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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 631 records · Page 35

Collaborative Research: Towards Advanced Understanding and Predictive Capability of Climate Change in the Arctic Using a High-Resolution Regional Arctic Climate Model

The primary research task completed for this project was the development of the Regional Arctic Climate Model (RACM). This involved coupling existing atmosphere, ocean, sea ice, and land models using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM) coupler (CPL7). RACM is based on the Weather Research and Forecasting (WRF) atmospheric model, the Parallel Ocean Program (POP) ocean model, the CICE sea ice model, and the Variable Infiltration Capacity (VIC) land model. A secondary research task for this project was testing and evaluation of WRF for climate-scale simulations on the large pan-Arctic model domain used in RACM. This involved identification of a preferred set of model physical parameterizations for use in our coupled RACM simulations and documenting any atmospheric biases present in RACM.

54 ENVIRONMENTAL SCIENCES↗

Final Report to the U.S. Department of Energy for studies of Evaluation of Turbulence Parameterizations for Cloud-Resolving Models

The intermediately-prognostic higher-order turbulence closure (IPHOC) introduces a joint double-Gaussian distribution of liquid water potential temperature (θ l ), total water mixing ratio (q t ), and vertical velocity (w ) to represent any skewed turbulence circulations .The distribution is inferred from the first-, second-, and third-order moments of the variables given above, and is used to diagnose cloud fraction and grid-mean liquid water mixing ratio, as well as the buoyancy and fourth-order terms in the equations describing the evolution of the second- and third-order moments. Only three third-order moments (those of θ l , q t , and w ) are predicted in the IPHOC.

99 GENERAL AND MISCELLANEOUS↗

ACAPEX – Ship-Based Ice Nuclei Collections Field Campaign Report

Measurements were sought to evaluate a hypotheses that sea-spray-sourced ice-nucleating particles (INPs) are of biological origin and represent a distinctly different INP population in comparison to long-range-transported desert or urban and regional land-sourced INP, and that the layering of marine within other aerosol layers feeding orographic storms over the mountains of California and the Western United States thereby leads to common and quantifiable scenarios that influence precipitation over the region. Aerosol collections on the National Oceanic and Atmospheric Administration (NOAA) research vessel (RV) Ronald H. Brown, for subsequent processing of INP immersion freezing activation temperature spectra and composition analyses, added a valuable measurement component to the ARM Cloud Aerosol Precipitation Experiment (ACAPEX) and related CalWater2 (NOAA) studies for use in parameterizing and modeling the impacts of marine boundary layer and other aerosols on climate and radiation via aerosol indirect effects on mixed-phase clouds. Twenty-five nominally 24-hour collections were made and have been processed for immersion freezing INP number concentrations versus temperature in the mixed-phase cloud temperature regime from -10 to -27°C. The similarity of INP number concentrations compared to typical marine boundary layer values attributed to sea-spray aerosols was noted. Nevertheless, variability of INP concentrations of up to 50 times was noted at individual temperatures over the course of the study. A particular analysis possible with this data set is to examine INP budgets over oceans inside versus outside of atmospheric river conditions. These INP measurements supplemented multiple airborne INP measurements on the ARM Aerial Facility (AAF), and others on the ground during ACAPEX and CalWater2, to provide extensive spatial and temporal analyses of INP immersion freezing spectra during winter storm periods. Future analyses will use thermal sensitivity to examine INP compositions as organic versus inorganic in these marine boundary layer samples. Data set integration is occurring under funding from an Atmospheric System Research (ASR) proposal.

54 ENVIRONMENTAL SCIENCES↗

Coincident Aerosol and H2O Retrievals versus HSI Imager Field Campaign ReportH2O Retrievals versus HSI Imager Field Campaign Report

Two spectrally calibrated tarpaulins (tarps) were co-located at a fixed Global Positioning System (GPS) position on the gravel antenna field at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains (SGP) site. Their placement was timed to coincide with the overflight of a new hyperspectral imaging satellite. The intention was to provide an analysis of the data obtained, including the measured and retrieved spectral albedos for the calibration tarps. Subsequently, a full suite of retrieved values of H2O column, and the aerosol overburden, were to be compared to those determined by alternate SGP ground truth assets. To the extent possible, the down-looking cloud images would be assessed against the all-sky images. Because cloud contamination above a certain level precludes the inversion processing of the satellite data, coupled with infrequent targeting opportunities, clear-sky conditions were imposed. The SGP site was chosen not only as a target of opportunity for satellite validation, but as perhaps the best coincident field measurement site, as established by DOE’s ARM Facility. The satellite team had every expectation of using the information obtained from the SGP to improve the inversion products for all subsequent satellite images, including the cloud and radiative models and parameterizations and, thereby, the performance assessment for subsequent and historic image collections. Coordinating with the SGP onsite team, four visits, all in 2009, to the Central Facility occurred: • June 6-8 (successful exploratory visit to plan tarp placements, etc.) • July 18-24 (canceled because of forecast for heavy clouds) • Sep 9-12 (ground tarps placed, onset of clouds) • Nov 7-9 (visit ultimately canceled because of weather predictions). As noted, in each instance, any significant overcast prediction precluded image collection from the satellite. Given the long task-scheduling procedures (which were in place for each time period), coupled with other priorities and the satellite lifetime, no alternate SGP images could be obtained.

54 ENVIRONMENTAL SCIENCES↗

Investigating Marine Boundary Layer Parameterizations by Combining Observations with Models via State Estimation

In this project we have improved numerical weather prediction analyses and forecasts of low level winds in the marine boundary layer. This has been accomplished with the following tools; The National Center for Atmospheric Research (NCAR) Weather and Research Forecasting model, WRF, both in his single column (SCM) and three-dimensional (3D) versions; The National Oceanic and Atmospheric Administration (NOAA) Wave Watch III (WWIII); SE algorithms from the Data Assimilation Research Testbed (DART, Anderson et al. 2009); and Observations of key quantities of the lower MBL, including temperature and winds at multiple levels above the sea surface. The experiments with the WRF SCM / DART system have lead to large improvements with respect to a standard WRF configuration, which is currently commonly used by the wind energy industry. The single column model appears to be a tool particularly suitable for off-shore wind energy applications given its accuracy, the ability to quantify uncertainty, and the minimal computational resource requirements. In situations where the impact of an upwind wind park may be of interest in a downwind location, a 3D approach may be more suitable. We have demonstrated that with the WRF 3D / DART system the accuracy of wind predictions (and other meteorological parameters) can be improved over a 3D computational domain, and not only at specific locations. All the scripting systems developed in this project (i.e., to run WRF SCM / DART, WRF 3D / DART, and the coupling between WRF and WWIII) and the several modifications and upgrades made to the WRF SCM model will be shared with the broader community.

17 WIND ENERGY↗

Hydrogen Adsorbents with High Volumetric Density: New Materials and System Projections

A high-capacity, low-cost method for storing hydrogen remains one of the primary barriers to the widespread commercialization of fuel cell vehicles. Storage via adsorption is a promising approach due to its fast kinetics, reversibility, and high gravimetric densities. An unfortunate characteristic of adsorptive storage is that high gravimetric densities typically come at the expense of volumetric density (total basis). The goal of this project is to demonstrate best-in-class adsorbents based on metal-organic frameworks (MOFs) that achieve high volumetric and gravimetric H 2 densities simultaneously, while maintaining reversibility and fast kinetics. This is accomplished using a combination ion of high-throughput computational screening and experimental synthesis and characterization. The performance of the most promising materials are projected to the system level by parameterizing system models developed by the Hydrogen Storage Engineering Center of Excellence (HSECoE).

08 HYDROGEN↗

Notes on Davis EOS: Historical Perspective, Derivations & Physical Considerations

Davis reactant and product semi-empirical EOS have been introduced in a series of publications by W. C. Davis. These EOS are convenient to use in conjunction with reactive burn models to parameterize the thermodynamic response of quiescent high explosives (HE) and the mixture of HE decomposition products. There is a history of development of these EOS with different formulae and functional forms found in different papers. The goal of the present notes is to track that history, clarify the physical motivation behind various modifications to Davis EOS over time, and collect all the relevant formulae in one place.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Uncertainty quantification for equations of state: copper as an example

Equations of state are essential for providing a fundamental description of materials properties in thermodynamic equilibrium and are used to provide closure relations for hydrodynamics simulations. Generally, equations of state rely on simple physics-based parameterized materials models to inform on the free energy of a material through out a given thermodynamic state space. Historically the parameters of these models have been tuned by hand to fit various experimental data. However, modern optimization and uncertainty quantification techniques allow us to quickly test thousands of parameter combinations and obtain meaningful uncertainty estimates on the parameters, opening opportunities for assessing systematic uncertainties in experiments, assessing model adequacy, and more. In this report, we use Bayesian inference to fit the solid (fcc) equation of state of copper. We focus on fitting five different experimental datasets, including the isobaric density, isobaric heat capacity, room temperature isotherm, principal isentrope, and principal Hugoniot. We fit all five data types simultaneously, and then explore the extent to which combinations of 2 subsets of the 5 datasets can constrain the EOS parameters, as compared to the fit to all 5. This information is useful for investigating the extent to which different datasets can con strain EOS models and thereby help guide experimental investigations in order to best constrain the EOS. We also discuss ways that the methodologies can be used to investigate systematic discrepancies between experiments, as well as how the methods can be used to assess model uncertainty. The framework we develop is general, in that it can be used with a variety of optimization or uncertainty quantification techniques and with a variety of data sources, including both experimental and ab-inito data.

97 MATHEMATICS AND COMPUTING↗

Initial Uncertainty Analysis of Carbon Tetrachloride Contamination and Remediation in the Ringold A and Lower Mud Units at the Central Plateau

The long-term effectiveness of groundwater cleanup at the Hanford Site Central Plateau depends on predictive models that can capture key uncertainties in contaminant fate and transport. Carbon tetrachloride (CCl 4 ), a persistent and toxic compound, presents particular challenges due to variability in degradation rates, uncertainty in initial plume distribution, and subsurface heterogeneity. These uncertainties directly influence plume persistence, migration pathways, and remedy performance, and thus must be systematically evaluated to support long-term remediation planning. To address these gaps, a large-scale Monte Carlo analysis was conducted using the Plateau to River (P2R) model framework. The modeling approach parameterized three primary uncertainty factors: (1) degradation rate, (2) initial plume distribution, and (3) hydraulic conductivity. Degradation was represented as a first-order process, with half-lives ranging from 70 to 700 years. Initial plume distributions were created using a geostatistical simulation method (sgsim), which generates many equally plausible versions of how contaminants might be distributed underground. From this, 100 different scenarios were mapped onto the P2R grid. Variability in hydraulic conductivity was represented in a similar way, with 100 scenarios each for the Ringold Lower Mud and Ringold A units (layers 6 and 7), based on fitted exponential variograms and conditioned to well data. In total, more than 1000 realizations were simulated to assess plume behavior under uncertainty. Results demonstrate that degradation kinetics exert the strongest control over plume persistence: Shorter half-lives produced rapid mass reduction, while longer half-lives yielded persistent plumes with limited attenuation. A nonlinear response was observed, with steep mass reductions at half-lives greater than 200 years and near-linear declines beyond this threshold, reflecting interactions between degradation and pumping. The initial plume distribution strongly influenced early transport patterns, with broader sources generating larger plume footprints, although pump-and-treat operations constrained plume migration to managed areas. By comparison, hydraulic conductivity variability in the Ringold units had only a secondary influence, modifying spreading behavior without altering the dominant migration pathways governed by source configuration and hydraulic controls. Overall, the analysis highlights that uncertainty in degradation rate and initial plume configuration are the primary drivers of variability in plume predictions, while conductivity heterogeneity plays a limited role. These findings underscore the need for improved site-specific data on degradation processes and source characterization to enhance the reliability of long-term performance assessments and to better inform remedial decision-making at the Central Plateau.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Data From: Simulating bioclogging effects on dynamic riverbed permeability and infiltration, Water Resources Research

We collected a time series of Russian River infiltration rates to parameterize stochastic model development of infiltration conditions as a function of bioclogging. The time series of infiltration were collected from the Russian River Riverbank Filtration site located in Sonoma County California. Infiltration datasets are shown in units of m/day and were obtained using a seepage meter in 2012. To address the combined effects of bioclogging and disconnection on infiltration, we developed numerical representations of bioclogging processes based on these datasets using a within a one-dimensional, variably saturated flow model representing losing-connected and losing-disconnected rivers. All models and bioclogging formulations were used to create synthetic test cases for bioclogging.This research was supported by the Jane Lewis Fellowship from the University of California, Berkeley, the Sonoma County Water Agency (SCWA), the Roy G. Post Foundation Scholarship, the U.S. Department of Energy, Office of Science Graduate Student Research (SCGSR) Program, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award DE-AC02-05CH11231, and the UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany.

54 ENVIRONMENTAL SCIENCES↗

SPRUCE Representing Northern Peatland Microtopography and Hydrology within the Community Land Model: Modeling Archive

Here we provide model code, inputs, outputs and evaluation datasets for a new configuration of the Community Land Model (CLM) for SPRUCE, which includes a fully prognostic water table calculation for SPRUCE. Our structural and process changes to CLM focus on modifications needed to represent the hydrologic cycle of bogs environment with perched water tables, as well as distinct hydrologic dynamics and vegetation communities of the raised hummock and sunken hollow microtopography characteristic of SPRUCE and other peatland bogs. The modified model was parameterized and independently evaluated against observations from an ombrotrophic raised-dome bog in northern Minnesota (S1-Bog), the site for the Spruce and Peatland Responses Under Climatic and Environmental Change experiment (SPRUCE).

54 ENVIRONMENTAL SCIENCES↗

Multimessenger Diagnostics of the Engine behind Core-collapse Supernovae

Abstract Core-collapse supernova explosions play a wide role in astrophysics by producing compact remnants (neutron stars or black holes) and the synthesis and injection of many heavy elements into their host galaxy. Because they are produced in some of the most extreme conditions in the universe, they can also probe physics in extreme conditions (matter at nuclear densities and extreme temperatures and magnetic fields). To quantify the impact of supernovae on both fundamental physics and our understanding of the universe, we must leverage a broad set of observables of this engine. In this paper, we study a subset of these probes using a suite of one-dimensional, parameterized mixing models: ejecta remnants from supernovae, ultraviolet, optical and infrared light curves, and transient gamma-ray emission. We review the other diagnostics and show how the different probes tie together to provide a more clear picture of the supernova engine. Join us in improving and evolving this document through active community engagement. Instructions are provided at this link: https://github.com/clfryer/MM-SNe .

79 ASTRONOMY AND ASTROPHYSICS↗

Updraft dynamics and microphysics: on the added value of the cumulus thermal reference frame in simulations of aerosol–deep convection interactions

One fundamental question about atmospheric moist convection processes that remains debated is whether, or under which conditions, a relevant variability in background aerosol concentrations may have a significant dynamical impact on convective clouds and their associated precipitation. Furthermore, current climate models must parameterize both the microphysical and the cumulus convection processes, but this is usually implemented separately, whereas in nature there is a strong coupling between them. As a first step to improve our understanding of these two problems, we investigate how aerosol concentrations modify key properties of updrafts in eight large-eddy-permitting regional simulations of a case study of scattered convection over Houston, Texas, in which convection is explicitly simulated and microphysical processes are parameterized. Dynamical and liquid-phase microphysical responses are investigated using the following two different reference frames: static cloudy updraft grid cells versus tracked cumulus thermals. In both frameworks, we observe the expected microphysical responses to higher aerosol concentrations, such as higher cloud number concentrations and lower rain number concentrations. In terms of the dynamical responses, both frameworks indicate weak impacts of varying aerosol concentrations relative to the noise between simulations over the observationally derived range of aerosol variability for this case study. On the other hand, results suggest that thermals are more selective than cloudy updraft grid cells in terms of sampling the most active convective air masses. For instance, vertical velocity from thermals is significantly higher at upper levels than when sampled from cloudy updraft grid points, and several microphysical variables have higher average values in the cumulus thermal framework than in the cloudy updraft framework. In addition, the thermal analysis is seen to add rich quantitative information about the rates and covariability of microphysical processes spatially and throughout tracked thermal lifecycles, which can serve as a stronger foundation for improving subgrid-scale parameterizations.

54 ENVIRONMENTAL SCIENCES↗

Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere

The distribution of methane (CH 4 ) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH 4 mixing ratio (XCH 4 ), which is being measured increasingly for the assessment of CH 4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH 4 accurately for estimating surface emissions from XCH 4 . Simulations from three CTMs are tested against XCH 4 observations from the Total Carbon Column Network (TCCON). We analyze how the model–TCCON agreement in XCH 4 depends on the model representation of stratospheric CH 4 distributions. Model equivalents of TCCON XCH 4 are computed with stratospheric CH 4 fields from both the model simulations and from satellite-based CH 4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH 4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Using MIPAS-based stratospheric CH 4 fields in place of model simulations improves the model–TCCON XCH 4 agreement for all models. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH 4 bias is significantly reduced from 38.1 to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 to 4.3 ppb) and LMDz (Laboratoire de Météorologie Dynamique model with zooming capability; from 6.8 to 4.3 ppb). Replacing model simulations with MIPAS stratospheric CH 4 fields adjusted to ACE-FTS reduces the average XCH 4 bias for ACTM (3.3 ppb), but increases the average XCH 4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that model errors in simulating stratospheric CH 4 contribute to model biases. Current satellite instruments cannot definitively measure stratospheric CH 4 to sufficient accuracy to eliminate these biases. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH 4 and, hence, its contribution to XCH 4 . Furthermore, it would be worthwhile to analyze how individual model components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) impact the simulation of stratospheric CH 4 and XCH 4 .

54 ENVIRONMENTAL SCIENCES↗

Synergy between land use and climate change increases future fire risk in Amazon forests

Tropical forests have been a permanent feature of the Amazon basin for at least 55 million years, yet climate change and land use threaten the forest's future over the next century. Understory forest fires, which are common under the current climate in frontier forests, may accelerate Amazon forest losses from climate-driven dieback and deforestation. Far from land use frontiers, scarce fire ignitions and high moisture levels preclude significant burning, yet projected climate and land use changes may increase fire activity in these remote regions. Here, we used a fire model specifically parameterized for Amazon understory fires to examine the interactions between anthropogenic activities and climate under current and projected conditions. In a scenario of low mitigation efforts with substantial land use expansion and climate change – Representative Concentration Pathway (RCP) 8.5 – projected understory fires increase in frequency and duration, burning 4–28 times more forest in 2080–2100 than during 1990–2010. In contrast, active climate mitigation and land use contraction in RCP4.5 constrain the projected increase in fire activity to 0.9–5.4 times contemporary burned area. Importantly, if climate mitigation is not successful, land use contraction alone is very effective under low to moderate climate change, but does little to reduce fire activity under the most severe climate projections. These results underscore the potential for a fire-driven transformation of Amazon forests if recent regional policies for forest conservation are not paired with global efforts to mitigate climate change.

54 ENVIRONMENTAL SCIENCES↗

Campaign datasets for Two-Column Aerosol Project (TCAP)

This campaign was designed to provide a detailed set of observations with which to 1) perform radiative and cloud condensation nuclei (CCN) closure studies, 2) evaluate a new retrieval algorithm for aerosol optical depth (AOD) in the presence of clouds using passive remote sensing 3) extend a previously developed technique to investigate aerosol indirect effects, and 4) evaluate the performance of a detailed regional-scale model and a more parameterized global-scale model in simulating particle activation and AOD associated with the aging of anthropogenic aerosols. To meet these science objectives, the ARM Mobile Facility (AMF) and the Mobile Aerosol Observing System (MAOS) was deployed on Cape Cod, Massachusetts for a 12-month period starting in the summer of 2012 in order to quantify aerosol properties, radiation and cloud characteristics at a location subject to both clear- and cloudy- conditions, and clean- and polluted-conditions. These observations were supplemented by two aircraft intensive observation periods (IOPS), one in the summer and a second in the winter. Each IOP required two aircraft.

54 ENVIRONMENTAL SCIENCES↗

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictates the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.

97 MATHEMATICS AND COMPUTING↗