dominant mode of tropical intraseasonal variability and has an important role in the coupled-atmosphere system. This study used twin numerical model experiments to investigate the influence of the MJO activity on weather predictability in the midlatitudes of the Northern Hemisphere during boreal winter. The National Aeronautics and Space Administration (NASA) Goddard laboratory for the Atmospheres (GLA) general circulation model was first used in a 10-yr simulation with fixed climatological SSTs to generate a validation data set as well as to select initial conditions for active MJO periods and Null cases. Two perturbation numerical experiments were performed for the 75 cases selected [(4 MJO phases + Null phase) _ 15 initial conditions in each]. For each alternative initial condition, the model was integrated for 90 days. Mean anomaly correlations in the midlatitudes of the Northern Hemisphere (2O deg N_60 deg.N) and standardized root-mean-square errors were computed to validate forecasts and control run. The analyses of 500-hPa geopotential height, 200-hPa Streamfunction and 850-hPa zonal wind component systematically show larger predictability during periods of active MJO as opposed to quiescent episodes of the oscillation.

Jones, Charles↗

NASA NTRS1997

Comparative Results from a CFD Challenge Over a 2D Three-Element High-Lift Airfoil

A high-lift workshop was held in May of 1993 at NASA Langley Research Center. A major part of the workshop centered on a blind test of various computational fluid dynamics (CFD) methods in which the flow about a two- dimensional (2D) three-element airfoil was computed without prior knowledge of the experimental data. The results of this 'blind' test revealed: (1) The Reynolds Averaged Navier-Stokes (RANS) methods generally showed less variability among codes than did potential/Euler solvers coupled with boundary-layer solution techniques. However, some of the coupled methods still provided excellent predictions. (2) Drag prediction using coupled methods agreed more closely with experiment than the RANS methods. Lift was more accurately predicted than drag for both methods. (3) The CFD methods did well in predicting lift and drag changes due to changes in Reynolds number, however, they did not perform as well when predicting lift and drag increments due to changing flap gap, (4) Pressures and skin friction compared favorably with experiment for most of the codes. (5) There was a large variability in most of the velocity profile predictions. Computational results predict a stronger siat wake than measured suggesting a missing component in turbulence modeling, perhaps curvature effects.

Klausmeyer, Steven M.↗

DOE OSTI2022

Disruption of the tagF Orthologue in the epa Locus Variable Region of Enterococcus faecalis Causes Cell Surface Changes and Suppresses an eep -Dependent Lysozyme Resistance Phenotype

The disease-producing capacity of the opportunistic pathogen Enterococcus faecalis is enhanced by the ability of the bacterium to evade killing by antimicrobial agents. Survival of E. faecalis in the presence of the human antimicrobial enzyme lysozyme is mediated in part by the site 2 metalloprotease Eep; however, a complete model of enterococcal lysozyme resistance has not been elucidated. To better understand the molecular basis for lysozyme resistance in E. faecalis, we analyzed Δeep suppressor mutants that acquire resistance to lysozyme through mutation of the gene OG1RF_11713, a predicted teichoic acid biosynthesis-encoding gene located within the variable region of the enterococcal polysaccharide antigen (epa) locus. Sequence comparisons revealed that OG1RF_11713 is most similar to the cytidine-5'-diphosphate (CDP)-glycerol:poly-(glycerolphosphate)glycerophosphotransferase TagF from Staphylococcus epidermidis. Inactivation of OG1RF_11713 in both the wild-type and Δeep genetic backgrounds was sufficient to increase the resistance of E. faecalis OG1RF to lysozyme. Minimal amounts of N-acetylgalactosamine were detectable in cell wall carbohydrate extracts of OG1RF_11713 deletion mutants, and this was associated with a reduction in negative cell surface charge. Targeted disruption of OG1RF_11713 was also associated with increased susceptibility to the antibiotic polymyxin B and membrane-targeting detergents and decreased susceptibility to the lantibiotic nisin. Furthermore, this work implicates OG1RF_11713 as a major determinant of cell envelope integrity and provides further validation that lysozyme resistance is intrinsically linked to the modification of enterococcal cell wall polysaccharides.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2016

Fatigue Analysis of Notched Laminates: A Time-Efficient Macro-Mechanical Approach

A coupled transversely isotropic deformation and damage fatigue model is implemented within the finite element method and was utilized along with a static progressive damage model to predict the fatigue life, stiffness degradation as a function of number of cycles, and post-fatigue tension and compression response of notched, multidirectional laminates. Initially, the material parameters for the fatigue model were obtained utilizing micromechanics simulations and the provided [0], [90] and [plus or minus 45] experimental composite laminate S-N (stress-cycle) data. Within the fatigue damage model, the transverse and shear properties of the plies were degraded with an isotropic scalar damage variable. The damage in the longitudinal (fiber) ply direction was suppressed, and only the strength of the fiber was degraded as a function of fatigue cycles. A maximum strain criterion was used to capture the failure in each element, and once this criterion was satisfied, the longitudinal stiffness of the element was decreased by a factor of 10 (sup 4). The resulting, degraded properties were then used to calculate the new stress state. This procedure was repeated until final failure of the composite laminate was achieved or a specified number of cycles reached. For post-fatigue tension and compression behavior, four internal state variables were used to control the damage and failure. The predictive capability of the above-mentioned approach was assessed by performing blind predictions of the notched multidirectional IM7/977-3 composite laminates response under fatigue and post-fatigue tensile and compressive loading, followed by a recalibration phase. Although three different multidirectional laminates were analyzed in the course of this study, only detailed results (i.e., stiffness degradation and post-fatigue stress-train curves as well as damage evolution states for a single laminate ([30/60/90/minus 30/minus 60] (sub 2s)) are discussed in detail here.

Composite Fatigue analysis↗

NASA NTRS1990

A model for predicting damage induced fatigue life of laminated composite structural components

This paper presents a model for predicting the life of laminated composite structural components subjected to fatigue induced microstructural damage. The model uses the concept of continuum damage mechanics, wherein the effects of microcracks are incorporated into a damage dependent lamination theory instead of treating each crack as an internal boundary. Internal variables are formulated to account for the effects of both matrix cracks and internal delaminations. Evolution laws for determining the damage variables as functions of ply stresses are proposed, and comparisons of predicted damage evolution are made to experiment. In addition, predicted stiffness losses, as well as ply stresses are shown as functions of damage state for a variety of stacking sequences.

Allen, David H.↗

DOE OSTI2023

Memory-based parameterization with differentiable solver: Application to Lorenz ’96

Physical parameterizations (or closures) are used as representations of unresolved subgrid processes within weather and global climate models or coarse-scale turbulent models, whose resolutions are too coarse to resolve small-scale processes. These parameterizations are typically grounded on physically based, yet empirical, representations of the underlying small-scale processes. Machine learning-based parameterizations have recently been proposed as an alternative solution and have shown great promise to reduce uncertainties associated with the parameterization of small-scale processes. Yet, those approaches still show some important mismatches that are often attributed to the stochasticity of the considered process. This stochasticity can be due to coarse temporal resolution, unresolved variables, or simply to the inherent chaotic nature of the process. To address these issues, we propose a new type of parameterization (closure), which is built using memory-based neural networks, to account for the non-instantaneous response of the closure and to enhance its stability and prediction accuracy. We apply the proposed memory-based parameterization, with differentiable solver, to the Lorenz ’96 model in the presence of a coarse temporal resolution and show its capacity to predict skillful forecasts over a long time horizon of the resolved variables compared to instantaneous parameterizations. This approach paves the way for the use of memory-based parameterizations for closure problems.

Mathematics↗

DOE OSTI2025

The molten salt tritium transport experiment: A pumped fluoride salt loop for hydrogen isotope experimentation

Molten salt reactors (MSRs) and fusion reactors propose to use molten salts as coolants and breeder blanket materials, respectively. Tritium, however, poses safety concerns in both reactor types due to its ability to permeate through reactor materials and potential for environmental release. This manuscript addresses the tritium transport phenomena in molten salts and presents the design and analysis of the Molten Salt Tritium Transport Experiment (MSTTE). MSTTE is a forced-convection fluoride salt loop intended to measure hydrogen isotope permeation through structural materials in a flowing salt system. In the first phase, MSTTE will use FLiNaK salt and deuterium as surrogates for FLiBe and tritium, with future plans to utilize tritium and FLiBe. MSTTE couples a Copenhagen Atomics pumped salt loop with an external test section that introduces hydrogen isotopes into the loop and measures transport phenomena. The Hydrogen Injection System (HIS) controls hydrogen isotope introduction into the molten salt loop. Here, the permeation test section measures the permeation rate through stainless steel tubing in contact with flowing salt. Computational fluid dynamics (CFD) analysis ensures fully developed salt flow in the permeation test section. MSTTE is modeled with MELCOR-TMAP to predict the permeation rate as a function of experimental variables such as source term, salt flow rate, and salt temperature. Results indicate that the source term is the only parameter with a significant effect on the permeation rate. Pressure drop analysis suggests that the loop should operate below 200 LPM to maintain a pressure drop below 200 kPa. Additionally, finite-element analysis assesses thermal stress during loop operation to ensure the experiment's safe design. MSTTE will provide semi-integral data on tritium transport phenomena in molten salts and serve as a testbed for advancing molten salt technology.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗