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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A JSON to CMake tool

As part of the conversion of the MCNP®testing system to CTest, a tool was needed to generate CMake variables from JSON formatted data structure. The system was first implemented using the JSON to CMake parser which with some slight modifications provided data useful for the MCNP CTest testing system. Upon further evaluation, we found it to be slow when setting up the 1600 tests during the CMake configuration step. Not only was it slow, it did not validate the JSON files during configuration allowing for unexpected behavior. Upon some web searching we came across a simple recursive python example that would provide the same capability needed by our testing system. This python example was effectively wrapped into a single python script that was used to convert the JSON data into CMake variables. This tool is attached as json_to_cmake.py and is listed in the appendix. In essence, the tool provides single variables for each value defined within the JSON file. The name for the variable is constructed by using the associated object names and array indicies needed to deference the variable itself. In the following sections, examples are provided to better describe how this is done.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

NGEE Arctic Plant Traits: Vegetation Plot Locations, Ecotypes, and Photos, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, 2016

This dataset provides field site documentation for related datasets including GPS waypoints for intensive plots, reference points, vegetation plots, and soil temperature/moisture monitoring stations that were established in July 2016 at the Kougarok hill slope located at Kougarok Road, Mile Marker 64. At all intensive plots and reference points, photographs of the surrounding area were also taken using an Olympus Stylus TG-3 digital camera. Photographs were July 2nd through July 7th looking in each of the four cardinal directions. GPS waypoints were taken using a Garmin eTrek 10 handheld GPS and World Geodetic System 1984 (WGS-84). The error associated with the Garmin GPS coordinates was 9 foot or approximately 3 m. When marking waypoints, users waited until the instrument reached this error level before recording. Coordinate units are decimal degrees. This dataset is comprised of one comma-separated (*.csv) file containing GPS waypoints locating intensive plots, reference points, soil temperature/moisture monitoring stations, and vegetation plots. The dataset also contains one folder of photographs (*.jpg) taken at intensive stations, one folder of photographs (*.jpg) taken at reference points, and one User file (*.pdf). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

SAM Code Enhancements for Fission Product Tracking of Noble Gases and Metals in MSRs

This report documents fiscal year 2026 enhancements to the System Analysis Module (SAM) for modeling fission product transport in liquid-fueled molten salt reactors (MSRs). The work advances three principal areas: noble gas transport, noble metal deposition, and user interface improvements. The noble gas transport capability integrates drift-flux gas transport, Henry’s law two-film interphase mass transfer with pressure-based nucleation suppression, Knudsen-regime pore diffusion into porous graphite with a conjugate salt-graphite interface constraint, built-in material properties, five Sherwood-number mass transfer correlations including three derived from high-fidelity NekRS simulations, and xenon-135 reactivity feedback through SAM’s point-kinetics model. This work also presents a comprehensive verification test suite, including new analytically verified cases for pressure-dependent onset of interphase gas transfer in a stagnant vertical pipe, a postulated FLiBe-graphite Xe extraction permeator, a gravity riser with a fission-product source, and a descending pipe with gas redissolution driven by hydrostatic pressure. A machine learning framework for bubble rise velocity prediction in molten salt systems is developed and benchmarked on molten-salt and diverse aqueous bubble datasets. The best-performing fine-tuned transfer-learning networks achieve an 82% reduction in RMSE relative to the Clift correlation, and is implemented directly in SAM. The noble metal transport capability is developed, including a liquid-wall deposition model and a gas-surface flotation mechanism that transfers insoluble particles entrained by sparging gas to wetted structures. Verification tests and demonstration cases cover the surface deposition, flotation efflux, and flotation shedding. Finally, a new [SpeciesTransport] input structure replaces positional global vectors with selfcontained, order-independent, named species blocks, simplifies the specification of multiphase species and decay chains, and remains fully compatible with existing SAM input files. Together, these developments improve the physical fidelity, verification basis, and usability of SAM for system-level analyses of fissionproduct behavior in MSRs.

Mui, Travis (ORCID:0000000303736470)↗
DOE OSTI2021

Solar-to-Grid Public Data File for Utility-scale (UPV) and Distributed Photovoltaics (DPV) Generation, Capacity Credit, and Value for 2012-2020

Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI, covering the years 2012-2020. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission.

annual solar value↗
DOE OSTI2026

Supervisory Control and Data Acquisition for Electrochemical Separation Experimentation

The Python-based program is a laboratory automation tool designed to control and monitor electrochemical systems. The tool was developed for capacitive deionization (CDI) experiments, but it can be used for any system that requires controlled voltage or current segments and multi-parameter monitoring. The program integrates hardware components to run user-defined experimental parameters, providing operational control of a programmable power supply, peristaltic pump, and data acquisition devices. Currently, the program is structured with a workflow that includes an initialization (or pre-run) phase, a main loop, and a post-experiment stabilization (or post-run) phase. The initialization phase prepares and stabilizes the cell, ensuring that the electrodes and solution reach a baseline state before the experiment begins. The main loop consists of multiple voltage segments that repeat, controlling the experiment while recording key parameters such as time, voltage, current, pH, and conductivity. Finally, the post-experiment stabilization phase allows the system to stabilize after the experiment, returning the cell and solution to equilibrium conditions before ending the sequence. The program is designed with four variations, each tailored to different experimental needs. All variations include both the initialization and post-experiment stabilization stages, which run for a set amount of time, voltage, current, and flow rate before and after the main experiment block. The main loop runs for a set number of cycles, as defined by the user input, and each cycle is composed of 2 or 4 segments. The 4 program variations are described as follows: Program 1: The main program includes 2 segments. Each segment is defined to have a set duration, flow rate, voltage, and current. This program measures conductivity, flow rate, voltage, and current. Program 2: The main program expands Program 1 to include 4 segments. Each segment has a specified duration, flow rate, voltage, and current. Like Program 1, it measures conductivity, flow rate, voltage, and current. Program 3: The main program consists of 2 segments, each defined by time, flow rate, voltage, and current. In addition to conductivity, flow rate, voltage, and current, Program 3 collects pH and temperature data through a 4-channel data acquisition device. Program 4: This program independently controls two channels of a multi-channel power supply simultaneously. While conductivity can only be measured for one cell at a time, the dual-channel control makes it possible to operate two cells simultaneously under different voltage/current conditions. The main program includes 2 segments.For each program, all measurements are automatically logged and integrated into a single Excel output file. Data are displayed in numerical format and plotted, both in real time, to track system performance. A key feature of the program is its ability to synchronize all outputs so that every measurement shares a single timestamp, ensuring accurate alignment of voltage, current, pH, conductivity, and pH data.By combining hardware control, real-time monitoring, and unified data collection, this program significantly reduces manual workload and minimizes errors, making it a reliable platform for researchers, engineers, and laboratory technicians conducting CDI experiments, among other electrochemical tests.

Valentino, Lauren [Argonne National Laboratory (AN↗
DOE OSTI2022

Viability of S3 Object Storage for the ASC Program at Sandia

Recent efforts at Sandia such as DataSEA are creating search engines that enable analysts to query the institution’s massive archive of simulation and experiment data. The benefit of this work is that analysts will be able to retrieve all historical information about a system component that the institution has amassed over the years and make better-informed decisions in current work. As DataSEA gains momentum, it faces multiple technical challenges relating to capacity storage. From a raw capacity perspective, data producers will rapidly overwhelm the system with massive amounts of data. From an accessibility perspective, analysts will expect to be able to retrieve any portion of the bulk data, from any system on the enterprise network. Sandia’s Institutional Computing is mitigating storage problems at the enterprise level by procuring new capacity storage systems that can be accessed from anywhere on the enterprise network. These systems use the simple storage service, or S3, API for data transfers. While S3 uses objects instead of files, users can access it from their desktops or Sandia’s high-performance computing (HPC) platforms. S3 is particularly well suited for bulk storage in DataSEA, as datasets can be decomposed into object that can be referenced and retrieved individually, as needed by an analyst. In this report we describe our experiences working with S3 storage and provide information about how developers can leverage Sandia’s current systems. We present performance results from two sets of experiments. First, we measure S3 throughput when exchanging data between four different HPC platforms and two different enterprise S3 storage systems on the Sandia Restricted Network (SRN). Second, we measure the performance of S3 when communicating with a custom-built Ceph storage system that was constructed from HPC components. Overall, while S3 storage is significantly slower than traditional HPC storage, it provides significant accessibility benefits that will be valuable for archiving and exploiting historical data. There are multiple opportunities that arise from this work, including enhancing DataSEA to leverage S3 for bulk storage and adding native S3 support to Sandia’s IOSS library.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Label-based Virtual Directories In dCache

Traditional filesystems organize data in directories. These directories are typically a collection of files whose grouping is based on a single criterion, e.g., the starting date of an experiment, experiment name, beamline ID, measurement device, or instrument. However, each file in a directory can belong to several logical groups, such as a special event type, experiment condition, or a part of a selected dataset. dCache is a storage system developed to store large amounts of scientific data, used by many HEP and Photon Science experiments. With recent developments in dCache, we have introduced a concept of file tagging, which dynamically groups files with the same label into virtual directories. The file labels can be added, removed, renamed, and deleted through the admin interface or via REST API. The files in virtual directories are exposed through all protocols supported by dCache. This contribution will describe the details of the implementation for file tagging in dCache and present our future development plans on automatic metadata extractions, a feature that will significantly simplify data management. Additionally, we are exploring the future use of virtual directories as a way to translate scientific data catalogs into filesystem views for direct data analysis.

Sahakyan, Marina [DESY]↗
DOE OSTI2015

Quantifying Interannual Variability for Photovoltaic Systems in PVWatts

The National Renewable Energy Laboratory's (NREL's) PVWatts is a relatively simple tool used by industry and individuals alike to easily estimate the amount of energy a photovoltaic (PV) system will produce throughout the course of a typical year. PVWatts Version 5 has previously been shown to be able to reasonably represent an operating system's output when provided with concurrent weather data, however this type of data is not available when estimating system output during future time frames. For this purpose PVWatts uses weather data from typical meteorological year (TMY) datasets which are available on the NREL website. The TMY files represent a statistically 'typical' year which by definition excludes anomalous weather patterns and as a result may not provide sufficient quantification of project risk to the financial community. It was therefore desired to quantify the interannual variability associated with TMY files in order to improve the understanding of risk associated with these projects. To begin to understand the interannual variability of a PV project, we simulated two archetypal PV system designs, which are common in the PV industry, in PVWatts using the NSRDB's 1961-1990 historical dataset. This dataset contains measured hourly weather data and spans the thirty years from 1961-1990 for 239 locations in the United States. To note, this historical dataset was used to compose the TMY2 dataset. Using the results of these simulations we computed several statistical metrics which may be of interest to the financial community and normalized the results with respect to the TMY energy prediction at each location, so that these results could be easily translated to similar systems. This report briefly describes the simulation process used and the statistical methodology employed for this project, but otherwise focuses mainly on a sample of our results. A short discussion of these results is also provided. It is our hope that this quantification of the interannual variability of PV systems will provide a starting point for variability considerations in future PV system designs and investigations. however this type of data is not available when estimating system output during future time frames.

14 SOLAR ENERGY↗
DOE OSTI2015

SU-E-J-182: Reproducibility of Tumor Motion Probability Distribution Function in Stereotactic Body Radiation Therapy of Lung Using Real-Time Tumor-Tracking Radiotherapy System

Purpose: We aim to achieve new four-dimensional radiotherapy (4DRT) using the next generation real-time tumor-tracking (RTRT) system and flattening-filter-free techniques. To achieve new 4DRT, it is necessary to understand the respiratory motion of tumor. The purposes of this study were: 1.To develop the respiratory motion analysis tool using log files. 2.To evaluate the reproducibility of tumor motion probability distribution function (PDF) during stereotactic body RT (SBRT) of lung tumor. Methods: Seven patients having fiducial markers closely implanted to the lung tumor were enrolled in this study. The positions of fiducial markers were measured using the RTRT system (Mitsubishi Electronics Co., JP) and recorded as two types of log files during the course of SBRT. For each patients, tumor motion range and tumor motion PDFs in left-right (LR), anterior-posterior (AP) and superior-inferior (SI) directions were calculated using log files of all beams per fraction (PDFn). Fractional PDF reproducibility (Rn) was calculated as Kullback-Leibler (KL) divergence between PDF1 and PDFn of tumor motion. The mean of Rn (Rm) was calculated for each patient and correlated to the patient’s mean tumor motion range (Am). The change of Rm during the course of SBRT was also evluated. These analyses were performed using in-house developed software. Results: The Rm were 0.19 (0.07–0.30), 0.14 (0.07–0.32) and 0.16 (0.09–0.28) in LR, AP and SI directions, respectively. The Am were 5.11 mm (2.58–9.99 mm), 7.81 mm (2.87–15.57 mm) and 11.26 mm (3.80–21.27 mm) in LR, AP and SI directions, respectively. The PDF reproducibility decreased as the tumor motion range increased in AP and SI direction. That decreased slightly through the course of RT in SI direction. Conclusion: We developed the respiratory motion analysis tool for 4DRT using log files and quantified the range and reproducibility of respiratory motion for lung tumors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

SU-F-T-288: Impact of Trajectory Log Files for Clarkson-Based Independent Dose Verification of IMRT and VMAT

Purpose: To investigate the effect of the trajectory files from linear accelerator for Clarkson-based independent dose verification in IMRT and VMAT plans. Methods: A CT-based independent dose verification software (Simple MU Analysis: SMU, Triangle Products, Japan) with a Clarksonbased algorithm was modified to calculate dose using the trajectory log files. Eclipse with the three techniques of step and shoot (SS), sliding window (SW) and Rapid Arc (RA) was used as treatment planning system (TPS). In this study, clinically approved IMRT and VMAT plans for prostate and head and neck (HN) at two institutions were retrospectively analyzed to assess the dose deviation between DICOM-RT plan (PL) and trajectory log file (TJ). An additional analysis was performed to evaluate MLC error detection capability of SMU when the trajectory log files was modified by adding systematic errors (0.2, 0.5, 1.0 mm) and random errors (5, 10, 30 mm) to actual MLC position. Results: The dose deviations for prostate and HN in the two sites were 0.0% and 0.0% in SS, 0.1±0.0%, 0.1±0.1% in SW and 0.6±0.5%, 0.7±0.9% in RA, respectively. The MLC error detection capability shows the plans for HN IMRT were the most sensitive and 0.2 mm of systematic error affected 0.7% dose deviation on average. Effect of the MLC random error did not affect dose error. Conclusion: The use of trajectory log files including actual information of MLC location, gantry angle, etc should be more effective for an independent verification. The tolerance level for the secondary check using the trajectory file may be similar to that of the verification using DICOM-RT plan file. From the view of the resolution of MLC positional error detection, the secondary check could detect the MLC position error corresponding to the treatment sites and techniques. This research is partially supported by Japan Agency for Medical Research and Development (AMED)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A high-fidelity residential building occupancy detection dataset

Abstract This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable information; indoor environmental readings, captured every ten seconds; and ground truth binary occupancy status. The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. The environmental modalities are available as captured, but to preserve the privacy and identity of the occupants, images were downsized and audio files went through a series of processing steps, as described in this paper. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

SetEnvironment

SAND2025-11727O SetEnvironment software is a utility library module that uses ConfigParserEnhanced, also a library module, to read specifically formatted .ini files for setting the user's environment. It creates a consistent environment with an expected set of variables and module loads, which is important for testing. Environment variable operations include set, append, prepend, unset, remove, and others and uses the Python os.environ methods. The module operations are performed with a wrapper. Systems using the Lmod package can generate a module function, which can also perform module operations specified in the text file. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

McLendon III, William↗
DOE OSTI2020

Requirements Document for a Replacement Data Acquisition System

A flight vehicle is in the developmental stages. While in development flight testing is required by both the manufacturer and customer to rate design performance. The performance of the vehicle must fulfill the agreed upon requirements. Testing of a flight vehicle during operation requires various sensors in order to record and monitor performance for evaluation. A Data Acquisition System (DAQ) is needed in order to control and capture data from various sensors placed throughout the vehicle during flight. This DAQ will receive the analog data from the suite of sensors and convert them to a digital signal that can be transmitted to a recording station where the data will later be analyzed by test experts. Some of the key features to be included in the DAQ will be a self-check operation to verify the health of all sensors including the DAQ. The DAQ will also have the capability to store sensor calibration data files loaded during assembly and transmit those files to the recording station during flight. Breakthroughs in new technologies has allowed for higher resolution recording systems and faster sampling rates. Improved sensor data using a new Data Acquisition System (DAQ) can provide important answers to questions discovered during the last few flight tests. These questions arose when the flight system expressed unexpected behaviors. Having the capability of the new DAQ in flight tests would allow for more high-fidelity data to be captured from the system during flight tests and ultimately reducing the number of required flight test. Reducing the number of flight tests also reduce developmental cost and schedule.

42 ENGINEERING↗
DOE OSTI2019

Surface Water: Stage, Temperature and Discharge, Teller Road Mile Marker 27, Seward Peninsula, Alaska, 2016-2021

Surface water data were collected at one location in the Teller Mile 27 (TL_MM27) Research Basin Site, Seward Peninsula (N64 43' 35.29649", W165 56' 48.42823"). The site was installed and measurements collected from April 2016 to September 2021. During the pandemic, a combination of environmental factors (both animals and climate) led to a gap in the data record from late 2019 to June 2021. Station restarted in June 2021 with the same basin outlet and instrumentation type. These data are being collected to better understand the hydrology of this research basin. The data could also be used as supporting measurements for other research and modeling activities. Stream stage and water temperature are measured every 10 minutes. Discharge in the stream is also measured every site visit using one of two different methods. Most discharge measurements in this data set use the classic US Geological Survey (USGS) style wading rod to measure water velocity at multiple points (every 5 or 10 centimeters) across a known cross-section (https://pubs.usgs.gov/tm/tm3-a8/pdf/tm3-a8.pdf). The second method is the salt-slug mass diffusion method (https://www.uvm.edu/bwrl/lab_docs/protocols/2005_Moore_Slug_salt_dilution_gauging_volumetric_method_Streamline.pdf). Combining the continuous data record with the discrete observations, a stage-discharge relationship was developed. Dataset contains 14 *.csv files including a data dictionary and file-level metadata plus one user guide *.pdf file. DATASET UPDATE: The 2016-2019 data were published in 2019. Data collection continued through 2021 and were added to the dataset in 2025. There were no changes to the 2016-2019 data. Data files now have all years for a measurement combined into one file; whereas before, each measurement and each year had an individual file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Post-fire soil respiration in late growing season (2023 and 2024), Kougarok Fire Complex, Seward Peninsula, Alaska

Field soil respiration data collected in 2023 and 2024 from burned and unburned tussock tundra sites in the Kougarok Fire Complex, near Nome, on the Seward Peninsula of Alaska. Specifically, we measured soil properties and late-growing season CO2 fluxes in patches of unique plant functional types (forbs, shrubs, and graminoids) across two years in tundra recovering from repeated wildfires over the decade. The goal was to identify the main drivers of soil respiration in Arctic tundra underlain by discontinuous permafrost that is recovering from two recent, repeated wildfires that differed in fire age and number of times burned, thereby resulting in different levels of vegetation and subsurface property changes (i.e., successional trajectories). There are five files in *.csv format with one data file and four data description files including data, dictionary, methods, terminology, and file-level metadata. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), is a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic Phase 3 project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Santos, Fernanda [ORNL] (ORCID:0000000191555623)↗
DOE OSTI2023

Utilization of ACE nuclear data file toolkit ACEtk to calculate relative sensitivity coefficients of point-kinetics parameters

Sensitivity and uncertainty methods are quintessential for nuclear criticality safety and experiment design. This type of analysis relies on calculations of sensitivity coefficients; sensitivity coefficients of the effective neutron multiplication factor with respect to some nuclear data are predominantly calculated and used. As a part of the Laboratory Directed Research & Development project EUCLID (Experiments Underpinned by Computational Learning for Improvements in nuclear Data) at Los Alamos National Laboratory, sensitivity coefficients of many radiation detector measurement responses with respect to nuclear data were investigated. Specifically, this paper outlines a method to calculate point-kinetics parameters relative sensitivity coefficients with respect to nuclear data. Point-kinetics parameters such as the prompt neutron decay constant, effective delayed neutron fraction, and neutron generation time are especially important to experimenters and reactor operators designing systems with dynamic neutron populations. This method couples capabilities of the ACE (A Compact ENDF) nuclear data file toolkit, ACEtk, with the ability to load cross sections into the radiation transport code Monte Carlo N-Particle (MCNP). In conclusion, key aspects of optimizing this method for a particular application and sensitivity profiles of the Jezebel criticality experiment are examined and discussed.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Workflows Community Summit 2022: A Roadmap Revolution

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from the execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large-scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC, enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enable the publication of workflows and their associated products according to the FAIR principles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

TU-AB-BRC-07: Efficiency of An IAEA Phase-Space Source for a Low Energy X-Ray Tube Using Egs++

Purpose: To extend the capability of the EGSnrc C++ class library (egs++) to write and read IAEA phase-space files as a particle source, and to assess the relative efficiency gain in dose calculation using an IAEA phase-space source for modelling a miniature low energy x-ray source. Methods: We created a new ausgab object to score particles exiting a user-defined geometry and write them to an IAEA phase-space file. A new particle source was created to read from IAEA phase-space data. With these tools, a phase-space file was generated for particles exiting a miniature 50 kVp x-ray tube (The INTRABEAM System, Carl Zeiss). The phase-space source was validated by comparing calculated PDDs with a full electron source simulation of the INTRABEAM. The dose calculation efficiency gain of the phase-space source was determined relative to the full simulation. The efficiency gain as a function of i) depth in water, and ii) job parallelization was investigated. Results: The phase-space and electron source PDDs were found to agree to 0.5% RMS, comparable to statistical uncertainties. The use of a phase-space source for the INTRABEAM led to a relative efficiency gain of greater than 20 over the full electron source simulation, with an increase of up to a factor of 196. The efficiency gain was found to decrease with depth in water, due to the influence of scattering. Job parallelization (across 2 to 256 cores) was not found to have any detrimental effect on efficiency gain. Conclusion: A set of tools has been developed for writing and reading IAEA phase-space files, which can be used with any egs++ user code. For simulation of a low energy x-ray tube, the use of a phase-space source was found to increase the relative dose calculation efficiency by factor of up to 196. The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant No. 432290).

60 APPLIED LIFE SCIENCES↗