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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Quantifying the impacts of land surface schemes and dynamic vegetation on the model dependency of projected changes in surface energy and water budgets

Assessing and quantifying the uncertainties in projected future changes of energy and water budgets over land surface are important steps toward improving our confidence in climate change projections. In our study, the contribution of land surface models to the inter-GCM variation of projected future changes in land surface energy and water fluxes are assessed based on output from 19 global climate models (GCMs) and offline Community Land Model version 4 (CLM4) simulations driven by meteorological forcing from the 19 GCMs. Similar offline simulations using CLM4 with its dynamic vegetation submodel are also conducted to investigate how dynamic vegetation feedback, a process that is being added to more earth system models, may amplify or moderate the intermodel variations of projected future changes. Projected changes are quantified as the difference between the 2081–2100 period from the Representative Concentration Pathway 8.5 (RCP8.5) future experiment and the 1981–2000 period from the historical simulation. Under RCP8.5, projected changes in surface water and heat fluxes show a high degree of model dependency across the globe. Although precipitation is very likely to increase in the high latitudes of the Northern Hemisphere, a high degree of model-related uncertainty exists for evapotranspiration, soil water content, and surface runoff, suggesting discrepancy among land surface models (LSMs) in simulating the surface hydrological processes and snow-related processes. Large model-related uncertainties for the surface water budget also exist in the Tropics including southeastern South America and Central Africa. Moreover, these uncertainties would be reduced in the hypothetical scenario of a single near-perfect land surface model being used across all GCMs, suggesting the potential to reduce uncertainties through the use of more consistent approaches toward land surface model development. Under such a scenario, the most significant reduction is likely to be seen in the Northern Hemisphere high latitudes. Including representation of vegetation dynamics is expected to further amplify the model-related uncertainties in projected future changes in surface water and heat fluxes as well as soil moisture content. This is especially the case in the high latitudes of the Northern Hemisphere (e.g., northwestern North America and central North Asia) where the projected vegetation changes are uncertain and in the Tropics (e.g., the Amazon and Congo Basins) where dense vegetation exists. Finally, findings from this study highlight the importance of improving land surface model parameterizations related to soil and snow processes, as well as the importance of improving the accuracy of dynamic vegetation models.

58 GEOSCIENCES↗
DOE OSTI2018

Comparison of Daytime Low-Level Cloud Properties Derived From GOES and ARM SGP Measurements

Large-scale satellite data are critical for both verifying and improving general circulation model parameterizations of clouds and radiation for climate prediction. For reliable application of satellite data sets in cloud processes and climate models, it is important to have a reasonable estimate of the errors in the derived cloud properties. The daytime single-layered low-level cloud properties retrieved by the Geostationary Operational Environmental Satellite system (GOES) are compared with ground-based observations and retrievals over the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility from June 1998 to December 2006. The GOES retrievals are made via the Visible-Infrared Solar-infrared Split-window Technique. They are spatially averaged within a 0.15° × 0.15° box centered on the ARM SGP site, and the ARM surface observations are temporally averaged ±15 min around the GOES scans to produce collocated pairs. Comparisons are made for monthly means, diurnal means, and one-to-one GOES and ARM collocated pairs. GOES T eff is highly correlated with ARM T top cloud temperature, having an R 2 value of 0.75, though GOES exhibits a cold bias. GOES-retrieved τ and liquid water path have very good agreement with ARM retrievals with R 2 s of 0.45 and 0.47, while r e (GOES), on average, is about 2 μm greater than ARM r e . An examination of solar and viewing geometry has shown that GOES-retrieved mean r e and τ values are impacted by solar zenith angle and especially scattering angle, which is not unexpected and needs to be accounted for by users.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

CMIP5 Climate Models Overestimate Cooling by Volcanic Aerosols

We compare projections of the observed hemispherical mean surface temperature (HadCRUT4.6.0.0) and the ensemble mean of CMIP5 climate models' simulations on a set of standard regression model forcing variables. We find that the volcanic aerosol regression coefficients of the CMIP5 simulations are consistently significantly larger (by 40–49%) than the volcanic aerosol coefficients of the observed temperature. The probability that the observed differences are caused just by chance is much less than 0.01. The overestimate is due to the climate models' response to volcanic aerosol radiative forcing. The largest overestimate occurs in the winter season of each hemisphere. We hypothesize that the models' parameterization of aerosol-cloud interactions within ice and mixed phase clouds is a likely source of this discrepancy. Furthermore, the models significantly underestimate the effect of solar variability on temperature for both hemispheres.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Effects of Assumed Source Depth and Shear-Wave Velocity on Moment Tensors Estimated for Small, Contained Chemical Explosions in Granite

ABSTRACT The Source Phenomenology Experiment (SPE-Arizona) included of a series of chemical explosions detonated within a copper mine in Arizona. This study focuses on ground motions from detonations in the copper mine, which are analyzed to assess the uniqueness of the resulting source representation when the source region propagation characteristics have a range of possible models. P-wave velocities are well constrained by refraction data with less constraint of the S-wave velocities. The effects of explosion source depth and VS are assessed with Green’s functions for a range of models in which VP is held constant. Propagation models with a Poisson’s value of 0.25 and a source depth 30–60 m most accurately replicate the data. The explosion was detonated at a centroid depth of 30 m, so trade-offs in depth are demonstrated. The compensated linear vector dipole and explosion components of the Green’s functions convolved with a Mueller–Murphy source function are compared. Both produce significant energy in the 2–12 Hz band, due to surface-wave contributions with no clear depth dependencies above 20 Hz. The range of propagation models is used with the observational data to invert for the frequency-domain moment tensor. Fits to the data from these inversions have cross-correlation values of 0.64, demonstrating effectiveness in replicating the observations with the assumed propagation path effects and resulting source function. Inversions produce horizontal dipoles (Mxx and Myy), roughly half the maximum amplitude of Mzz, consistent with a compensated linear vector dipole source, which is frequency dependent. Denny and Johnson, Mueller–Murphy, Walter and Ford, and the revised Mueller–Murphy source models, parameterized for granite, are compared to the moment tensors. Despite a nonisotropic moment tensor source, the revised Mueller–Murphy isotropic source model best replicates the long-period moments, overshoot, and corner frequency.

Geochemistry & Geophysics↗
DOE OSTI2016

GeoTess: A generalized Earth model software utility

GeoTess is a model parameterization and software support library that manages the construction, population, storage, and interrogation of data stored in 2D and 3D Earth models. Here, the software is available in Java and C++, with a C interface to the C++ library.

58 GEOSCIENCES↗
DOE OSTI2015

Experiments to Populate and Validate a Processing Model for Polyurethane Foam: Additional Data for Structural Foams

We are developing computational models to help understand manufacturing processes, final properties and aging of structural foam, polyurethane PMDI. Th e resulting model predictions of density and cure gradients from the manufacturing process will be used as input to foam heat transfer and mechanical models. BKC 44306 PMDI-10 and BKC 44307 PMDI-18 are the most prevalent foams used in structural parts. Experiments needed to parameterize models of the reaction kinetics and the equations of motion during the foam blowing stages were described for BKC 44306 PMDI-10 in the first of this report series (Mondy et al. 2014). BKC 44307 PMDI-18 is a new foam that will be used to make relatively dense structural supports via over packing. It uses a different catalyst than those in the BKC 44306 family of foams; hence, we expect that the reaction kineti cs models must be modified. Here we detail the experiments needed to characteriz e the reaction kinetics of BKC 44307 PMDI-18 and suggest parameters for the model based on these experiments. In additi on, the second part of this report describes data taken to provide input to the preliminary nonlinear visco elastic structural response model developed for BKC 44306 PMDI-10 foam. We show that the standard cu re schedule used by KCP does not fully cure the material, and, upon temperature elevation above 150°C, oxidation or decomposition reactions occur that alter the composition of the foam. These findings suggest that achieving a fully cured foam part with this formulation may be not be possible through therma l curing. As such, visco elastic characterization procedures developed for curing thermosets can provide only approximate material properties, since the state of the material continuously evolves during tests.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Scientific Case for Concurrent Neutron and X-ray Scattering and Spectroscopy

The interrogation of materials with X-rays or neutrons to determine the structure, energetics, and dynamics of materials is fundamental to advancing materials' physical and chemical science and developing innovative material technologies. A transcending challenge in developing novel materials is that progress hinges on understanding the structure and dynamics across multiple time and length scales in complex materials that feature multiple components, interfaces, and compositions. Despite the ever-growing demands on materials’ characterization, existing approaches are almost exclusively based on isolated X-ray or neutron scattering, i.e., an approach commensurate with the more narrowly defined needs of fifty years ago. A three-day workshop sponsored by the U.S. National Science Foundation (NSF) analyzed the demand for concurrent neutron and X-ray (NeX) experiments. It was held at the Spring Hill Suites, San Jose, California, from June 2 to 4, 2022. In this workshop, 70 national and international experts ascertained the crucial need to establish NeX capabilities to advance the science of complex materials and systems in the US. Here, we illustrate the need for NeX scattering and spectroscopy experiments by showcasing examples that span areas as diverse as biomaterials, energy science, soft matter, and nanomaterials. To provide NeX capability will require new instrumentation that enables concurrent experiments. Affected areas include chemistry, soft matter, quantum materials, pure and applied chemistry, bioscience, geoscience, and applied materials. NeX benefits research outcomes due to the complementarity of the two techniques, which is essential for better model refinement. While joint refinement of data from separate neutron and X-ray experiments is critical to avoid ambiguities, especially in multiphase-multicomponent materials, concurrent experiments overcome scientific and technical barriers associated with single measurements, separated by location and, thus, time. Among all the examples, these factors introduce uncertainties in the results that complicate data analysis. [1,2] [3] While models are strongly sample-dependent, the principles of joint refinement are generally applicable to these disciplines, including the development of advanced parameterization, modeling, and analysis techniques that also consider the temporal and spatial resolutions of the two methods, leading to unambiguous data interpretation. Solutions for technical barriers must be found to realize NeX experiments, including developing robust sample environments that meet the optical requirements of neutrons and X-rays.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Precision analysis of the photomultiplier response to ultra low signals

Here, a new computational model for the description of the photon detector response functions measured in conditions of low light is presented, together with examples of the observed photomultiplier signal amplitude distributions, successfully described using the parameterized model equation. In extension to the previously known approximations, the new model describes the underlying discrete statistical behavior of the photoelectron cascade multiplication processes in photon detectors with complex non-uniform gain structure of the first dynode. Important features of the model include the ability to represent the true single-photoelectron spectra from different photomultipliers with a variety of parameterized shapes, reflecting the variability in the design and in the individual parameters of the detectors. The new software tool is available for evaluation of the detectors’ performance, response, and efficiency parameters that may be used in various applications including the ultra low background experiments such as the searches for Dark Matter and rare decays, underground neutrino studies, optimizing operations of the Cherenkov light detectors, help in the detector selection procedures, and in the experiment simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Experimental and numerical investigations of beryllium strength models using the Rayleigh-Taylor instability

We present a set of high explosive driven Rayleigh-Taylor strength experiments for beryllium to produce data to distinguish predictions by various strength models. Design simulations using existing strength model parameterizations from Steinberg-Lund and Preston-Tonks-Wallace (PTW) suggested an optimal design that would delineate between not just different strength models, but different parameters sets of the PTW model. Application of the models to the post-shot results, however, suggests growth consistent with little material strength. We focus mostly on efforts to simulate the data using published strength models as well as the more recent RING relaxation model developed at VNIIEF. The results of the strength experiments indicate weak influence of strength in mitigating the growth with the RING model coming closest to predicting the material behavior. Finally, we present shock and ramp-loading recovery experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

The Model of Decomposition of a Fe–Cu Alloy with Concentration-Depending Interatomic Interactions

The thermodynamics and kinetics of decomposition of a Fe–Cu alloy are investigated in the context of a simple ab initio parameterization model taking into account the concentration dependence of the magnetic contribution to the free energy. It is shown that taking into account a difference between the short-range and long-range magnetic orders near the Curie temperature is very important for calculating the solubility of copper in iron. The solubility of copper in bcc iron and the stability limit of a homogeneous state (physical spinodal) are evaluated via the kinetic Monte Carlo method. The impact of structural defects (dislocations and grain boundaries) on these curves, as well as on the kinetic time–temperature—transformation (TTT) diagram, is also discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Statistical uncertainty of eddy covariance CO 2 fluxes inferred using a residual bootstrap approach

High-frequency eddy-covariance measurements of net ecosystem CO 2 exchange (NEE) with the atmosphere are valuable resources for model parameterization, calibration, and validation. However, uncertainties in measured data, i.e., data gaps and inherent random errors, create problems for researchers attempting to quantify uncertainties in model projections of terrestrial ecosystem carbon cycling. Here, we demonstrate that a model-data fusion method (residual bootstrap) produces defensible annual NEE sums, through mimicking the behavior of random errors, filling missing values, and simulating gap-filling biases. This study estimated annual NEE sums for 53 site-years based on nine eddy-covariance tower sites in the USA, and found that our annual estimates were, in most cases, comparable in magnitude with those obtained from AmeriFlux gap-filled data. Additionally, compared to the AmeriFlux standardized gap-filling, our approach provides better NEE estimates for moderate to longer, and more frequent, data gaps. Annual accumulated uncertainties in NEE at the 95% confidence level were ±30gCm -2 year -1 for evergreen needleleaf forests; ±60gCm -2 year -1 for deciduous broadleaf forests; and ±80gCm -2 year -1 for croplands. The residual bootstrap performed worst when gap length was greater than one month or data exclusion greater than 90% during the growing season, common to other gap-filling techniques. However, this study produced robust results for most site years when monthly data coverage during the growing season is not extremely low. We therefore suggest that the inclusion of NEE uncertainty estimates and better estimation for moderate to longer, and more frequent, data gaps as provided by the residual bootstrap approach can be beneficial for ecosystem model evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Use of Combined Biogeochemical Model Approaches and Empirical Data to Assess Critical Loads of Nitrogen

Empirical and dynamic biogeochemical modelling are complementary approaches for determining the critical load (CL) of atmospheric nitrogen (N) or other constituent deposition that an ecosystem can tolerate without causing ecological harm. The greatest benefits are obtained when these approaches are used in combination. Confounding environmental factors can complicate the determination of empirical CLs across depositional gradients, while the experimental application of N amendments for estimating the CL does not realistically mimic the effects of chronic atmospheric N deposition. Biogeochemical and vegetation simulation models can provide CL estimates and valuable ecosystem response information, allowing for past and future scenario testing with various combinations of environmental factors, pollutants, pollutant control options, land management, and ecosystem response parameters. Even so, models are fundamentally gross simplifications of the real ecosystems they attempt to simulate. Empirical approaches are vital as a check on simulations and CL estimates, to parameterize models, and to elucidate mechanisms and responses under real world conditions. In this chapter, we provide examples of empirical and modelled N CL approaches in ecosystems from three regions of the United States: mixed conifer forest, desert scrub and pinyon- juniper woodland in California; alpine catchments in the Rocky Mountains; and lakes in the Adirondack region of New York state.

Fenn, Mark E.↗
DOE OSTI2025

CONCURRENT, CONDENSED STEIN VARIATIONAL GRADIENT DESCENT FOR UNCERTAINTY QUANTIFICATION OF NEURAL NETWORKS

In this work, we propose a Stein variational gradient descent (SVGD) method to concurrently sparsify, train, and provide uncertainty quantification (UQ) of a complexly parameterized model, such as a neural network (NN). It employs a graph reconciliation and condensation process to reduce complexity and increase similarity in the Stein ensemble of parameterizations. Therefore, the proposed concurrent, condensed SVGD (ccSVGD) method can provide UQ on parameters, not just outputs. Furthermore, the parameter reduction speeds up the convergence of the Stein gradient descent as it reduces the combinatorial complexity by aligning and differentiating the sensitivity to parameters. These properties are demonstrated with an illustrative example and an application to a mechanical response representation problem in solid mechanics.

42 ENGINEERING↗
DOE OSTI2021

Ocean Surface Flux Algorithm Effects on Earth System Model Energy and Water Cycles

Earth system models parameterize ocean surface fluxes of heat, moisture, and momentum with empirical bulk flux algorithms, which introduce biases and uncertainties into simulations. We investigate the atmosphere and ocean model sensitivity to algorithm choice in the Energy Exascale Earth System Model (E3SM). Flux differences between algorithms are larger in atmosphere simulations (where wind speeds can vary) than ocean simulations (where wind speeds are fixed by forcing data). Surface flux changes lead to global scale changes in the energy and water cycles, notably including ocean heat uptake and global mean precipitation rates. Compared to the control algorithm, both COARE and University of Arizona (UA) algorithms reduce global mean precipitation and top of atmosphere radiative biases. Further, UA may slightly reduce biases in ocean meridional heat transport. We speculate that changes seen here, especially in the ocean, could be even larger in coupled simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2015

LES ARM Symbiotic Simulation and Observation (LASSO) Implementation Strategy

This document illustrates the design of the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) workflow to provide a routine, high-resolution modeling capability to augment the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s high-density observations. LASSO will create a powerful new capability for furthering ARM’s mission to advance understanding of cloud, radiation, aerosol, and land-surface processes. The combined observational and modeling elements will enable a new level of scientific inquiry by connecting processes and context to observations and providing needed statistics for details that cannot be measured. The result will be improved process understanding that facilitates concomitant improvements in climate model parameterizations. The initial LASSO implementation will be for ARM’s Southern Great Plains site in Oklahoma and will focus on shallow convection, which is poorly simulated by climate models due in part to clouds’ typically small spatial scale compared to model grid spacing, and because the convection involves complicated interactions of microphysical and boundary layer processes.

Gustafson Jr., WI↗
DOE OSTI2022

An evaluation of the E3SMv1 Arctic ocean and sea-ice regionally refined model

The Energy Exascale Earth System Model (E3SM) is a state-of-the-science Earth system model (ESM) with the ability to focus horizontal resolution of its multiple components in specific areas. Regionally refined global ESMs are motivated by the need to explicitly resolve, rather than parameterize, relevant physics within the regions of refined resolution, while offering significant computational cost savings relative to the respective cost of configurations with high-resolution (HR) everywhere on the globe. In this paper, we document results from the first Arctic regionally refined E3SM configuration for the ocean and sea-ice components (E3SM-Arctic-OSI), while employing data-based atmosphere, land, and hydrology components. Our aim is an improved representation of the Arctic coupled ocean and sea-ice state, its variability and trends, and the exchanges of mass and property fluxes between the Arctic and the sub-Arctic. We find that E3SM-Arctic-OSI increases the realism of simulated Arctic ocean and sea-ice conditions compared to a similar low-resolution E3SM simulation without the Arctic regional refinement in ocean and sea-ice components (E3SM-LR-OSI). In particular, exchanges through the main Arctic gateways are greatly improved with respect to E3SM-LR-OSI. Other aspects, such as the Arctic freshwater content variability and sea-ice trends, are also satisfactorily simulated. Yet, other features, such as the upper-ocean stratification and the sea-ice thickness distribution, need further improvements, involving either more advanced parameterizations, model tuning, or additional grid refinements. Overall, E3SM-Arctic-OSI offers an improved representation of the Arctic system relative to E3SM-LR-OSI, at a fraction (15 %) of the computational cost of comparable global high-resolution configurations, while permitting exchanges with the lower-latitude oceans that cannot be directly accounted for in Arctic regional models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Cloud-Based Model Calibration Using OpenStudio: Preprint

OpenStudio is a free, open source Software Development Kit (SDK) and application suite for performing building energy modeling and analysis. The OpenStudio Parametric Analysis Tool has been extended to allow cloud-based simulation of multiple OpenStudio models parametrically related to a baseline model. This paper describes the new cloud-based simulation functionality and presents a model cali-bration case study. Calibration is initiated by entering actual monthly utility bill data into the baseline model. Multiple parameters are then varied over multiple iterations to reduce the difference between actual energy consumption and model simulation results, as calculated and visualized by billing period and by fuel type. Simulations are per-formed in parallel using the Amazon Elastic Cloud service. This paper highlights model parameterizations (measures) used for calibration, but the same multi-nodal computing architecture is available for other purposes, for example, recommending combinations of retrofit energy saving measures using the calibrated model as the new baseline.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗