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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Changes in Physical Properties of Everglades Peat Soils Induced by Increased Salinity at the Laboratory Scale: Implications for Changes in Biogenic Gas Dynamics

Abstract Saltwater intrusion due to sea level rise is a major concern for the Florida Everglades because it may induce shifts in ecosystem productivity and physical soil properties. However, the effects of saline water intrusion into the current carbon gas dynamics of the Everglades (particularly in terms of biogenic gas production and emissions, i.e., CH 4 and CO 2 ) are still uncertain. In this work, we present a laboratory‐based study to simulate how sea level rise may alter the physical properties (i.e., hydraulic conductivity) of peat soils from the Everglades and consequently affect the accumulation and release of biogenic gases within the peat matrix. Peat monoliths collected from the Everglades were subjected to progressive increases in salinity from a NaCl solution, and changes to the biogenic gas dynamics regime were simultaneously monitored using a combination of time‐lapse ground‐penetrating radar measurements, manometers, time‐lapse photography, and gas traps. Physical changes to the peat matrix at each salinity interval were assessed using constant head permeameter tests. Consistent with previous research, results show that a progressive increase in salinity (from fresh to saltwater) results in (1) a progressive increase in hydraulic conductivity and (2) a progressive decrease in gas content within the peat matrix (i.e., production) and gas releases. This work has implications for better understanding the potential effects of saltwater intrusion into freshwater peatland systems in the Everglades, particularly in terms of carbon gas dynamics.

Sirianni, Matthew J.↗
DOE OSTI2021

Using TRMM Latent Heat as a Source to Estimate Convection Induced Gravity Wave Momentum Flux in the Lower Stratosphere

Due to a lack of observations, it is a big challenge to quantify the momentum flux transported from the troposphere into the lower stratosphere by gravity waves generated by convection. This limits our understanding of the dynamics of general circulation in the stratosphere and the realism of simulations of the Quasi Biennial Oscillation. In the past decade, some general circulation models have linked the momentum flux to the latent heating from subgrid scale convective precipitation with some success. However, there is still a large uncertainty in the sources of subgrid scale convection in these models. This study applies the links between the momentum flux and convective latent heating directly to the convective precipitation derived from 16 years of TRMM precipitation radar observations in the tropics and subtropics. The total and directional momentum flux at 100 hPa are derived at individual convective pixels by using the TRMM Spectrum Latent Heating product and large-scale wind profiles from the ERA-Interim dataset. For the first time, we are able to estimate the geographical distribution of momentum flux at 100 hPa in tropics and subtropics from the observed convective sources. The diurnal, seasonal, and interannual variations of the derived momentum flux are presented and discussed. These results could provide a reference to validate the dynamic coupling between the tropical troposphere and the stratosphere in the general circulation models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

Flood prediction systems need hierarchical atmospheric, hydrologic, and hydraulic models to predict rainfall, runoff, streamflow, and floodplain inundation. The accuracy of such systems depends on the error propagation through the modeling chain, sensitivity to input data, and choice of models. In this study, we used multiple precipitation forcings (hindcast and forecast) to drive hydrologic and hydrodynamic models to analyze the impacts of various drivers on the estimates of flood inundation depth and extent. We implement this framework to unravel the August 2021 extreme flooding event that occurred in Central Tennessee, USA. We used two radar-based quantitative precipitation estimates (STAGE4 and MRMS) as well as quantitative precipitation forecasts (QPF) from the National Weather Service Weather Prediction Center (WPC) to drive a series of models in the hierarchical framework, including the Variable Infiltration Capacity (VIC) land surface model, the Routing Application for Parallel Computation of Discharge (RAPID) river routing model, and the AutoRoute and TRITON inundation models. An evaluation with observed high-water marks demonstrates that the framework can reasonably simulate flood inundation. Despite the complex error propagation mechanism of the modeling chain, we show that inundation estimates are most sensitive to rainfall estimates. Most notably, QPF significantly underestimates flood magnitudes and inundations leading to unanticipated severe flooding for all stakeholders involved in the event. Finally, we discuss the implications of the hydrodynamic modeling framework for real-time flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Formulation of autoconversion and drop spectra shape in shallow cumulus clouds

Two-moment autoconversion parameterizations as compared to accretion parameterizations exhibit significant errors suggesting that additional moments are needed to increase their accuracy. We develop a three-moment autoconversion parameterization using output from an LES model with size-resolved microphysics. Adding the third moment decreases the errors of parameterization and improves precipitation prediction. However, the errors are still significantly larger than errors of accretion rate. An analysis of the cloud drop size distributions (DSDs) in the simulated tropical convective cloud system reveals that most of DSDs have a significant fraction of cloud liquid water content (qc) in the mid-size droplet range (radii from 20 to 40 microns). Our data indicates that more than 30% of DSDs have over half of qc contained in the mid-size range and about 60% of spectra have, at least, one third of qc in this range. Even when the rain/drizzle mode is small (radar reflectivity Z < -10 dBZ), there is a significant number of spectra in which fraction of qc in the mid-size range is as large as 60%. These DSDs are more complex than the frequently used single-mode Gamma or Log-normal distributions, which usually do not have a considerable tail extending to the mid-size range and can be defined by three microphysical moments. The need to define DSDs by more than three moments explains the large errors in the three-moment autoconversion parameterization. The limitation of three-parameter Gamma or Log-normal distributions should be kept in mind when applying them in precipitating shallow Cu clouds.

Kogan, Yefim↗
DOE OSTI2020

Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations (Final Report)

Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sensitivity of back‐projection algorithm (BPA) synthetic aperture radar (SAR) image formation to initial position, velocity, and attitude navigation errors

Abstract The Back‐Projection Algorithm (BPA) is a time‐domain‐matched filtering technique to form synthetic aperture radar (SAR) images. To produce high‐quality BPA images, precise navigation data for the radar platform must be known. Errors in position, velocity, or attitude result in improperly formed images that are corrupted by shifting and blurring. The contribution of this paper is the development of analytical expressions that characterise the relationship between navigation errors and image formation errors from an inertial navigation point of view, where trajectory estimation errors in position, velocity, and attitude propagate through time and cause compounding errors in the vehicle state vector. These analytical expressions are verified via simulated image formation and real‐data image formation.

Lindstrom, Colton↗
DOE OSTI2021

Suitability of Altair FEKO's Computational Electromagnetic Techniques for Tree Model Scattering and Penetration at Ku-band

Foliage penetration (FOPEN) radar at lower frequencies (VHF, UHF) is a well-studied area with many contributions. However, there is growing interest in using higher Ku-band frequencies (12-18 GHz) for FOPEN. Specifically, the reduced wavelength sizes provide some key saliencies for developing more optimized detection solutions. The disadvantage is that exploiting Ku-band for FOPEN is complicated because higher frequencies have pronounced scattering effects due to their smaller wavelengths. A methodology h as been developed to model and simulate FOPEN problems that characterize the phenomenology of Ku-band electromagnetic ( EM ) wave transmissions through moderate foliage. The details of this research (i.e. the realistic tree models, simulation setup and results) are documented in multiple reports. The main focus of this report is to describe the preliminary validation and verification of Altair FEKO, the computational EM (CEM) software used for this research, as well as present a simplified symmetrical tree model and an introductory CAD tree model.

42 ENGINEERING↗
DOE OSTI2023

Boundary Layer Controls on the Shallow-to-Deep Cumulus Transition (Final Technical Report)

This project advanced our understanding of the processes governing cumulus cloud formation and provides an improved observational basis for validating earth system models. To be specific, the project used laser- and radar-remote sensors to examine the physical properties of updrafts that rise from earth’s surface and initiate clouds deeper in the atmosphere. These updrafts comprise “thermals” and “plumes” and occur at small spatial and temporal scales (e.g., 10s of minutes, 100s of meters). These small scales preclude explicit representation in most earth system and climate models, and thus necessitate “sub-grid-scale” parameterization of updraft processes. The innovation of this project was to directly measure the size, shape, strength and water vapor content of these updrafts with Doppler and Raman lidars, respectively, and to link these updraft properties to cloud processes using vertically pointed weather radars. The resulting data sets comprise 100s of thousands of updrafts and thousands of clouds, which far exceeds previous efforts, and thereby provides a robust statistical and physical representation of these processes. From these large datasets the project produced a sequence of scientific analyses that: (1) Elucidate how variations in the turbulent structure of the convective boundary layer control shallow cumulus convection, (2) Quantify the upward transport of water vapor to cloud base via thermals and plumes, (3) Validate large-eddy simulations of updrafts and shallow convective clouds, (4) Demonstrate a size-to-strength relationship between updraft width and updraft speed, and (5) Demonstrate how updrafts interact with the stability at the top of the convective boundary layer to modulate the depth and vigor of convective clouds. These results have been disseminated via several published journal articles, academic theses, and conference presentations. Collectively these results contribute to the Atmospheric System Research (ASR) program’s goal to “improve understanding of the key cloud, aerosol, precipitation, and radiation processes that affect the Earth’s radiative balance and hydrological cycle, particularly processes that limit the predictive ability of regional and global earth system models”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Abstract Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Polar Cloud Microphysics and Surface Energy Budget from AWARE (Final Technical Report)

This was a collaborative project between the Scripps Institution of Oceanography at the University of California San Diego (SIO), Byrd Polar and Climate Research Center at The Ohio State University (BPCRC), The Department of Meteorology and Atmospheric Science at The Pennsylvania State University (Penn State), and Brookhaven National Laboratory (BNL). The project’s over-arching objectives involved maximizing the scientific potential of the ARM West Antarctic Radiation Experiment (AWARE) data set, in two respects: (1) application of polar-optimized climate models to test the most current and comprehensive cloud microphysical parameterizations; and (2) analysis of the most advanced ARM Mobile Facility sensors (e.g., cloud radar, spectroradiometer, and aerosol observing system data) to advance understanding of Antarctic cloud microphysical and radiative properties, particularly on a climatological basis with emphasis on contrasts with the high Arctic. The team’s efforts have resulted in 16 peer-review publications, in addition to an overview of the AWARE campaign in the Bulletin of the American Meteorological Society. Our emphasis throughout this program was to go beyond the traditional “case study” approach to model evaluation, in which a “classical” or otherwise well-characterized surface-atmosphere-cloud system of short duration is simulated by several climate models. Instead, we attempted analysis of longer time series in the AWARE data, both climatologically and with models. We have numerous successful results from both AWARE locations: The main AMF-2 deployment at McMurdo Station on Ross Island (13 months duration), and the extended facility at the West Antarctic Ice Sheet (WAIS) Divide Ice Camp (40 days during austral summer 2015-16).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A high-resolution pseudo-polygon discrete element model for regional sea ice

Here, this work presents a pseudo-polygon discrete element model for high-resolution sea ice simulations. A scale-invariant bonded particle contact model is proposed to model joints between sea ice floes based on the smeared fracture model and a lattice spring beam model, and the Mohr–Coulomb failure criterion is implemented to represent the shearing failure mechanism of sea ice packings under complex loadings. All mechanical parameters of the bond model can be directly determined from laboratory tests. Validations of the proposed model are made by investigations of mechanical response and failure criteria of field sea ice sheets. Compared with the field observations of sea ice from satellite radar and in situ stress sensors, the proposed model is capable of reproducing the typical constitutive behavior and the Coulomb friction envelope of field sea ice. Finally, the proposed discrete element sea ice model is used to study the effect of loading rates on mechanical behavior including failure strength of regional sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The prevalence of precipitation from polar supercooled clouds

Abstract. Supercooled clouds substantially impact polar surface energy budgets, but large-scale models often underestimate their occurrence, which motivates accurately establishing metrics of basic processes. An analysis of long-term measurements at Utqiaġvik, Alaska, and McMurdo Station, Antarctica, combines lidar-validated use of soundings to identify supercooled cloud layers and colocated ground-based profiling radar measurements to quantify cloud base precipitation. We find that more than 85 % (75 %) of sampled supercooled layers are precipitating over the Arctic (Antarctic) site, with more than 75 % (50 %) precipitating continuously to the surface. Such high frequencies can be reconciled with substantially lesser spaceborne estimates by considering differences in radar hydrometeor detection sensitivity. While ice precipitation into supercooled clouds from aloft is common, we also find that the great majority of supercooled cloud layers without ice falling into them are themselves continuously generating precipitation. Such sustained primary ice formation is consistent with continuous activation of immersion-mode ice-nucleating particles (INPs), suggesting that supercooled cloud formation is a principal gateway to ice formation at temperatures greater than ∼-38 ∘C over polar regions. The prevalence of weak precipitation fluxes is also consistent with supercooled cloud longevity and with well-observed and widely simulated case studies. An analysis of colocated microwave radiometer retrievals suggests that weak precipitation fluxes can be nonetheless consequential to moisture budgets for supercooled clouds owing to small liquid water paths. The results here also demonstrate that the observed abundance of mixed-phase clouds can vary substantially with instrument sensitivity and methodology. Finally, we suggest that these ground-based precipitation rate statistics offer valuable guidance for improving the representation of polar cloud processes in large-scale models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Factors Governing Cloud Growth and Entrainment Rates in Shallow Cumulus and Cumulus Congestus During GoAmazon2014/5

Shallow cumulus and cumulus congestus clouds play an important role in the large-scale tropical circulation by mixing heat and moisture vertically and preconditioning the environment for deeper convection. Different representations of these shallow clouds account for much of the spread in General Circulation Model (GCMs) climate sensitivity, potentially because of how entrainment is represented in GCM parameterizations. This study uses observations from the Department of Energy's Atmospheric Radiation Measurement (ARM) mobile facility deployed at Manacapuru, Brazil, during the Green Ocean Amazon (GoAmazon2014/5) Campaign. Environmental thermodynamic profiles and observations of cloud top height (CTH) are used to constrain an entraining plume model to estimate bulk entrainment rates (ERs). Estimates of CTH are obtained from a combination of vertically pointing W-band ARM cloud radar and 1,290 MHz Radar Wind Profiler observations. A combination of radiosonde, microwave radiometer profiler, and microwave radiometer observations provides new best estimates of the environmental thermodynamic state. We quantify uncertainty in ERs considering uncertainties in estimated CTH, environmental thermodynamic properties, and assumed initial parcel characteristics. We find ERs ranging from 0.16 to 2.8 km -1 with an average of 0.58 ± 0.10 km -1 over a selected population of 469 shallow cumulus and cumulus congestus clouds. Using the retrieved estimates of ER, we evaluate several entrainment closures that are currently used in atmospheric models or have been proposed based on theory or large eddy simulation. Finally, entrainment rates in cumulus clouds are weakly correlated with low-level buoyancy, cloud depth, and cloud size.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Dynamic Queue Prediction at Signalized Intersections with Fusing Sensory Information and Connected Vehicles

Queue length is one of the most essential metrics for indicating traffic conditions at signalized intersections. Most existing studies mainly focus on queue length estimation in a retrieving way which cannot effectively predict the future state of vehicle in queue. This paper proposes an innovative dynamic queue prediction model in a vehicle-infrastructure cooperative way. The proposed dynamic queue predictor can customize the queue prediction using the trajectories of preceding vehicles from radar detection with varying information from loop detectors and connected vehicles (CVs). The dynamic queue prediction accuracy can be further improved with at least one present CV in the same cycle. With the accurate dynamic queue prediction, we present a case study of Queue-Aware Eco-Approach and Departure (QEAD) to provide an online optimal vehicle trajectory considering both the signal status and preceding vehicle's future status in queue. The proposed framework and algorithms are of pragmatic significance and one of its advanced features is the ability to work under low penetration rate of CVs. The test using Next Generation SIMulation (NGSIM) dataset and a case study in VISSIM microscopic simulation show promising results.

Ye, Fei↗
DOE OSTI2026

Surface Quantitative Precipitation Estimates (SQUIRE) of Snow Water Equivalent from the Surface Atmospheric Integrated Field Laboratory

The upper Colorado River basin is the primary source of water for 40 million people. With declining snowpack in the basin, forecasting hydrological budgets in the Southwest United States is more important than ever. However, due in part, to a lack of reliable observations of precipitation in complex terrain, hydrological models struggle to assess and forecast snowpack snow water equivalent (SWE) in the upper Colorado River basin (UCRB). Therefore, the need for more reliable SWE forecasts in the UCRB motivated the U.S. Department of Energy Atmospheric Radiation Measurement Facility’s Surface Atmospheric Integrated Field Laboratory (SAIL) that occurred from June 2021 to June 2023. During SAIL, the X-band precipitation radar from Colorado State University conducted volume scans sampling the precipitation properties over the UCRB. The ARM facility developed a gridded Surface Quantitative Precipitation Estimates (SQUIRE) product from the radar observations. To do this, various daily SWE estimates from radar using the radar reflectivity factor Z e and specific differential phase K dp were compared against ground-based precipitation gauges. SWE in precipitation calculated from Wolfe and Snider’s S–Z e estimator was in best agreement with the rain gauges for the days when SWE < 12 mm. For days with SWE > 12 mm, the WSR-88D Intermountain West relationship had the best agreement with the precipitation gauges. Airborne snow depth observations show that SQUIRE captures regions of orographic enhancement in the mountains to the west and northwest of the SAIL study area, indicating that the scientific community should focus on understanding and ultimately simulating orographic atmospheric precipitation processes to improve UCRB snowpack SWE assessment and forecasting.

Hydrology↗
DOE OSTI2022

Feasibility Study of Millimeter Wave Radars For Safeguards Applications

Containment and surveillance are fundamental measures in nuclear safeguards. Techniques such as video surveillance and laser curtain for containment provide effective monitoring in areas where maintaining continuity of knowledge is required. These systems, however, can be susceptible to loss of monitoring capabilities under certain environmental conditions such as poor visibility (i.e. low light conditions, smoke, fog, etc.) or extended power loss past the duration that the backup power system is designed for. Brookhaven National Laboratory has been investigating the feasibility of millimeter waves (mmWave) as a new perimeter seal in which radio frequency waves in the range of 60-64 GHz are used to detect and monitor objects of interest. Signals in this frequency range are not susceptible to environmental conditions. For proof-of-concept tests, mmWave sensors from Texas Instruments (TI), specificallyIWR6843, are used in a test bed at BNL's Waste Management facility to simulate the operations at nuclear facilities. The unique design of TI mmWave sensors requires less memory and power consumption compared to counterpart systems. These devices are capable of exporting 3D point-cloud data, which is visualized graphically and compared to videos recorded at the same time to validate the performance of the mmWave sensor. A set of experiments were planned to test the feasibility of the mmWave in this application, including monitoring static containers in a storage area and detecting intrusions at the boundaries of the area. In addition, the experiments also identify potential blind spots relative to sensor position and utilize multiple operating sensors simultaneously to reduce or eliminate such blind spots. The optimal positioning of multiple sensors was determined for the experimental room configuration. In this paper, we will discuss the details of this novel perimeter sealing concept and present the test results.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Understanding Asteroid 16 Psyche’s composition through 3D impact crater modeling

Asteroid 16 Psyche is the largest M-type (metallic) Main Belt Asteroid (MBA). Radar albedo data indicate Psyche’s surface is rich in metallic content, but estimates for Psyche’s bulk structure vary widely, with bulk density estimates ranging from 1.4 ± 0.3 g/cm 3 to 4.5 ± 1.4 g/cm 3 and porosity estimates ranging from 30% to 70%. A forthcoming NASA discovery mission will visit Psyche. Narrowing the range of estimated compositions of Psyche prior to mission launch can help to determine the necessary tools for asteroid analysis. Psyche has two large impact structures in its Southern hemisphere, with estimated diameters from 50 km to 70 km and estimated depths up to 6.4 km. In this work, we present results from 2D and 3D hydrocode simulations of the largest of these impact structures. Through quantitative comparison of the simulated crater dimensions with measured values, our models suggest that Psyche is largely composed of porous, metallic material. In addition, to match the crater dimensions, our simulations indicate that the angle of impact was significantly altered from vertical by 45–60 degrees.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Earth System Reanalysis in Support of Climate Model Improvements

Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.

54 ENVIRONMENTAL SCIENCES↗