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33 Shafts Category of Transuranic Waste Stored Below Ground within Area G

This report compiles information to support the evaluation of alternatives and analysis of regulatory paths forward for the 33 shafts. The historical information includes a form completed by waste generators for each waste package (Reference 6) that included a waste description, estimates of Pu-239 and uranium-235 (U-235) based on an accounting technique, and calculations of mixed fission products (MFP) based on radiation measurements. A 1979 letter and questionnaire (Reference 7) provides information on waste packaging of hot cell waste and the configuration of disposal shafts as storage in the 33 Shafts was initiated. Tables of data by waste package were developed during a review of historical documents that was performed in 2005 (Reference 8). Radiological data was coupled with material-type data to estimate the initial isotopic content of each waste package and an Oak Ridge National Laboratory computer code was used to calculate 2009 decay levels. Other sources of information include a waste disposal logbook for the 33 shafts (Reference 9), reports that summarize remote-handled waste generated at the CMR facility (Reference 10) and placement of waste in the 33 shafts (Reference 11), a report on decommissioning of the LAMPRE reactor (Reference 12), interviews with an employee and manager involved in placing waste in the 33 shafts (References 13 and 14), an interview with a long-time LANL employee involved in waste operations (Reference 15), a 2002 plan for disposition of remote-handled TRU waste (Reference 16), and photographs obtained during field surveys of several shafts in 2007. The WIPP Central Characterization Project (CCP) completed an Acceptable Knowledge (AK) summary report for 16 canisters of remote-handled waste from the CMR Facility that contains information relevant to the 33 Shafts on hot-cell operations and timeline (Reference 17).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

A Mercury Model of Atmospheric Transport

Using the particle transport code Mercury, accurate models were built of the two sources used in Operation BREN, a series of radiation experiments performed by the United States during the 1960s. In the future, these models will be used to validate Mercury’s ability to simulate atmospheric transport.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

COVID-19: Spatiotemporal social data analytics and machine learning for pandemic exploration and forecasting

This task focused on developing a preliminary approach to use machine learning (ML) to explore the relationship between county-level societal variables and COVID-19 parameters, including COVID-19 cases rates and counts and COVID-19 death rates and counts. The objective was to develop and test a prototype approach for linking COVID-19 and county-level data. The task focused on enhancing and applying existing LANL ML techniques to COVID-19. Our novel ML methods have been a subject of a recently approved U.S. patent. The codes based on these methods are already open-source released. Our ML tools (NMFk/NTFk) are applied to extract hidden features (signals, waves) in the analyzed datasets and automatically identify their optimal number. The features are extracted by identifying counties that have similarities between the county-level societal variables and the COVID-19 parameters. These demonstration analyses will facilitate the ongoing pandemic simulations and predictions performed by Los Alamos other institutions, as well as lay the groundwork for future work.

60 APPLIED LIFE SCIENCES↗

SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals

Current neutrino detectors will observe hundreds to thousands of neutrinos from Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between supernova simulations and the corresponding signals in neutrino detectors, which will make any comparison between theory and observation very difficult. SNEWPY is an open-source software package that bridges this gap. The SNEWPY code can interface with supernova simulation data to generate from the model either a time series of neutrino spectral fluences at Earth, or the total time-integrated spectral fluence. Data from several hundred simulations of core-collapse, thermonuclear, and pair-instability supernovae is included in the package. This output may then be used by an event generator such as sntools or an event rate calculator such as the SuperNova Observatories with General Long Baseline Experiment Simulator (SNOwGLoBES). Additional routines in the SNEWPY package automate the processing of the generated data through the SNOwGLoBES software and collate its output into the observable channels of each detector. In this paper we describe the contents of the package, the physics behind SNEWPY, the organization of the code, and provide examples of how to make use of its capabilities.

79 ASTRONOMY AND ASTROPHYSICS↗

NCSU Flux Tower Data

NCSU Flux Tower Data Level b1: QC checks applied to measurements Data Format: CSV Description: See "instrument descripotion Site: Houston, TX; Tracking Aerosol Convection interactions ExpeRiment (HOU) Location: Houston, TX; AMF1 (main site for TRACER) Facility Code: M1 Category: Aerosol Properties Data Type: PI Data Source Instrument/Data: Sonic Anemometer, 3 Condensation Particle Counters, 1 SP2, 1 POPS Start Date: 2022-06-01 End Date: 2022-9-26 Contact PI: Markus Petters (mdpetter@ncsu.edu) Funding Source: DOE ASR award US Department of Energy, Office of Science, Biological and Environment Research (grant no. DE-SC 0021074) Instrument description The NCSU flux tower was located next to the AMF sampling pad. The flux tower consisted of a 10 m telescoping tower. Mounted at the top of the tower was a sonic anemometer (RM Young 8000) and a Krypton Hygrometer. A sample line (¼” conductive tubing) and communication line was laid to a trailer located underneath and slightly adjacent to the tower. The sample line brought aerosol inside the trailer at 4.5 L min-1, where it was distributed between 3 CPCs (TSI 3776c, Dc ~ 2.5 nm, TSI 3771, Dc ~10nm, and TSI 3772, Dc ~40 nm) with different size cuts. Also sampling was a printed particle optical spectrometer (POPS) and a single particle soot photometer (SP2). Please contact mdpetter@ncsu.edu for further information.

54 ENVIRONMENTAL SCIENCES↗

NCSU RDMA data

NCSU RDMA Data Level b1: QC checks applied to measurements Data Format: CSV Description: See "instrument description Site: Houston, TX; Tracking Aerosol Convection interactions ExpeRiment (HOU) Location: Houston, TX; AMF1 (main site for TRACER) Facility Code: M1 Category: Aerosol Properties Data Type: PI Data Source Instrument/Data: Nano-Scanning Mobility Particle (SMPS); A Radial Differential Mobility Analyzer (RDMA) coupled with 1 Condensation Particle Counter Start Date: 2022-06-01 End Date: 2022-9-26 Contact PI: Markus Petters (mdpetter@ncsu.edu) Funding Source: DOE ASR award US Department of Energy, Office of Science, Biological and Environment Research (grant no. DE-SC 0021074) Instrument description The NCSU RDMA was operated at a sheath-to-sample flow ratio of 5:1.5 L min−1. The RDMA was configured to scan from 5 to 55 nm. It was located into the temperature-controlled trailer adjacent to the NCSU Flux Tower to observe size distributions of the aerosols.The sample line was dried with three silica-gel driers in series, and then neutralized with X-ray neutralizer. Please contact mdpetter@ncsu.edu for further information.

54 ENVIRONMENTAL SCIENCES↗

NCSU HTDMA data

NCSU HTDMA data Data Level b1: QC checks applied to measurements Data Format: CSV Description: See "instrument description Site: Houston, TX; Tracking Aerosol Convection interactions ExpeRiment (HOU) Location: Houston, TX; AMF1 (main site for TRACER) Facility Code: M1 Category: Aerosol Properties Data Type: PI Data Source Instrument/Data: Humidified Tandem Differential Mobility Analyzer; 2 Differential Mobility Analyzers (DMA1 and DMA2). Start Date: 2022-06-01 End Date: 2022-09-26 Contact PI: Markus Petters (mdpetter@ncsu.edu) Funding Source: DOE ASR award US Department of Energy, Office of Science, Biological and Environment Research (grant no. DE-SC 0021074) Instrument description The NCSU HTDMA was operated at a sheath-to-sample flow ratio of 5:1 L min−1. The HTDMA was configured to measure hygroscopic growth factors of dry particles with mobility diameters of D = 15, 20, 30, 40, and 50 nm at RH ~ 70%. A complete cycle for all diameters took ~30 minutes. A sample line brought aerosol inside the trailer at 2.5 L min-1, where it was distributed between NCSU RDMA (1.5 L min-1) and NCSU HTDMA (1 L min-1) lines. The sample line was dried with three silica-gel driers in series, and then neutralized with X-ray neutralizer. The sample line entered DMA1 (operated as an electrostatic classifier). Monodisperse particles with certain fractions were humidified with temperature controlled Nafion membrane immersed in water before entering DMA2 (operated in scanning mobility particle sizer). Please contact mdpetter@ncsu.edu for further information.

54 ENVIRONMENTAL SCIENCES↗

NCSU Black Carbon (SP2) Data

NCSU SP2 Data Data Level b1: QC checks applied to measurements Data Format: CSV Description: See "instrument description Site: Houston, TX; Tracking Aerosol Convection interactions ExpeRiment (HOU) Location: Houston, TX; AMF1 (main site for TRACER) Facility Code: M1 Category: Aerosol Properties Data Type: PI Data Source Instrument/Data: Single Particle Soot Photometer (SP2) Start Date: 2022-06-01 End Date: 2022-09-27 Contact PI: Markus Petters (mdpetter@ncsu.edu) Funding Source: DOE ASR award US Department of Energy, Office of Science, Biological and Environment Research (grant no. DE-SC 0021074) Instrument description The SP2 was located in the temperature-controlled trailer adjacent to the NCSU Flux Tower to observe refractory black carbon (rBC) number concentration and mixing states of black carbon. The sample line brought aerosol inside the trailer at 4.5 L min-1, where it was distributed between 3 CPCs (TSI 3776c, Dc ~ 2.5 nm, TSI 3771, Dc ~10nm, and TSI 3772, Dc ~40 nm) with different size cuts, a printed particle optical spectrometer (POPS), and SP2. Please contact mdpetter@ncsu.edu for further information.

54 ENVIRONMENTAL SCIENCES↗

Calculating uncertainty on K-effective with MONK10 - 14583

Criticality safety assessments require a demonstration that a particular configuration of fissile material has an adequate sub-critical margin (k-effective sufficiently below unity) to ensure that the risk of criticality under normal operation and accident conditions is acceptable. The required sub-critical margin depends upon the uncertainty in the estimated value of k-effective. The uncertainty in the calculated value of k-effective arises from a number of sources, including: manufacturing tolerances on input data to the code (affecting geometry, compositions and densities), uncertainty in the nuclear data used by the code, stochastic uncertainty resulting from Monte Carlo simulation and modelling approximations/errors, including the inevitable bugs in the software. The ANSWERS Software Service, in collaboration with industrial partners, is developing a number of techniques to better understand and quantify uncertainty on predicted values of k-effective, using MONK. The SPRUCE utility code has been developed to allow uncertainty to be estimated using sampling methods. This can include the sampling of input parameters (including dimensions, compositions and densities) from statistical distributions. It can also include sampling different nuclear data libraries. A set of nuclear data libraries has been generated for this purpose by sampling from statistical distributions that represent the uncertainties in the published nuclear data evaluated files; a set of libraries has been produced for Latin Hypercube Sampling. By varying the input data and nuclear data, separate and combined uncertainties due to manufacturing tolerances and nuclear data can be derived. By performing least squares fitting on the results it is also possible to estimate the contribution of each of the uncertain inputs and a sensitivity method in MONK can break down the nuclear data uncertainty. (authors)

42 ENGINEERING↗

Transient Modelling of HTGR Thermal Load Follow in Modelica

The ability of nuclear power plants to load follow electrical power demand is particularly important as we move to higher variable renewable (VRE) energy production on low carbon energy grids. High temperature gas reactors (HTGRs) are capable of load following by adjusting thermal output, but their high temperatures and thermal inertia significantly impact the power transients they are able to follow. To aid investigation into new integrated energy systems with advanced reactors, it is necessary to have an open-source transient model of a full HTGR primary and secondary. This paper introduces two new open-source models for a HTGR and corresponding balance of plant (BOP) written for the HYBRID library developed at Idaho National Laboratory. A transient thermal load follow test case was taken by the work from Brits et al. to benchmark our open-source model against transient operation results from other proprietary codes that exist. The control scheme for the two new models in combination is discussed and a comparison of the response in transient load follow between the two codes performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

As-Run ATR Physics Evaluation for the UCF-1 HSIS Experiment

This Engineering Calculations and Analysis Report (ECAR) documents the Advanced Test Reactor (ATR) detailed MCNP full core model physics analysis performed to support the irradiation of the University of Central Florida (UCF-1) Hydraulic Shuttle Irradiation System Experiment in the ATR B-7 position. The purpose of this analysis is to calculate: • The as-run neutron and gamma heating rates for the experiment components (W/g); • The as-run DPA for the experiment specimens after 20 (test train 1) and 2 (test train 2) days of irradiation in the ATR B-7 position (neutrons/cm2-sec); • The radionuclide source term after 2 days and 20 days of irradiation in the ATR B-7 position; • The decay heat associated with the UCF-1 radionuclide source term. These calculations were performed using the computer codes MCNP and ORIGEN2. The neutronics model, analysis details, and results are presented in the attached report.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

WISE PHOTOMETRY FOR 400 MILLION SDSS SOURCES

We present photometry of images from the Wide-Field Infrared Survey Explorer (WISE) of over 400 million sources detected by the Sloan Digital Sky Survey (SDSS). We use a “forced photometry” technique, using measured SDSS source positions, star–galaxy classification, and galaxy profiles to define the sources whose fluxes are to be measured in the WISE images. We perform photometry with The Tractor image modeling code, working on our “unWISE” coaddds and taking account of the WISE point-spread function and a noise model. The result is a measurement of the flux of each SDSS source in each WISE band. Many sources have little flux in the WISE bands, so often the measurements we report are consistent with zero given our uncertainties. However, for many sources we get 3σ or 4σ measurements; these sources would not be reported by the “official” WISE pipeline and will not appear in the WISE catalog, yet they can be highly informative for some scientific questions. In addition, these small-signal measurements can be used in stacking analyses at the catalog level. The forced photometry approach has the advantage that we measure a consistent set of sources between SDSS and WISE, taking advantage of the resolution and depth of the SDSS images to interpret the WISE images; objects that are resolved in SDSS but blended together in WISE still have accurate measurements in our photometry. Our results, and the code used to produce them, are publicly available at http://unwise.me.

79 ASTRONOMY AND ASTROPHYSICS↗

Quasiclassical Computations of Compton-Scattered Spectra

Quality X-ray sources are crucial to fundamental physics research, medical radiology, humanities research, and materials science. While synchrotron radiation (SR) facilities produce the state-of-the-art emissions with respect to brilliance and frequency tunability, the great expense required to build, maintain, and operate these structures greatly limits their accessibility to researchers. Much of the research conducted at SR facilities, however, may be conducted with inverse Compton sources (ICS). Accelerator-based Compton scattering light sources generate high-energy, high-brilliance emissions. Compton scattering is the process by which a photon scatters o? an electron. ICS offer an affordable, in-lab alternative to SR facilities. Even though SR facilities produce greater intensity emissions, Compton sources provide the same frequency tunability at the intensities suitable for the purposes of many researchers currently fighting for time at SR facilities, i.e., ICS provides intensities suitable for contrast imaging, X-ray fluorescence, X-ray-diffraction, and X-ray spectroscopy. The focus of this work is to create computational models to simulate Compton-scattered spectra. These models have been used to build a theoretical basis for methods of improving the quality of future Compton sources and to preform diagnostic analysis of existing light sources. The theoretical basis of each model is derived from first principles. The numerical methods employed by each model are defined. A full description of the various functionalities of each code will be addressed. Furthermore, an in-depth analysis of spectral bandwidth sources is discussed. The complex physics arising from an extremely high-intensity, nonlinear laser pulse is explored in detail. Methods of frequency modulation of the incident laser, i.e., a method for correcting the nonlinear broadening effects on the scattered spectrum, will also be discussed. The work will conclude with a an exploration of the ongoing research efforts regarding regimes of operation outside of the limits of these current models.

Johnson, Erik↗

Design of an Yb-169 source optimized for gold nanoparticle-aided radiation therapy

Purpose: To find an optimum design of a new high-dose rate ytterbium (Yb)-169 brachytherapy source that would maximize the dose enhancement during gold nanoparticle-aided radiation therapy (GNRT), while meeting practical constraints for manufacturing a clinically relevant brachytherapy source. Methods: Four different Yb-169 source designs were considered in this investigation. The first three source models had a single encapsulation made of one of the following materials: aluminum, titanium, and stainless steel. The last source model adopted a dual encapsulation design with an inner aluminum capsule surrounding the Yb-core and an outer titanium capsule. Monte Carlo (MC) simulations using the Monte Carlo N-Particle code version 5 (MCNP5) were conducted initially to investigate the spectral changes caused by these four source designs and the associated variations in macroscopic dose enhancement across the tumor loaded with gold nanoparticles (GNPs) at 0.7% by weight. Subsequent MC simulations were performed using the EGSnrc and NOREC codes to determine the secondary electron spectra and microscopic dose enhancement as a result of irradiating the GNP-loaded tumor with the MCNP-calculated source spectra. Results: Effects of the source filter design were apparent in the current MC results. The intensity-weighted average energy of the Yb-169 source varied from 108.9 to 122.9 keV, as the source encapsulation material changed from aluminum to stainless steel. Accordingly, the macroscopic dose enhancement calculated at 1 cm away from the source changed from 51.0% to 45.3%. The sources encapsulated by titanium and aluminum/titanium combination showed similar levels of dose enhancement, 49.3% at 1 cm, and average energies of 113.0 and 112.3 keV, respectively. While the secondary electron spectra due to the investigated source designs appeared to look similar in general, some differences were noted especially in the low energy region (<50 keV) of the spectra suggesting the dependence of the photoelectron yield on the atomic number of source filter material, consistent with the macroscopic dose enhancement results. A similar trend was also shown in the so-called microscopic dose enhancement factor, for example, resulting in the maximum values of 138 and 119 for the titanium- and the stainless steel-encapsulated Yb-169 sources, respectively. Conclusions: The current results consistently show that the dose enhancement achievable from the Yb-169 source is closely related with the atomic number (Z) of source encapsulation material. While the observed range of improvement in the dose enhancement may be considered moderate after factoring all uncertainties in the MC results, the current study provides a reasonable support for the encapsulation of the Yb-core with lower-Z materials than stainless steel, for GNRT applications. Overall, the titanium capsule design can be favored over the aluminum or dual aluminum/titanium capsule designs, due to its superior structural integrity and improved safety during manufacturing and clinical use.

07 ISOTOPE AND RADIATION SOURCES↗

SU-F-19A-05: Experimental and Monte Carlo Characterization of the 1 Cm CivaString 103Pd Brachytherapy Source

Purpose: To determine the in-air azimuthal anisotropy and in-water dose distribution for the 1 cm length of the CivaString {sup 103}Pd brachytherapy source through measurements and Monte Carlo (MC) simulations. American Association of Physicists in Medicine Task Group No. 43 (TG-43) dosimetry parameters were also determined for this source. Methods: The in-air azimuthal anisotropy of the source was measured with a NaI scintillation detector and simulated with the MCNP5 radiation transport code. Measured and simulated results were normalized to their respective mean values and compared. The TG-43 dose-rate constant, line-source radial dose function, and 2D anisotropy function for this source were determined from LiF:Mg,Ti thermoluminescent dosimeter (TLD) measurements and MC simulations. The impact of {sup 103}Pd well-loading variability on the in-water dose distribution was investigated using MC simulations by comparing the dose distribution for a source model with four wells of equal strength to that for a source model with strengths increased by 1% for two of the four wells. Results: NaI scintillation detector measurements and MC simulations of the in-air azimuthal anisotropy showed that ≥95% of the normalized data were within 1.2% of the mean value. TLD measurements and MC simulations of the TG-43 dose-rate constant, line-source radial dose function, and 2D anisotropy function agreed to within the experimental TLD uncertainties (k=2). MC simulations showed that a 1% variability in {sup 103}Pd well-loading resulted in changes of <0.1%, <0.1%, and <0.3% in the TG-43 dose-rate constant, radial dose distribution, and polar dose distribution, respectively. Conclusion: The CivaString source has a high degree of azimuthal symmetry as indicated by the NaI scintillation detector measurements and MC simulations of the in-air azimuthal anisotropy. TG-43 dosimetry parameters for this source were determined from TLD measurements and MC simulations. {sup 103}Pd well-loading variability results in minimal variations in the in-water dose distribution according to MC simulations. This work was partially supported by CivaTech Oncology, Inc. through an educational grant for Joshua Reed, John Micka, Wesley Culberson, and Larry DeWerd and through research support for Mark Rivard.

07 ISOTOPE AND RADIATION SOURCES↗

Calculational Model of the Radiation Calibration Lab (RCL) and the Neutron Measurements performed at the Facility, with a 252 Cf Source

Calibration of detectors measuring gamma, neutron, alpha and beta radiation, and of personal dosimeters used at Lawrence Livermore National Laboratory (LLNL), is performed at the Radiation Calibration Lab (RCL). The calibrations done with a standard calibrated 252 Cf source are a function of the energy spectra, fluence and dose of the source. A detailed calculational model of the RCL has been carried out with the MCNP6 code. The calculations have also reproduced quite well the measurements of the fluence and dose rates for the bare and moderated 252 Cf source carried out by R. Radev using the Rospec Spectrometer and by Clark and Wilson using a Passive Neutron Spectrometer. The importance of having the calculational model of the RCL facility is to calculate the dose rates expected for a given planned measurement, optimizing the choice of detectors, design and shielding materials before the actual measurement are carried out, resulting in a more efficient and time saving measurement. Furthermore, an accurate model of the facility allows to detect possible problems with the measurement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SU-E-T-194: From Dicom-RT to Radiobiological Dose Metrics in 5 Minutes

Purpose: To develop a flexible and standalone framework for batch calculation of radiobiological dose metrics from Dicom-RT. Methods: Software has been developed which allows (1) The calculation of DVH data from DICOM dose and structure files (DVHgenerator), (2) Calculation of a wide range of radiobiological metrics from this data (CompPlanGui). Both these tools are run via graphical user interface (GUI), making them fast and simple. Part 1 is a new tool which has not previously been published, whilst part 2 is a GUI overlay for the previously published software ‘Comp-Plan’ (Holloway et. al., Medical Dosimetry, 2012), previously reliant on command line interface. The time taken for an experienced user to evaluate a test case of 6 plans with and without CompPlanGUI was quantified. Results: The DVH-generator has been found to be faster, more robust and require far less physical memory then using alternative software solutions for the same purpose. The Comp Plan GUI significantly reduces the amount of time required to set up a base directory, eliminates code crashes arising from typographical errors, and renders the code far more accessible to non-expert users. It took an experienced user of the code around 3 minutes to set up a base directory of 6 plans compared around 8 minutes without, indicating that using CompPlanGUI reduced setup time by over 50%. Conclusion: A standalone GUI based framework has developed which allows for the batch calculation of radiobiological dose metrics directly from Dicom-RT files. As with the original code, this work will be made freely available on request, as well as via matlab file exchange.

07 ISOTOPE AND RADIATION SOURCES↗

Effects of heat exchanger tubes on hydrodynamics and CO 2 capture of a sorbent-based fluidized bed reactor

In virtual design and scale up of pilot-scale carbon capture systems, the coupled reactive multiphase flow problem must be solved to predict the adsorber’s performance and capture efficiency under various operation conditions. This paper focuses on the detailed computational fluid dynamics (CFD) modeling of a pilot-scale fluidized bed adsorber equipped with vertical cooling tubes. Multiphase Flow with Interphase eXchanges (MFiX), an open-source multiphase flow CFD solver, is used for the simulations with custom code to simulate the chemical reactions and filtered models to capture the effect of the unresolved details in the coarser mesh for simulations with reasonable simulations and manageable computational effort. Previously developed two filtered models for horizontal cylinder drag, heat transfer, and reaction kinetics have been modified to derive the 2D filtered models representing vertical cylinders in the coarse-grid CFD simulations. The effects of the heat exchanger configurations (i.e., horizontal or vertical) on the adsorber’s hydrodynamics and CO2 capture performance are then examined. The simulation result subsequently is compared and contrasted with another predicted by a one-dimensional three-region process model.

bubbling bed↗