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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Coastal Cloud Chemistry during EPCAPE Field Campaign Report

For this campaign, Principal Investigator Markus Petters deployed a suite of instruments inside Lynn Russell’s container that was sited at Mt. Soledad during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) campaign. Funding for this activity was provided by National Science Foundation Award 2410536. The instrument rack (Figure 1) included a differential mobility analyzer, a condensation particle counter, a portable optical particle spectrometer, and a continuous flow diffusion cloud condensation nuclei counter (CCN). The instruments sampled from a counterflow virtual impactor (CVI) inlet and an isokinetic inlet. The measurements provided aerosol size distribution, size-resolved CCN activity, and denuded versus undenuded CCN activity of aerosols and cloud drop residuals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

EPCAPE-PT-LANL Measurements: Ground based counterflow virtual impactor

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Ground based counter flow virtual impactor (Brechtel Inc) Data Notes: A factor of 6.7 needs to be applied to all cloud droplet residual concentration to correct the enhancement of the concentration because all the residual samples collected at 100 lpm were delivered into the 15 lpm of CVI sample flow. [https://amt.copernicus.org/articles/5/1259/2012/amt-5-1259-2012.html] Header: - Visibility[m]: The atmospheric visibility at the time of measurement, expressed in meters. - QualityControl_Flag[bool]: A boolean flag indicating whether the measurement passed quality control checks. - Temperature[C]: The ambient temperature at the time of the measurement, expressed in degrees Celsius. - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active (true) or inactive (false) during the measurement. - RelativeHumidity[%]: The relative humidity at the time of the measurement, expressed as a percentage

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SCILLA mini-Aerosol Mass Spectrometer (mAMS) airborne data

Measurements of non-refractory, sub-micron aerosol composition, namely organic, sulfate, nitrate, chloride, and ammonium aerosols, by a mini- aerosol mass spectrometer (mAMS, Aerodyne Research, Inc.) are reported. The instrument sampled ambient air through 3 different inlets: unperturbed ambient air, a Counter-flow Virtual Impactor (CVI), and an oxidation flow reactor (OFR). Users should consult the Sampling Key for proper interpretation of the data.

and secondary aerosol in air",total_organics,nitra↗
DOE OSTI2025

Infrared Cloud Imager Instrument Intercomparison Report

The Infrared Cloud Imager Instrument Intercomparison was a guest instrument deployment by NWB Sensors to the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility observatory on the Southern Great Plains (SGP) between May 18 and December 12, 2023. NWB Sensors is a company that has developed a commercially available infrared cloud imager (ICI). The ICI provides radiometrically calibrated, full-sky images of the downwelling infrared radiance in the 7.3-14 µm band. In addition, it provides cloud radiance as the residual between the observed radiance and the modeled cloud-free radiance as well as derived cloud products. The instrument is used in applications that require consistent detection of clouds across day and night. For more information, consult the instrument's webpage. The primary goal of the deployment was to validate the radiometric accuracy of the ICI. The ICI uses a proprietary calibration method to convert the raw data from its infrared camera into downwelling radiance. Unlike similar instruments, the system does not have an onboard blackbody calibration standard. Instead, NWB Sensors characterizes each ICI camera individually in an environmental chamber while looking at a blackbody standard. The resulting (proprietary) calibration is used operationally in the instrument and has been demonstrated to be stable over long periods. To validate the radiometric products from the ICI, an intercomparison between the ICI data products and those from ARM’s atmospheric emitted radiance interferometer (AERI) was made. The AERI is a best-in-class instrument for measuring downwelling infrared radiance (Gero et al. 2025). A weighted integration of the AERI’s spectral radiances across the ICI’s camera response was performed. The resulting radiance (herein called the AERI radiance) was directly compared to the zenith radiance concurrently observed by the ICI. The results of these comparisons are reported in the next section of this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Characterizing the Small-Scale Dynamical, Ice Microphysical, and Residual Aerosol Properties of Mid-Latitude Cold Clouds: A Pilot Study (Final Report)

Our understanding of cirrus clouds has been advancing rapidly due to improvements in measurements made inside clouds, computer cloud models, and laboratory experimentation. This advancement in knowledge has made it clear that cirrus are complicated systems where the intricate interactions among dynamic air motions, solar and terrestrial radiation, ice crystal growth, and water and air chemistry can substantially influence cirrus structure and evolution. Measurements made inside clouds have shown that cirrus crystals are often complex with shapes that are often unclassifiable, especially at small sizes. Laboratory measurements have revealed that the ice crystal surface is complex, even at small sizes. Recent laboratory and satellite studies show that this surface complexity modifies the radiative properties of cirrus clouds in important ways. Moreover, observations and computer cloud modeling work show that the larger and smaller scale properties of cirrus are sensitive to the interplay between small-scale air motions and the aerosol articles upon which cirrus ice crystals form. Taken together, these studies indicate that accurately accounting for the radiative properties of cirrus clouds requires knowing the air motions and aerosol properties that influence the number concentration and crystal size, along with an understanding of the surface properties of vapor grown ice. Although this prior work has been illuminating, there have been few studies that provide detailed shape and surface complexity measurements for the small ice crystals that often compose cirrus clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Performance of pumped counterflow virtual impactors to study aerosol interactions with laboratory generated warm clouds

Pumped Counterflow Virtual Impactors (PCVI) are designed to separate aerosols based on aerodynamic diameter, which is particularly useful for isolating cloud droplets and ice crystals from smaller particles. However, the PCVI transmission efficiency (TE) values reported in the literature show considerable variability, and little information is available on the TEs for cloud liquid droplets. Here, we determined the optimal flow conditions for PCVI sampling for different activation ratios from the MTU Pi-cloud chamber, highlighting the conditions that maximize droplet residual sampling while minimizing interstitial transmission. Even for a lower limit cloud activation ratio = 1:10, an add flow of 1.5 LPM achieves a residual fraction greater than 0.80, with a droplet TE of ∼15% for typical Pi-Chamber droplets with diameters between 3.5 to 10 µm. This framework can be adapted for field cloud measurements based on the cloud conditions of interest. TE for cloud droplets under flow conditions at which ∼99% of the unactivated submicron particles were removed was substantially lower between 8 and 16% than for supermicron dry particles (∼30 to 40% for 3 µm polystyrene latex spheres). In addition, we conducted a comparison of the performances of three similar PCVI units to assess repeatability for dry aerosols and cloud droplets. All three PCVIs performed similarly in rejecting submicron particles smaller than the desired cutoff diameter. One of the units showed lower TEs due to a misalignment of the internal orifice. We discuss a procedure that improved alignment and performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Relative Contributions of Atmospheric, Oceanic, and Coupled Processes to North Pacific and North Atlantic Variability

Patterns of sea surface temperature (SST) variability over the northern oceans arise from a combination of atmospheric, oceanic, and coupled processes. In this work, we use a novel methodology and a suite of observations to quantify the processes contributing to the dominant patterns of interannual SST variability over these regions. We decompose the upper ocean heat content tendency associated with such dominant patterns into contributions from different heat fluxes: (a) atmospherically driven, (b) surface feedbacks, and (c) oceanic. We find that in the subtropics, cloud radiative flux, turbulent heat flux, and residual oceanic processes each contributes substantially to North Pacific SST variability, whereas turbulent heat flux primarily induces North Atlantic SST variability. Cloud radiative fluxes therefore provide a major source of interannual SST variability in the North Pacific but not in the North Atlantic. In midlatitudes, SST fluctuations over the northern oceans are driven by the combination of turbulent and oceanic heat fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

NWB Sensors Infrared Cloud Imager Data Products from SGP

NWB Sensors is a company which has developed a commercially available Infrared Cloud Imager (ICI). For more information, consult the company's webpage, https://www.nwbsensors.com/infrared-cloud-imager. To validate the radiometric accuracy of the ICI, NWB Sensors deployed it to the ARM SGP User Facility in 2023. The primary motivation of this deployment was to perform an intercomparison between the ICI and the Atmospheric Emitted Radiance Interferometer (AERI). The AERI spectral radiance data product can be integrated across the response function of the ICI and directly compared to the zenith radiance observed by the ICI. In addition, the ICI uses proprietary models of the downwelling clear-sky radiance in its cloud processing algorithms. They are based on surface meteorology and precipitable water vapor (PWV). These models were validated by comparing their predicted radiances to those derived from radiative transfer models of the ARM radiosondes. Finally, PWV observations derived from the ICI's onboard GNSS-based PWV retrieval system were compared against those from the microwave radiometer. This dataset contains the ICI radiance and cloud data products.

Atmosphere↗
DOE OSTI2023

Three-dimensional phenotyping of peach tree-crown architecture utilizing terrestrial laser scanning

Tree training systems for temperate fruit have been developed throughout history by pomologists to improve light interception, fruit yield, and fruit quality. These training systems direct crown and branch growth to specific configurations. Quantifying crown architecture could aid the selection of trees that require less pruning or that naturally excel in specific growing/training system conditions. Regarding peaches [Prunus persica (L.) Batsch], access tools such as branching indices have been developed to characterize tree-crown architecture. However, the required branching data (BD) to develop these indices are difficult to collect. Traditionally, BD have been collected manually, but this process is tedious, time-consuming, and prone to human error. These barriers can be circumnavigated by utilizing terrestrial laser scanning (TLS) to obtain a digital twin of the real tree. TLS generates three-dimensional (3D) point clouds of the tree crown, wherein every point contains 3D coordinates (x, y, z). To facilitate the use of these tools for peach, we selected 16 young peach trees scanned in 2021 and 2022. These 16 trees were then modeled and quantified using the open-source software TreeQSM. As a result, “in silico” branching and biometric data for the young peach trees were calculated to demonstrate the capabilities of TLS phenotyping of peach tree-crown architecture. The comparison and analysis of field measurements (in situ) and in silico BD, biometric data, and quantitative structural model branch uncertainty data were utilized to determine the reconstructive model’s reliability as a source substitute for field measurements. Mean average deviation when comparing young tree (YT) height was approx. 5.93%, with crown volume was approx. 13.26% across both 2021 and 2022. All point clouds of the YTs in 2022 showed residuals lower than 12 mm to cylinders fitted to all branches, and mean surface coverage greater than 40% for both the trunk and primary branching orders.

09 BIOMASS FUELS↗
DOE OSTI2021

A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry–meteorology feedbacks on air quality

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry–meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM 2.5 ). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O 3 , sulfate, nitrate, ammonium, elemental carbon, tropospheric O 3 residual, and column nitrogen dioxide (NO 2 ). For example, the mean biases have been reduced by more than 10 W m –2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O 3 . However, relatively large biases still exist for cloud predictions, some PM 2.5 species, and PM 10 that warrant follow-up studies to better understand those issues. The impacts of chemistry–meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O 3 , nitrogen oxide (NO x ), volatile organic compounds (VOCs), and PM 2.5 by 3.1 % (up to 27.8 %), 4.2 % (up to 16.2 %), 6.6 % (up to 50.9 %), 5.8 % (up to 46.6 %), and 8.6 % (up to 49.1 %), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

58 GEOSCIENCES↗
DOE OSTI2023

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Intercomparison of MJO Column Moist Static Energy and Water Vapor Budget among Six Modern Reanalysis Products

This study conducts an intercomparison of the column-integrated moist static energy (MSE) and water vapor budget of the Madden–Julian oscillation (MJO) among six modern global reanalysis products (RAs). Inter-RA differences in the mean MSE, MJO MSE anomalies, individual MSE budget terms, and their relative contributions to the propagation and maintenance of MJO MSE anomalies are examined. Also investigated is the relationship between the MJO column water vapor (CWV) budget residuals with the other CWV budget terms as well as with the two parameters that characterize cloud–radiation feedback and moisture–convection coupling. Results show a noticeable inter-RA spread in the mean-state MSE, especially its vertical structure. In all RAs, horizontal MSE advection dominates the propagation of the MJO MSE while column-integrated longwave radiative heating and vertical MSE advection are found to be the key processes for MJO maintenance. The MSE budget terms directly affected by the model parameterization schemes exhibit high uncertainty. The differences in anomalous vertical velocity mainly contribute to the large differences in vertical MSE advection among the RAs. The budget residuals show large inter-RA differences and have nonnegligible contributions to MJO maintenance and propagation in most RAs. RAs that underestimate (overestimate) the strength of cloud–radiation feedback and the convective moisture adjustment time scale tend to have positive (negative) MJO CWV budget residual, indicating the critical role of these processes in the maintenance of MJO CWV anomalies. Our results emphasize that a correct representation of the interactions among moisture, convection, cloud, and radiation is the key for an accurate depiction of the MJO MSE and CWV budget in RAs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Counterflow Virtual Impactor (CVI) Inlet Aboard Aircraft (INLETCVI-AIR) Instrument Handbook

The counterflow virtual impactor (CVI) inlet separates out and samples cloud elements via inertial impaction. Warm, dry, particle-free carrier gas of known composition is pumped to the tip of the inlet in the direction opposite of the flow of air. The flow lines of the incoming free stream air are split. Larger droplets and ice crystals suspended in the sample air have enough inertia to penetrate the counterflow and enter the sample flow, while smaller, unactivated particles follow the streamlines of the air flowing around the inlet’s tip (Noone et al. 1988). The instrument is capable of sampling cloud elements as a function of cut size by changing the velocity of counterflow at the probe tip. The CVI cut size is usually defined as a diameter of a smallest particle for which penetration rate is 50%. For the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facilityGulfstream-159 aircraft’s CVI inlet, that value is usually around 13 microns. The dry heated air, normally used as the carrier gas, also serves to evaporate water from the hydrometeor, leaving the residuals to be sampled by in-cabin aerosol instrumentation. The number concentration of the droplet residue in the CVI sample line is usually increased with respect to corresponding cloud element number concentration. The CVI performance as a concentrator is usually described via CVI enhancement factor, which is a conversion factor between ambient volumetric concentration (not adjusted to standard temperature and pressure) and volumetric concentration in the CVI sample line.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Moist Physics Parameterization Based on Deep Learning

Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained with 1‐year simulation from a superparameterized GCM, SPCAM. An independent year of SPCAM simulation is used for evaluation. In the design of the neural network, referred to as ResCu, the moist static energy conservation during moist processes is considered. In addition, the past history of the atmospheric states, convection, and clouds is also considered. The predicted variables from the neural network are GCM grid‐scale heating and drying rates by convection and clouds, and cloud liquid and ice water contents. Precipitation is derived from predicted moisture tendency. In the independent data test, ResCu can accurately reproduce the SPCAM simulation in both time mean and temporal variance. Comparison with other neural networks demonstrates the superior performance of ResNet architecture. ResCu is further tested in a single‐column model for both continental midlatitude warm season convection and tropical monsoonal convection. In both cases, it simulates the timing and intensity of convective events well. In the prognostic test of tropical convection case, the simulated temperature and moisture biases with ResCu are smaller than those using conventional convection and cloud parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

EIC Hadron Beamline Vacuum Studies

Ninety percent of the EIC hadron ring vacuum chamber consists of cold-bore sections running through and interconnecting 4.55 K RHIC superconducting (SC) magnets. The EIC operating specification requires shorter bunches and 3x higher intensity beams which are not appropriate for the present RHIC stainless steel cold-bore beam tube. The intensity and emittance of the hadron beams will degrade due to interactions with residual gas or vacuum instabilities arising from the expected resistive-wall (RW) heating, electron clouds, and beam-induced desorption mechanisms. Without strategies to limit RW heating, major cryogenic system modifications are needed to prevent SC magnet quenches. The SC magnet cold-bore beam tubes will be equipped with a high RRR copper-clad stainless steel screen to significantly reduce RW heating and so the effect on the SC magnet cryogenic heat load and temperature. A thin amorphous carbon film applied to the beam-facing copper surface will suppress electron cloud formation. This paper discusses the vacuum requirements imposed by the EIC hadron beams and the plans to achieve the necessary vacuum and thermal stability that ensure acceptable beam quality and lifetime.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Fallout Cloud Regimes

The U.S. Department of Defense (DOD), Department of Energy (DOE), and other organizations maintain operational nuclear explosion and atmospheric dispersion models to provide critical guidance on the expected effects of an accidental or deliberate explosion of a nuclear weapon (in this paper simply referred to as “device”). To be effective, these models must represent, as accurately as possible, the complex interactions of the blast, fire, and residual radiological hazards with the environment and population. One hundred atmospheric nuclear tests that form the basis for many models were conducted at the Nevada Test Site (NTS) (now referred to as the Nevada Nuclear Security Site, NNSS) in a dry desert environment. Other environments should be studied, but have less data available and are beyond the scope of the work presented in this paper. The debris clouds produced by the NTS tests, frequently called “mushroom clouds,” are familiar, with common structural elements such as a buoyant cap connected to a skirt of raised dust at the desert surface by a thin, dirt-filled stem. The film scanning project at LLNL has investigated historical film records of nuclear weapons tests. Here, we summarize findings showing that the mushroom cloud behavior for historic U.S. tests conducted in Nevada, has similar characteristics based on the distance of the device from the ground surface or Height of Burst (HOB), scaled by the energy release, or yield, of the device. This scaled height is referred to as the scaled-height-of-burst (SHOB). The findings discussed below show that mushroom clouds look and behave similarly when detonated at the same SHOB. The amount of residual radiation that is produced by a nuclear detonation is proportional to the yield. But, the amount of that residual radiation that actually becomes local fallout is strongly dependent on the SHOB and the type of surface over which the detonation occurs. In order to develop a more comprehensive model that predicts the fraction of the residual radiation that becomes local fallout, it is convenient to define a series of regimes based on SHOB values in which all detonations that occur within a given regime can be modeled using the same algorithms. The purpose of this paper is to provide a framework for defining different regimes, and, in a qualitative way, a basic understanding of the fundamental characteristics of each of these regimes.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Monte Carlo Simulation with CAD Interface for Calculation of 3D Maps of Residual Dose (CRADA)

Objective: To develop an easy-to-use software application to predict and mitigate radiation effects in research environment, space instruments, nuclear plants and medical facilities and help nonproliferation and national security efforts. Tech-X will develop standalone software libraries and command-line tools for ( 1) translating CAD into tessellated surfaces and tetrahedral meshes in GDML (for Geant4 and MARS 15), ROOT (for MARS 15) and HDF5 (for compact representation and for the visualization) formats, (2) healing CAD geometries to make them suitable for Monte Carlo simulations; (3) creating uniform and variable Cartesian and cylindrical meshes for detailed scoring; and ( 4) efficient Monte Carlo navigation in CAD geometries. JLAB will finish automation of simulations of residual dose in CAD geometries and integrate Tech-X software into Geant4 and MARS15. Finally, Tech-X will develop a Graphical User Interface to set up and heal CAD geometries, create input files, run and visualize simulations for residual dose. This application will run on local desktops, local and remote clusters and supercomputers and will be made available through public clouds, such as Amazon Web Services.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗