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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Opportunities for enhancing MLCommons efforts while leveraging insights from educational MLCommons earthquake benchmarks efforts

MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need for benchmark carpentry for scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection called cloudmesh with its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).

58 GEOSCIENCES↗
DOE OSTI2022

A physics-informed multi-agents model to predict thermo-oxidative/hydrolytic aging of elastomers

This paper introduces a novel physics-informed multi-agents constitutive model to propose prediction in quasi-static constitutive behavior of cross-linked elastomer and the loss of mechanical performance during environmental aging. The presented model is used to simulate the effect of single-mechanism chemical aging (i.e. thermal-inducedor hydrolytic aging) on the behavior of the material in this hybrid framework. Those environmental single-mechanism damages change the polymer matrix over time due to massive chain scission, chain formations, and changing the arrangement of molecules in the polymer matrix. Here we propose a data-driven super-constrained machine-learned engine to represent damage in the polymer matrix and capture the changes in material behavior, including its inelastic features such as Mullins effect and permanent set in the course of aging. We have simplified the 3D stress–strain tensor mapping problem into a small number of super-constrained 1D mapping problems by means of a sequential order reduction. An assembly of multiple replicated conditional neural-network learning-agents (L-agents) is trained to systematically simplify the high-dimensional mapping problem into multiple 1D problems, each represented by a different type of agent. Our hybrid framework is designed to capture the effect of deformation history, aging time, and aging temperature. The model is validated with respect to a comprehensive set of experiments specifically designed to benchmark model capabilities and also against available data in the literature. Thermodynamic consistency and frame independency have been verified. Besides acceptable predictive abilities, a significant reduction of computational cost to predict behavior at multiple states of deformation is the most significant feature of this model.

42 ENGINEERING↗
DOE OSTI2022

Predicting the recovery and grade of a rougher flotation circuit from liberation data

A flotation model has been validated against the survey data obtained from a copper rougher flotation circuit with a circulating load. Here, the model was developed using the hydrophobic force as a kinetic parameter, which made it possible to use the contact angles of the free and composite particles as input parameters so that the model can predict both recoveries and grades. The model also recognizes the role of bubble coarsening in determining the froth phase recoveries and grades. The model predictions are in good agreement with the survey data. The size-by-size recovery curve as predicted from the model shows an optimum particle size range that is consistent with most of the operating data reported in the literature. The model suggests that the upper particle size limit may be determined by the instability of the froth phase rather than the turbulence in the pulp phase of a flotation cell.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dark Energy Survey Year 1 results: the lensing imprint of cosmic voids on the cosmic microwave background

ABSTRACT Cosmic voids gravitationally lens the cosmic microwave background (CMB) radiation, resulting in a distinct imprint on degree scales. We use the simulated CMB lensing convergence map from the Marenostrum Institut de Ciencias de l’Espai (MICE) N-body simulation to calibrate our detection strategy for a given void definition and galaxy tracer density. We then identify cosmic voids in Dark Energy Survey (DES) Year 1 data and stack the Planck 2015 lensing convergence map on their locations, probing the consistency of simulated and observed void lensing signals. When fixing the shape of the stacked convergence profile to that calibrated from simulations, we find imprints at the 3σ significance level for various analysis choices. The best measurement strategies based on the MICE calibration process yield S/N ≈ 4 for DES Y1, and the best-fitting amplitude recovered from the data is consistent with expectations from MICE (A ≈ 1). Given these results as well as the agreement between them and N-body simulations, we conclude that the previously reported excess integrated Sachs–Wolfe (ISW) signal associated with cosmic voids in DES Y1 has no counterpart in the Planck CMB lensing map.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Prediction of Redox Potentials for U, Np, Pu, and Am in Aqueous Solution

The redox properties of the actinides in aqueous solution are important for fuel production/reprocessing and understanding the environmental impact of nuclear waste. The redox potentials for U, Np, Pu, and Am in oxidation states from 0 up to VII (as appropriate) in aqueous solutions have been predicted at the density functional theory level with the B3LYP functional, Stuttgart small core pseudopotential basis sets for the actinides, and explicit (30H 2 O molecules)/implicit treatment of the aqueous solvent using the self-consistent reaction field COSMO and SMD approaches for the implicit solvation. The predictions of the structural parameters of clusters incorporating first and second solvation shells are consistent with the available experimental data., Our results are typically within 0.2 V of the available experimental data using two explicit solvation shells with an implicit solvent model. The use of the PW91 functional substantially improved the prediction of the Pu(VI/V) redox couple. The redox couples for An(VI/IV) and An(V/IV) which involve the addition of protons and removal of the actinyl oxygens led to slightly larger differences from experiment. Here, the An(IV/0) and An(III/0) couples were reliably predicted with our approach. Predictions of the unknown An(II/I) redox potentials were negative, consistent with expectations, and predictions for unknown An(VII/VI), An(III/II), and An(II/0) redox couples improve prior estimates.

Actinides↗
DOE OSTI2024

Measurement report: A comparison of ground-level ice-nucleating-particle abundance and aerosol properties during autumn at contrasting marine and terrestrial locations

Abstract. Ice-nucleating particles (INPs) are an essential class of aerosols found worldwide that have far-reaching but poorly quantified climate feedback mechanisms through interaction with clouds and impacts on precipitation. These particles can have highly variable physicochemical properties in the atmosphere, and it is crucial to continuously monitor their long-term concentration relative to total ambient aerosol populations at a wide variety of sites to comprehensively understand aerosol–cloud interactions in the atmosphere. Hence, our study applied an in situ forced expansion cooling device to measure ambient INP concentrations and test its automated continuous measurements at atmospheric observatories, where complementary aerosol instruments are heavily equipped. Using collocated aerosol size, number, and composition measurements from these sites, we analyzed the correlation between sources and abundance of INPs in different environments. Toward this aim, we have measured ground-level INP concentrations at two contrasting sites, one in the Southern Great Plains (SGP) region of the United States with a substantial terrestrially influenced aerosol population and one in the Eastern North Atlantic Ocean (ENA) region with a primarily marine-influenced aerosol population. These measurements examined INPs mainly formed through immersion freezing and were performed at a ≤ 12 min resolution and with a wide range of heterogeneous freezing temperatures (Ts above −31 °C) for at least 45 d at each site. The associated INP data analysis was conducted in a consistent manner. We also explored the additional offline characterization of ambient aerosol particle samples from both locations in comparison to in situ data. From our ENA data, on average, INP abundance ranges from ≈ 1 to ≈ 20 L−1 (−30 °C ≤ T ≤ −20 °C) during October–November 2020. Backward air mass trajectories reveal a strong marine influence at ENA with 75.7 % of air masses originating over the Atlantic Ocean and 96.6 % of air masses traveling over open water, but analysis of particle chemistry suggests an additional INP source besides maritime aerosols (e.g., sea spray aerosols) at ENA. In contrast, 90.8 % of air masses at the SGP location originated from the North American continent, and 96.1 % of the time, these air masses traveled over land. As a result, organic-rich SGP aerosols from terrestrial sources exhibited notably high INP abundance from ≈ 1 to ≈ 100 L−1 (−30 °C ≤ T ≤ −15 °C) during October–November 2019. The probability density function of aerosol surface area-scaled immersion freezing efficiency (ice nucleation active surface site density; ns) was assessed for selected freezing temperatures. While the INP concentrations measured at SGP are higher than those of ENA, the ns(T) values of SGP (≈ 105 to ≈ 107 m−2 for −30 °C ≤ T ≤ −15 °C) are reciprocally lower than ENA for approximately 2 orders of magnitude (≈ 107 to ≈ 109 m−2 for −30 °C ≤ T ≤ −15 °C). The observed difference in ns(T) mainly stems from varied available aerosol surface areas, Saer, from two sites (Saer,SGP > Saer,ENA). INP parameterizations were developed as a function of examined freezing temperatures from SGP and ENA for our study periods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Physics-Informed Neural Network Solution of Point Kinetics Equations for a Nuclear Reactor Digital Twin

A digital twin (DT) for nuclear reactor monitoring can be implemented using either a differential equations-based physics model or a data-driven machine learning model. The challenge of a physics-model-based DT consists of achieving sufficient model fidelity to represent a complex experimental system, whereas the challenge of a data-driven DT consists of extensive training requirements and a potential lack of predictive ability. We investigate the performance of a hybrid approach, which is based on physics-informed neural networks (PINNs) that encode fundamental physical laws into the loss function of the neural network. We develop a PINN model to solve the point kinetic equations (PKEs), which are time-dependent, stiff, nonlinear, ordinary differential equations that constitute a nuclear reactor reduced-order model under the approximation of ignoring spatial dependence of the neutron flux. The PINN model solution of PKEs is developed to monitor the start-up transient of Purdue University Reactor Number One (PUR-1) using experimental parameters for the reactivity feedback schedule and the neutron source. The results demonstrate strong agreement between the PINN solution and finite difference numerical solution of PKEs. We investigate PINNs performance in both data interpolation and extrapolation. For the test cases considered, the extrapolation errors are comparable to those of interpolation predictions. Extrapolation accuracy decreases with increasing time interval.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Second Report of the Nuclear Data Subcommittee of the Nuclear Science Advisory Committee

The central importance of the nuclear data curated by the US Nuclear Data Program (USNDP) for clean energy generation, national security, nonproliferation, medical applications, and space exploration as well as basic science was described in a prior report issued by the DOE/NSF Nuclear Science Advisory Committee subcommittee on Nuclear Data (NSAC-ND) in September 2022. In this report, we present a set of fourteen (14) recommendations that will enhance and advance DOE-NP's stewardship of nuclear data. The first three recommendations focus on the existing core USNDP capabilities, namely: 1) Support the nuclear structure evaluation workforce to improve the currency, consistency, and accessibility of the Evaluated Nuclear Structure Data File (ENSDF); 2) Enhance nuclear reaction evaluation within the USNDP in support of the Evaluated Nuclear Data File (ENDF) through expansion of the workforce and integration of high-performance computing, automation, and machine learning and; 3) Continue atomic mass evaluation in support AME and NUBASE databases. This is followed by eight (8) recommendations representing new cross-cutting initiatives involving both measurement and evaluation to address outstanding nuclear data needs. These new initiatives require a highly trained, diverse workforce that includes personnel with expertise from both inside and outside the nuclear physics community from which evaluators have traditionally been recruited. As such, many of these initiatives are accomplished via a Topical Nuclear Data Collaborations (TNDC). A TNDC is made up of domestic and international stakeholders, subject matter and nuclear data experts, and nuclear data evaluators and features a workforce development plan to ensure that nuclear data evaluators maintain currency in the relevant applications and are seen as equity partners in the endeavor. These include: 1) Establish a coordinated effort to improve evaluation and modeling in nuclear astrophysics for stellar dynamics, multi-messenger astronomy and nucleosynthesis; 2) Initiate a TNDC to develop and maintain nuclear structure evaluation beyond discrete states, including nuclear level densities, photon strength functions and photonuclear data for improved reaction modeling, and exploring nuclear structure at finite temperature; 3) Create a TNDC to perform correlated fission data evaluation, including cross sections, fragment yields, v(A), v(E n ) for nuclear energy, national security, nonproliferation and basic science; 4) From a panel of subject matter experts to establish and annually update a roster of key decay data to nurture its accelerated dissemination including both measurement and evaluation for targeted high-value nuclides for national security, nonproliferation and medical applications; 5) Comprehensive, consistent neutron-induced structure and reaction data for nuclear energy, national security, nonproliferation and planetary nuclear spectroscopy; 6) Charged-particle stopping powers for detector design, space effects and ion beam therapy; 7) High-energy reactions for space exploration and medical nuclide production, and; 8) The creation of an infrastructure for open data and data preservation for use by the entire nuclear physics community. All told, these initiatives require approximately $6.5M increase in NP support of the USNDP in fiscal year 2023 dollars and would require at least 3-5 years to carry out due to the length of time needed to recruit and train new nuclear data researchers. This relatively modest investment would help ensure that the fruits of the nuclear data research carried out by DOE-NP and its collaborators would be brought to bear to address some of the most important needs of our nation and the world. To ensure effective execution of this plan, we present an overview of recruitment, training, and retention goals for the USNDP, the centerpiece of which is a mutually agreed upon code of conduct. Finally, we identify the facility and instrumentation needed to perform the recommended experimental activities. This includes a short review of target fabrication capabilities, reactors, neutron beam, light- and heavy-stable ion, gamma-ray, high-energy and radioactive ion beam facilities. Lastly, a more complete appendix of experimental facilities previously compiled is included with new input provided for 6 facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Verification and Validation Tests of Gamma Library of MC2-3 for Coupled Neutron and Gamma Heating Calculation

For the accurate assessment of the heat generation rate in fast reactors, the gamma library of MC2 -3 and the MC2 -3 + GAMSOR procedure employing the coupled neutron and gamma heating calculation has been thoroughly verified against Monte Carlo results and validated using the ZPPR-15D gamma dose measurement data. NJOY outputs are post-processed in a consistent way with the NJOY procedure to avoid any missing data or double counting of data. Prompt heating for 379 out of 391 isotopes in the gamma library was verified against MCNP6.2 to within 1% relative error in total heating for most isotopes. For both a simple one-dimensional slab problem representing a sodium cooled fast reactor and the Experimental Breeder Reactor II (EBR-II) benchmark problem, the root-mean-square values of assembly power error were less than 0.5% for fuel assemblies, ~1% in blankets and ~1 to ~3% in reflectors compared to MCNP6.2 results. The most plausible cause for the 3% error in a reflector assembly is believed to be the error in the multigroup neutron cross sections for the reflector assembly. For validation, gamma doses measured with thermoluminiscent dosimeters (TLDs) in the ZPPR-15D experiment were calculated using GAMSOR. Due to the uncertainty in the TLD measurement with regards to the energy deposition of photons and neutrons, the validation data leads to a 12.7% uncertainty on the experimental measurement. With this uncertainty bound, the calculated doses all fell within one standard deviation of the measured value. Combined with the accurate calculation of reaction rate distributions and neutron spectrum measurements, these results indicate good agreement for the neutron and gamma heating calculations that were performed.

coupled neutron and gamma heating↗
DOE OSTI2021

Measurement of the beam-normal single-spin asymmetry for elastic electron scattering from 12 C and 27 Al

We report measurements of the parity-conserving beam-normal single-spin elastic scattering asymmetries B n on 12 C and 27 Al, obtained with an electron beam polarized transverse to its momentum direction. These measurements add an additional kinematic point to a series of previous measurements of B n on 12 C and provide a first measurement on 27 Al. The experiment utilized the Q weak apparatus at Jefferson Lab with a beam energy of 1.158 GeV. The average lab scattering angle for both targets was 7.7°, and the average Q 2 for both targets was 0.02437 GeV 2 (Q = 0.1561 GeV). The asymmetries are B n = -10.68 ± 0.90 (stat) ± 0.57 (syst) ppm 12 C and B n = -12.16 ± 0.58 (stat) ± 0.62 (syst) ppm for 27 Al. The results are consistent with theoretical predictions, and are compared to existing data. When scaled by Z/A, the Q dependence of all the far-forward angle (θ < 10°) data from 1 H to 27 Al can be described by the same slope out to Q ≈ 0.35 GeV. Larger-angle data from other experiments in the same Q range are consistent with a slope about twice as steep.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Design and Operation of a Multi-Bed Catalytic Micro-Reactor for the Study of Co-Processing of Bio-Oils with VGO

An industry wide shift from fossil-based fuel to renewable fuel sources including biomass, municipal waste, and plastics will require new process monitoring methods to minimize transitional risks including off specification product formation and catalyst deactivation. This project aims to provide a machine learning based process monitoring tool composed of online, slipstream mass spectra for use in biomass refineries and co-processing in existing refineries allowing operators to monitor product qualities and adjust process conditions accordingly. In order to maximize the robustness of the tool, large volumes of data must be collected to fine tune model parameters which consists of both micro and pilot scale mass spectral data. Micro-scale data is collected with a multi-tube micro-reactor housing up to six catalysts in horizontal beds, coupled with a molecular beam mass spectrometer. A pyrolizer equipped with an auto-sampler streamlines the micro-scale data collection process. This type of pyrolizer/micro-reactor configuration does not exist on the market, and therefore had to be created for the purposes of this project. The design and commissioning of this reactor will be presented in detail. This reactor set-up is highly flexible and increases throughput of analysis. For catalyst testing, each bed can be individually selected simply by turning valves. For catalyst reduction and regeneration, simultaneous flow through all six beds is used. The reproducibility of the system was first assessed with whole biomass pyrolysis along with pyrolysis of calibration standards. Initial work on this system evaluated two FCC catalysts, equilibrium catalyst (E-cat), and a proprietary catalyst from Johnson Matthey specifically design for co-processing of bio-oil with vacuum gas oil (VGO). This work used model compounds and VGO which illuminated differences in products produced by the catalysts.

biomass↗
DOE OSTI2022

The LLNL nuclear data infrastructure for the GNDS data format

The next generation of nuclear data infrastructure tools at the Livermore National Laboratory (LLNL) consists of pipeline of codes that read and process nuclear data from evaluated files saved in the new GNDS (Generalised Nuclear Data Structure) nuclear data format. The processing code FUDGE (For Updating Data and Generating Evaluations) is at the front-end of this pipeline as it reads and process the evaluated data for use in downstream transport codes. FUDGE is Python based with C and C++ extensions for computationally intensive tasks. As is the case for the evaluated data, the processed output is also saved in the GNDS format and the GIDI+ API is provided as the interface between the processed data and the transport codes. GIDI+ is a C++ based suite of codes and it includes GIDI (General Interaction Data Interface), a library for reading and writing GNDS data, and MCGIDI which is the cross section lookup, and reaction and product distribution sampling interface between Monte Carlo transport codes and the GNDS data. GIDI provides methods for easy access to the multi-group processed GNDS data and this is demonstrated through its implementation in ARDRA, the LLNL deterministic transport code. The evaluation and sampling methods in MCGIDI are available as both CPU and GPU methods which facilitates the use of MCGIDI in both traditional CPU-based as well as the next generation mixed model computational architectures. This is demonstrated through the GIDI+ implementation in MERCURY, the LLNL Monte Carlo transport code. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. Here, we present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Direct astrophysical tests of chiral effective field theory at supranuclear densities

Recent observations of neutron stars with gravitational waves and X-ray timing provide unprecedented access to the equation of state (EoS) of cold dense matter at densities difficult to realize in terrestrial experiments. At the same time, predictions for the EoS equipped with reliable uncertainty estimates from chiral effective field theory ( x EFT) allow us to bound our theoretical ignorance. In this work, we analyze astrophysical data using a nonparametric representation of the neutron-star EoS conditioned on x EFT to directly constrain the underlying physical properties of the compact objects without introducing modeling systematics. We discuss how the data alone constrain the EoS at high densities when we condition on x EFT at low densities. Here, we also demonstrate how to exploit astrophysical data to directly test the predictions of x EFT for the EoS up to twice nuclear saturation density, in order to estimate the density at which these predictions might break down. We nd that the existence of massive pulsars, gravitational waves from GW170817, and NICER observations of PSR J0030+0451 favor x EFT predictions for the EoS up to nuclear saturation density over a more agnostic analysis by as much as a factor of 7 for the quantum Monte Carlo (QMC) calculations used in this work. While x EFT predictions using QMC are fully consistent with gravitational-wave data up to twice nuclear saturation density, NICER observations suggest that the EoS stiffens relative to these predictions at or slightly above nuclear saturation density. Additionally, for these QMC calculations, we marginalize over the uncertainty in the density at which x EFT begins to break down, constraining the radius of a 1.4M $\bigodot$ neutron star to R 1.4 = $11.40^{+1.38}_{–1.04}$ ($12.54^{+0.71}_{–0.63}$) km and the pressure at twice nuclear saturation density to p(2n sat ) = $14.2^{+18.1}_{–8.4}$ ($28.7^{+15.3}_{–15.0}$) MeV=fm 3 with massive pulsar and gravitational-wave (and NICER) data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

US Fallout Shelter

Being indoors (sheltering) reduces radiation exposures resulting from nuclear fallout or a power-plant accident. However, most US building types, particularly non-residential buildings, lack quantitative estimates. We provide here a high-level modeling analysis of modern US building protection based on a novel radiation protection building attribute taxonomy, a new building protection model, compilation of prior experimental results, and nationally representative building survey data. This approach provides a consistent, quantitative understanding of US building protection and, when combined with the distribution of people among different buildings, can assist in selecting emergency response strategies. We find that indoor radiation protection varies by orders of magnitude. Most people in non-residential buildings are adequately protected, particularly below ground or in the building center. Residential buildings are less protective and those with lighter weight walls lack adequate, above ground protection. This work also highlights key areas where further investigation will improve the current results. These include the improving the understanding of residential basement protection, additional experimental data on non-residential buildings (particularly schools and industrial buildings), and the frequency of brick veneer and stucco exterior residential buildings.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Real-space representation of the quasiparticle self-consistent GW self-energy and its application to defect calculations

The quasiparticle self-consistent (QS) GW (G for Green's function, W for screened Coulomb interaction) approach incorporates the corrections of the quasiparticle energies from their Kohn-Sham density functional theory (DFT) eigenvalues by means of an energy-independent and Hermitian self-energy matrix usually given in the basis set of the DFT eigenstates. By expanding these into an atom-centered basis set (specifically here the linearized muffin-tin orbitals) a real space representation of the self-energy corrections becomes possible. In this work, We show that this representation is relatively short-ranged. This offers opportunities to construct the self-energy of a complex system from parts of the system by a cut-and-paste method. Specifically for a point defect, represented in a large supercell, the self-energy can be constructed from those of the host and a smaller defect-containing cell. The self-energy of the periodic host can be constructed simply from a GW calculation for the primitive cell. We show for the case of the As Ga in GaAs that the defect part can already be well represented by a minimal eight-atom cell and allows us to construct the self-energy for a 64-atom cell in good agreement with direct QSGW calculations for the large cell. Using this approach to an even larger 216-atom cell shows the defect band approaches an isolated defect level. The calculations also allow us to identify a second defect band which appears as a resonance near the conduction band minimum. The results on the extracted defect levels agree well with Green's function calculations for an isolated defect and with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comparison of gamma-ray spectral analysis methods for thick-walled UF 6 cylinders

Three data analysis methods were compared for gamma ray spectra collected by planar and coaxial high-purity germanium detectors (HPGe) on thick-walled uranium hexafluoride cylinders. The cylinder contents range from natural to low-enriched uranium (4.95% 235 U). Each spectrum was analyzed with the traditional 185.7 keV enrichment meter method, a 185.7/1001 keV ratio method, and the Fixed Energy Response Function Analysis with Multiple Efficiency (FRAM) software that utilizes low- and high-energy gamma rays and X-rays. When results for all cylinders were compared, the enrichment meter method and the 185.7/1001 keV peak ratio method consistently provided the most accurate data for all detector types. The error on the enrichment calculations analyzed with FRAM did not meet the international target value whereas those calculated with the enrichment meter and ratio method did. Enrichments calculated by FRAM were found to skew to higher values on natural uranium and to lower values on low-enriched uranium. FRAM calculated the least-precise enrichments; replicate analyses of single cylinders resulted in 235 U values that varied by as much as 19%. FRAM does have the significant advantages in that it does not require the use of calibration cylinders, infinite thickness criteria are not necessary, and it can be applied to any HPGe detector. Finally, if calibration cylinders are available, the enrichment meter and 185.7/1001 peak ratio are the recommended methods as they are the most accurate and precise.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗