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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

A METHOD TO SEARCH FOR CORRELATIONS OF ULTRA-HIGH ENERGY COSMIC-RAY MASSES WITH THE LARGE-SCALE STRUCTURES IN THE LOCAL GALAXY DENSITY FIELD

One of the main goals of investigations using present and future giant extensive air shower (EAS) arrays is the mass composition of ultra-high energy cosmic rays (UHECRs). A new approach to the problem is presented, combining the analysis of arrival directions with the statistical test of the paired EAS samples. One of the ideas of the method is to search for possible correlations between UHECR masses and their separate sources; for instance, if there are two sources in different areas of the celestial sphere injecting different nuclei, but the fluxes are comparable so that arrival directions are isotropic, then the aim is to reveal a difference in the mass composition of cosmic-ray fluxes. The method is based on a non-parametric statistical test-the Wilcoxon signed-rank routine-which does not depend on the populations fitting any parameterized distributions. Two particular algorithms are proposed: first, using measurements of the depth of the EAS maximum position in the atmosphere; and second, relying on the age variance of air showers initiated by different primary particles. The formulated method is applied to the Yakutsk array data, in order to demonstrate the possibility of searching for a difference in average mass composition between the two UHECR sets, arriving particularly from the supergalactic plane and a complementary region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A Type–Curve Approach for Evaluating Aquifer Properties by Interpreting Shallow Strain Measured During Well Tests

Strains occur at shallow depths in response to pressure changes during well tests in an underlying aquifer, and recent developments in instrumentation have made it feasible to measure essentially the full strain tensor. Simulations using poroelastic analyses indicate that shallow normal strains are approximately proportional to the logarithm of time when a well is injecting into or pumping from a deep aquifer or reservoir. The drawdown is also a linear function of log time, as shown by the classic Cooper-Jacob type-curve analysis. The time when the semilog straight line intercepts the zero-strain axis is similar to the time determined from the Cooper-Jacob pressure analysis, and it can be used to estimate hydraulic diffusivity, suggesting that horizontal strain data can be used directly to estimate aquifer properties. This approach was validated using measurements from shallow (30-m deep) borehole strainmeters during an injection test at a 530-m-deep sandstone aquifer/reservoir in Oklahoma. The results show intercept times for the shallow normal strain data are essentially the same as for deep pressure data from an equivalent radial distance. The slopes of the semilog plots of the pressure and the strain increase at the same time, suggesting that they both respond to a lateral aquifer boundary. Significantly, though, strain was measured at shallow depths while the pressure data were measured at 530-m depth. This suggests that strain data from shallow depths could be an effective way to improve the characterization of an underlying aquifer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

INVESTIGATION OF SYSTEMATIC EFFECTS IN KEPLER DATA: SEASONAL VARIATIONS IN THE LIGHT CURVE OF HAT-P-7b

With years of Kepler data currently available, the measurement of variations in planetary transit depths over time can now be attempted. To do so, it is of primary importance to understand which systematic effects may affect the measurement of transits. We aim to measure the stability of Kepler measurements over years of observations. We present a study of the depth of about 500 transit events of the Hot Jupiter HAT-P-7b, using 14 quarters (Q0-Q13) of data from the Kepler satellite. We find a systematic variation in the depth of the primary transit, related to quarters of data and recurring yearly. These seasonal variations are about 1%. Within seasons, we find no evidence for trends. We speculate that the cause of the seasonal variations could be unknown field crowding or instrumental artifacts. Our results show that care must be taken when combining transits throughout different quarters of Kepler data. Measuring the relative planetary radius of HAT-P-7b without taking these systematic effects into account leads to unrealistically low error estimates. This effect could be present in all Kepler targets. If so, relative radius measurements of all Hot Jupiters to a precision much better than 1% are unrealistic.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

COVID-19 biomarkers based on respiratory microbiome content

COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Spitzer ultra faint survey program (surfs up). I. An overview

Spitzer UltRa Faint SUrvey Program is a joint Spitzer and Hubble Space Telescope Exploration Science program using 10 galaxy clusters as cosmic telescopes to study z ≳ 7 galaxies at intrinsically lower luminosities, enabled by gravitational lensing, than blank field surveys of the same exposure time. Our main goal is to measure stellar masses and ages of these galaxies, which are the most likely sources of the ionizing photons that drive reionization. Accurate knowledge of the star formation density and star formation history at this epoch is necessary to determine whether these galaxies indeed reionized the universe. Determination of the stellar masses and ages requires measuring rest-frame optical light, which only Spitzer can probe for sources at z ≳ 7, for a large enough sample of typical galaxies. Our program consists of 550 hr of Spitzer/IRAC imaging covering 10 galaxy clusters with very well-known mass distributions, making them extremely precise cosmic telescopes. We combine our data with archival observations to obtain mosaics with ∼30 hr exposure time in both 3.6 μm and 4.5 μm in the central 4' × 4' field and ∼15 hr in the flanking fields. This results in 3σ sensitivity limits of ∼26.6 and ∼26.2 AB magnitudes for the central field in the IRAC 3.6 and 4.5 μm bands, respectively. To illustrate the survey strategy and characteristics we introduce the sample, present the details of the data reduction and demonstrate that these data are sufficient for in-depth studies of z ≳ 7 sources (using a z = 9.5 galaxy behind MACS J1149.5+2223 as an example). For the first cluster of the survey (the Bullet Cluster) we have released all high-level data mosaics and IRAC empirical point-spread function models. In the future we plan to release these data products for the entire survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

KilonovaNet : Surrogate models of kilonova spectra with conditional variational autoencoders

ABSTRACT Detailed radiative transfer simulations of kilonova spectra play an essential role in multimessenger astrophysics. Using the simulation results in parameter inference studies requires building a surrogate model from the simulation outputs to use in algorithms requiring sampling. In this work, we present kilonovanet, an implementation of conditional variational autoencoders (cVAEs) for the construction of surrogate models of kilonova spectra. This method can be trained on spectra directly, removing overhead time of pre-processing spectra, and greatly speeds up parameter inference time. We build surrogate models of three state-of-the-art kilonova simulation data sets and present in-depth surrogate error evaluation methods, which can in general be applied to any surrogate construction method. By creating synthetic photometric observations from the spectral surrogate, we perform parameter inference for the observed light-curve data of GW170817 and compare the results with previous analyses. Given the speed with which kilonovanet performs during parameter inference, it will serve as a useful tool in future gravitational wave observing runs to quickly analyse potential kilonova candidates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Probing the Wind Component of Radio Emission in Luminous High-redshift Quasars

We discuss a probe of the contribution of wind-related shocks to the radio emission in otherwise radio-quiet quasars. Given (1) the nonlinear correlation between UV and X-ray luminosity in quasars, (2) that such a correlation leads to higher likelihood of radiation-line-driven winds in more luminous quasars, and (3) that luminous quasars are more abundant at high redshift, deep radio observations of high-redshift quasars are needed to probe potential contributions from accretion disk winds. We target a sample of 50 z ≃ 1.65 color-selected quasars that span the range of expected accretion disk wind properties as traced by broad C iv emission. 3 GHz observations with the Very Large Array to an rms of ≈10 μJy beam -1 probe to star formation rates of ~400 M ⊙ yr -1 , leading to 22 detections. Supplementing these pointed observations are survey data of 388 sources from the LOFAR Two-meter Sky Survey Data Release 1 that reach comparable depth (for a typical radio spectral index), where 123 sources are detected. These combined observations reveal a radio detection fraction that is a nonlinear function of C iv emission-line properties and suggest that the data may require multiple origins of radio emission in radio-quiet quasars. We find evidence for radio emission from weak jets or coronae in radio-quiet quasars with low Eddington ratios, with either (or both) star formation and accretion disk winds playing an important role in optically luminous quasars and correlated with increasing Eddington ratio. Additional pointed radio observations are needed to fully establish the nature of radio emission in radio-quiet quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

SU-E-T-253: Development of a GDPM Monte Carlo Based Quality Assurance Tool for Cyberknife

Purpose: To develop a 3D quality assurance (QA) tool for Cyberknife plans using a GPU-based Monte Carlo dose calculation package, gDPM. The developed QA tool will be used as a secondary check for Cyberknife treatment planning software (TPS) reported dose distributions. Methods: Phase space files of the 5, 7.5, 10, 15, and 60 mm iris cones were downloaded from IAEA phase-space database. From the 60mm iris cone phase space file, we were able to generate raw phase space files for all other cones using a particle rejection method. The particles in the raw phase space files were binned with respect to the radial position and energy for commissioning. During the commissioning process, gDPM calculated percent depth dose and off-center ratios which were compared to those measured in the Blue Phantom. The off-axis data was measured with an SAD setup at depths of 15, 50, 100, 200, and 300 mm. At the commissioning stage, an optimization problem was solved to adjust the binned particles weights to minimize the difference between the calculated and measured ones. Commissioning validations will be performed by measuring point dose data of patient specific plans delivered to water phantoms. With commissioned phase space files, 3D patient-specific dose distributions will be calculated and compared against TPS reported dose. Results: Beyond the initial buildup region, the root mean square difference between the calculated and measured percent depth dose differences was less than 0.5%. The full-width half-max data from the off-axis ratio calculations was found to be within 0.1 mm of the measured data. Conclusion: Our current work showed excellent agreement between the gDPM calculated dose and the measured data for all cone sizes and types for the Cyberknife system. Implementation of a fast and accurate QA tool for patient specific plans will be feasible with this tool.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Raw tensiometer soil matric potential data, from Oct 2017-Jun 2020 in Manaus, Brazil

This data package contains raw output from two Campbell Scientific CR1000 data loggers each connected to three UMS T4e tensiometer sensors in Manaus, Brazil. Tensiometers at each data logger location were installed to depths of 10 cm, 20 cm and 50 cm below the ground surface for the purpose of measuring soil matric potential at each depth. “Station 3” tensiometers were installed at approximately 2°36'31.68"S, 60°12'34.70"W. “Station 4” tensiometers were installed at approximately 2°36'32.46"S, 60°12'34.92"W. The files “tensio3.edit.202109.csv” and “tensio4.edit.202109.csv” contain raw data (with invalid data removed). A text editor will be required to view the raw files in .csv (comma-separated values) format. Data processing was limited to: 1) Editing column headers for clarity; 2) Applying a correction that converts voltage read by the data loggers to pressure units (kPa); 3) removing all data greater than 50 kPa and less than -115 kPa; 4) Adding columns entitled “Caution.10cm”, “Caution.20cm” and “Caution.50cm” that contains values of either “NA” or “Caution”, the latter of which is applied when data exceeds the manufacturer reported data validity range of less than -85 kPa to suggest use of these data with caution; 5) Adding columns entitled “Invalid.10cm”, “Invalid.20cm”, and “Invalid.50cm” that contains values of either “NA” or “Invalid”, the latter of which is applied when data accuracy is deemed compromised due to the introduction of air bubbles into the instrument. Data marked “Invalid” may still indicate general trends of wet/high matric potential and dry/low matric potential, but values are inaccurate and dry/low matric potential values are likely not low enough. Wet/high matric potential values are likely to be more accurate than dry/low matric potential values; 6) Data format changed from .dat to .csv. This research was performed as a part of the NGEE Tropics project, which aims to advance model predictions of tropical forest carbon cycle responses to a changing climate over the 21st Century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Deep Look at the Ultra-Faint Milky Way Satellite Virgo III with Rubin Observatory Data Preview 2

We analyze the ultra-faint Milky Way satellite Virgo III using data from the Vera C. Rubin Observatory Data Preview 2 (DP2). Virgo III was observed in the Rubin "Cosmic Treasure Chest" (M49) First Look field, which contains 924 visits in the u,g,r,i bands comprising ~10.5hrs of exposure time with LSSTCam. These data are considerably deeper than the majority of DP2, with a $5σ$ limiting magnitude that approaches the expected 10-year depth of LSST (~25.2-26.5mag, depending on band). We report the morphological and stellar population parameters of Virgo III measured with the maximum-likelihood-based package ugali. The depth of the Rubin imaging yields more than a factor of four increase in the number of candidate member stars ($N_* = 114^{+11}_{-11}$) relative to the Virgo III discovery results ($N_* = 25^{+5}_{-4}$), enabling significantly more precise morphological constraints. Our best-fit parameters broadly agree with previous measurements, further confirming that Virgo III has properties that are consistent with an ultra-faint dwarf galaxy ($M_V = -2.72^{+0.49}_{-0.70}$; $r_{1/2} = 53^{+10}_{-8}$) located at a heliocentric distance of $D_\odot = 151^{+8}_{-8}$. We also demonstrate that the depth and photometric quality of the DP2 data are sufficient to separate metal-poor and metal-rich stars in color-color space. We further present period estimates for the three known RR Lyrae member stars derived from the DP2 forced photometry; we use theoretical Period-Luminosity-Metallicity (PLZ) and Period-Wesenheit-Metallicity (PWZ) relations to obtain independent distance estimates. We find that our period and distance estimates are broadly consistent with previous measurements for these RR Lyrae. These results demonstrate the power of LSST data for the discovery and characterization of ultra-faint dwarf galaxies and motivate future searches for new satellites across the southern sky.

Pai, Aashay [Chicago U.; Chicago U., KICP; SkAI, C↗
DOE OSTI2016

SU-F-T-460: Dosimetric Matching Between Trilogy Tx and TrueBeam STx

Purpose: To compare the commissioned beam data for one flattening filter photon mode (6 MV) and two flattening filter-free (FFF) photon modes (6 and 10 MV-FFF) between Trilogy Tx and TrueBeam STx and evaluate the possibility of dosimetric matching Methods: Dosimetric characteristics of the new Trilogy Tx including percent depth doses (PDDs), profiles, and output factors were measured for commissioning. Linear diode array detector and ion chambers were used to measure dosimetric data. The depth of dose maximum (dmax) and PDD at 10 cm (PDD10) were evaluated: 3×3 cm{sup 2}, 10×10 cm{sup 2}, and 40×40 cm{sup 2}. The beam profiles were compared and then penumbras were evaluated. As a further test of the dosimetric matching, the same VMAT plans were delivered, measured with film, and compared with TPS calculation. Results: All the measured PDDs matched well across the two units. PDD10 showed less than 0.5% variation and dmax were within 1.5 mm at the field sizes evaluated. Within the central 80% of transverse axis, profile data were almost identical. TrueBeam data resulted in a slightly greater penumbra width (up to 1.9 mm). The greatest differences of output factors were found at 40 × 40 cm{sup 2}: 2.40%, 2.03%, and 2.22% for 6 MV, 6 MV-FFF, and 10 MV-FFF, respectively. For smaller field sizes, less than 1% differences were observed. The film measurements demonstrated over 97.3% pixels passing-gamma analysis (2%/2mm). The results showed excellent agreement between measurements of two machines. Conclusion: The differences between Trilogy Tx and TrueBeam STx found could possibly affect small field and also very large field sizes in dosimetric matching considerations. These differences encountered are mostly related with the changes in the head design of the TrueBeam. Although it cannot guarantee full interchangeability of two machines, dosimetric matching by field size of 25 × 25 cm{sup 2} might be clinically acceptable.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗
DOE OSTI2015

SU-E-T-405: Evaluation of the Raystation Electron Monte Carlo Algorithm for Varian Linear Accelerators

Purpose: To evaluate the Raystation v4.51 Electron Monte Carlo algorithm for Varian Trilogy, IX and 2100 series linear accelerators and commission for clinical use. Methods: Seventy two water and forty air scans were acquired with a water tank in the form of profiles and depth doses, as requested by vendor. Data was imported into Rayphysics beam modeling module. Energy spectrum was modeled using seven parameters. Contamination photons were modeled using five parameters. Source phase space was modeled using six parameters. Calculations were performed in clinical version 4.51 and percent depth dose curves and profiles were extracted to be compared to water tank measurements. Sensitivity tests were performed for all parameters. Grid size and particle histories were evaluated per energy for statistical uncertainty performance. Results: Model accuracy for air profiles is poor in the shoulder and penumbra region. However, model accuracy for water scans is acceptable. All energies and cones are within 2%/2mm for 90% of the points evaluated. Source phase space parameters have a cumulative effect. To achieve distributions with satisfactory smoothness level a 0.1cm grid and 3,000,000 particle histories were used for commissioning calculations. Calculation time was approximately 3 hours per energy. Conclusion: Raystation electron Monte Carlo is acceptable for clinical use for the Varian accelerators listed. Results are inferior to Elekta Electron Monte Carlo modeling. Known issues were reported to Raysearch and will be resolved in upcoming releases. Auto-modeling is limited to open cone depth dose curves and needs expansion.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Intra-canopy leaf trait variation facilitates high LAI and compensatory growth in a clonal woody encroaching shrub in the tallgrass prairie

This data package contains the data for Tooley et al. (2022) Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub. The purpose of this project was to evaluate the leaf ecophysiological traits of Cornus drummondii C.A. Mey to better understand how C. drummondii maximizes photosynthetic rates across large gradients of light availability in its short, dense canopies. We also evaluate how C. drummondii modifies its allocation of leaf traits in response to grassland disturbance. High LAI canopies and a compensatory growth response to disturbance are two key factors contributing to the success of C. drummondii and other woody species responsible for the encroachment of grasslands.The data package contains ecophysiological measurements, including leaf morphological, physiological, and chemical traits; photosynthetic measurements, leaf carbon and nitrogen stable isotopes, leaf area index, and photosynthetically active radiation. All measurements were taken vertically across canopies of the clonal woody-encroaching shrub Cornus drummondii under browsed, grazed, and control (unbrowsed and ungrazed) conditions.Files:SeasonalMeasurements_Archive.csv - Data for all seasonal measurements used in the study.LeafPhotoSeasonalRaw_Archive.zip - zip file containing raw data from an li-6400 for each sampling period in the study.LAI_Archive.xlsx - Data for LAI Measurements.ACiandLightCurveResults_Archive.xlsx - Contains parameters extracted from A-ci and Light Response curves along with the morphological and chemical traits for each leaf.ACiRaw Archive.xlsx - Raw data from Li-6400 for A-ci response curvesLightCurves_Raw.zip - zip file containing raw data for Li-6400 light response curves. Excel file format: "IslandID"-"Depth"-lc.xlsx (or .csv)DataCollectionMethods.docx - file containing the methods of data collection, processing, and QA/QC for all data files. File also contains a list of excel file headers, abbreviations, and their units.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-T-577: Commissioning of a Deterministic Algorithm for External Photon Beams

Purpose: We report commissioning results for a deterministic algorithm for external photon beam treatment planning. A deterministic algorithm solves the radiation transport equations directly using a finite difference method, thus improve the accuracy of dose calculation, particularly under heterogeneous conditions with results similar to that of Monte Carlo (MC) simulation. Methods: Commissioning data for photon energies 6 – 15 MV includes the percentage depth dose (PDD) measured at SSD = 90 cm and output ratio in water (Spc), both normalized to 10 cm depth, for field sizes between 2 and 40 cm and depths between 0 and 40 cm. Off-axis ratio (OAR) for the same set of field sizes was used at 5 depths (dmax, 5, 10, 20, 30 cm). The final model was compared with the commissioning data as well as additional benchmark data. The benchmark data includes dose per MU determined for 17 points for SSD between 80 and 110 cm, depth between 5 and 20 cm, and lateral offset of up to 16.5 cm. Relative comparisons were made in a heterogeneous phantom made of cork and solid water. Results: Compared to the commissioning beam data, the agreement are generally better than 2% with large errors (up to 13%) observed in the buildup regions of the FDD and penumbra regions of the OAR profiles. The overall mean standard deviation is 0.04% when all data are taken into account. Compared to the benchmark data, the agreements are generally better than 2%. Relative comparison in heterogeneous phantom is in general better than 4%. Conclusion: A commercial deterministic algorithm was commissioned for megavoltage photon beams. In a homogeneous medium, the agreement between the algorithm and measurement at the benchmark points is generally better than 2%. The dose accuracy for a deterministic algorithm is better than a convolution algorithm in heterogeneous medium.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

In-Depth Analysis of Simulation Engine Codes for Comparison with DOE's Roof Savings Calculator and Measured Data

The Roof Savings Calculator (RSC) was developed through collaborations among Oak Ridge National Laboratory (ORNL), White Box Technologies, Lawrence Berkeley National Laboratory (LBNL), and the Environmental Protection Agency in the context of a California Energy Commission Public Interest Energy Research project to make cool-color roofing materials a market reality. The RSC website and a simulation engine validated against demonstration homes were developed to replace the liberal DOE Cool Roof Calculator and the conservative EPA Energy Star Roofing Calculator, which reported different roof savings estimates. A preliminary analysis arrived at a tentative explanation for why RSC results differed from previous LBNL studies and provided guidance for future analysis in the comparison of four simulation programs (doe2attic, DOE-2.1E, EnergyPlus, and MicroPas), including heat exchange between the attic surfaces (principally the roof and ceiling) and the resulting heat flows through the ceiling to the building below. The results were consolidated in an ORNL technical report, ORNL/TM-2013/501. This report is an in-depth inter-comparison of four programs with detailed measured data from an experimental facility operated by ORNL in South Carolina in which different segments of the attic had different roof and attic systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Planck intermediate results

We present the NPIPE processing pipeline, which produces calibrated frequency maps in temperature and polarization from data from the Planck Low Frequency Instrument (LFI) and High Frequency Instrument (HFI) using high-performance computers. NPIPE represents a natural evolution of previous Planck analysis efforts, and combines some of the most powerful features of the separate LFI and HFI analysis pipelines. For example, following the LFI 2018 processing procedure, NPIPE uses foreground polarization priors during the calibration stage in order to break scanning-induced degeneracies. Similarly, NPIPE employs the HFI 2018 time-domain processing methodology to correct for bandpass mismatch at all frequencies. In addition, NPIPE introduces several improvements, including, but not limited to: inclusion of the 8% of data collected during repointing manoeuvres; smoothing of the LFI reference load data streams; in-flight estimation of detector polarization parameters; and construction of maximally independent detector-set split maps. For component-separation purposes, important improvements include: maps that retain the CMB Solar dipole, allowing for high-precision relative calibration in higher-level analyses; well-defined single-detector maps, allowing for robust CO extraction; and HFI temperature maps between 217 and 857 GHz that are binned into 0'.9 pixels (Nside = 4096), ensuring that the full angular information in the data is represented in the maps even at the highest Planck resolutions. The net effect of these improvements is lower levels of noise and systematics in both frequency and component maps at essentially all angular scales, as well as notably improved internal consistency between the various frequency channels. Based on the NPIPE maps, we present the first estimate of the Solar dipole determined through component separation across all nine Planck frequencies. The amplitude is (3366.6 ± 2.7) μK, consistent with, albeit slightly higher than, earlier estimates. From the large-scale polarization data, we derive an updated estimate of the optical depth of reionization of τ = 0.051 ± 0.006, which appears robust with respect to data and sky cuts. There are 600 complete signal, noise and systematics simulations of the full-frequency and detector-set maps. As a Planck first, these simulations include full time-domain processing of the beam-convolved CMB anisotropies. The release of NPIPE maps and simulations is accompanied with a complete suite of raw and processed time-ordered data and the software, scripts, auxiliary data, and parameter files needed to improve further on the analysis and to run matching simulations.

79 ASTRONOMY AND ASTROPHYSICS↗