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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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DOE OSTI2016

A Conceptual World Information Library (WIL) and Land Use Information System (LUIS) - 16181

The Organization for Economic Co-operation and Development Nuclear Energy Agency initiative on the Preservation of Records, Knowledge and Memory is addressing the topic of communicating with future generations on the location, specific contents, and potential risks associated with deep geologic nuclear waste repositories. At the Waste Isolation Pilot Plant (WIPP) in the United States, the U.S. Department of Energy (DOE) has committed, prior to the facility closure, to develop a Passive Institutional Controls (PICs) program designed to last as long as practicable. A portion of this PICs program includes development of records packages, selection of data storage materials and methods, and a determination of records retention/storage locations. The approved plan for record retention at WIPP includes the storage of information including hard copies of data, figures and maps in multiple libraries and archive record centers. With this in mind, it is easy to envision reams of various paper documents in a robust physical archive record center similar to one of the museums in Washington D.C., or DOE's Legacy Management facility in Morgantown, West Virginia. While some portion of records for nuclear waste burial sites should be maintained in this fashion for near-term retrieval purposes, it is widely accepted that this is not an efficient way to communicate with future generations even 1,000 years from now. Transmitting large detailed volumes of information to the future should be captured in an electronic format; maintained on computers, translated by computers, and perhaps updated by artificial intelligence as machine and human languages change over the centuries. In addition to a physical records development and storage process for transmittal to future generations, a web-based electronic database system should be developed. A World Electronic Library (WEL) database could be funded from various nations and perhaps sanctioned by the United Nations and/or the IAEA. This WEL could contain information regarding several different subjects with the intent of being a one-stop-information-shop for future generations. This database would require updates and maintenance for as long as there is a recipient. One subset of the WEL would be a spatial database containing all the information available to any set of GPS coordinates, anywhere within the world. This Land Use Information System (LUIS) database would contain information on past and present land use, natural resources, known geology, hydrology and atmospheric conditions, potential warnings, and other pertinent information. The LUIS database is where specific electronic information, a subset of all the records data gathered for a nuclear waste repository, could reside. For not only would LUIS provide the information on a nuclear waste repository when specific GPS coordinates were entered; but a search from the LUIS on 'Nuclear Waste Repositories' could bring up information, including data on location of repositories, radionuclides, operations, D and D, maps, and regulations, plus many other details for locations throughout the world. This tool could be used by future generations for a number of land use or resource exploration decisions. So what can we do now? We can agree to the types of information that should be captured into an electronic database for nuclear waste repositories. We can agree to the levels of detail, the file names and the formats, for ease of combining information sets in the future. Perhaps we can agree to a central electronic data storage site, or sites, where this information will reside for import into a centralized electronic record system, if one is ever developed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Ground surface temperature derived Snow Cover Properties, Seward Peninsula, Alaska, 2019-2023

Snow-ground interface temperatures have been collected at the Teller mile marker 27 and Kougarok mile marker 64 field sites on the Seward Peninsula, Alaska from 2019 through 2023 (with data missing from Fall 2020 through Summer 2021 due to COVID). Temperatures were measured using iButton Link DS1921G-F5# Thermochron miniature temperature sensors and Tinytag TGP-4017 internal sensors deployed across the Kougarok 64 and Teller 27 field sites. These sensors are a cost-efficient way to collect snow-ground interface temperatures at a high spatial resolution, and when paired with air temperature data these measurements can provide insight into fine-scale variability in snowpack characteristics across the study sites. From this data, snow process metrics were calculated at each sensor location based on the methods outlined in Staub and Delaloye, 2017. Metrics are calculated daily for each sensor as well as over the entire season. These metrics include ground surface temperature (°C), the number of days under snow cover (number of days), the insulation effect of snow (unitless), the length of the transitional snow periods (number of days), as well as intermediaries such as temperature variability. Calculating these snow processes relies on the assumption that when snow covers a temperature sensor, it is buffered from diurnal fluctuations in air temperature by the insulating snow layer. More information on the calculated metrics can be found in the User Guide of this dataset, as well as in Staub and Delaloye’s 2017 publication Using Near-Surface Ground Temperature Data to Derive Snow Insulation and Melt Indices for Mountain Permafrost Applications. This dataset includes one daily and one seasonal *.csv file of metrics for every year of data, a daily and a seasonal *.csv data dictionary, and one User Guide document (*.pdf) describing data collection and processing.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 OSTI2018

Scalable Data Management of the Uintah Simulation Framework for Next-Generation Engineering Problems with Radiation

The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. This paper focuses on data management related problems faced by the Uintah simulation framework at a production scale of 260K processes. Uintah provides a highly scalable asynchronous many-task runtime system, which in this work is used for the modeling of a 1000 megawatt electric (MWe) ultra-supercritical (USC) coal boiler. At 260K processes, we faced both parallel I/O and visualization related challenges, e.g., the default file-per-process I/O approach of Uintah did not scale on Mira. In this paper we present a simple to implement, restructuring based parallel I/O technique. We impose a restructuring step that alters the distribution of data among processes. The goal is to distribute the dataset such that each process holds a larger chunk of data, which is then written to a file independently. This approach finds a middle ground between two of the most common parallel I/O schemes–file per process I/O and shared file I/O–in terms of both the total number of generated files, and the extent of communication involved during the data aggregation phase. To address scalability issues when visualizing the simulation data, we developed a lightweight renderer using OSPRay, which allows scientists to visualize the data interactively at high quality and make production movies. Finally, this work presents a highly efficient and scalable radiation model based on the sweeping method, which significantly outperforms previous approaches in Uintah, like discrete ordinates. The integrated approach allowed the USC boiler problem to run on 260K CPU cores on Mira.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Adiabatic Cooling System Spreadsheet Model for sCO 2

The attached cost and performance spreadsheet model is for an adiabatic cooling system, one of four cooling system models developed for use in optimizing indirectly-heated supercritical CO 2 (sCO 2 ) power cycles. Running the model requires that the user has REFPROP version 9 or below installed, as well as the Microsoft Excel REFPROP Add-In. Upon opening, use Excel’s Edit Links feature to link to the user’s installed REFPROP.xla or REFPROP.xlam file. For the spreadsheet model compatible with REFPROP version 10 and above, contact Nathan Weiland at nathan.weiland@netl.doe.gov. Technical documentation for the model can be found here: https://www.netl.doe.gov/energy-analysis/details?id=3199.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Direct Dry Cooling System Spreadsheet Model for sCO 2

The attached cost and performance spreadsheet model is for a direct dry cooling system, one of four cooling system models developed for use in optimizing indirectly-heated supercritical CO 2 (sCO 2 ) power cycles. Running the model requires that the user has REFPROP version 9 or below installed, as well as the Microsoft Excel REFPROP Add-In. Upon opening, use Excel’s Edit Links feature to link to the user’s installed REFPROP.xla or REFPROP.xlam file. For the spreadsheet model compatible with REFPROP version 10 and above, contact Nathan Weiland at nathan.weiland@netl.doe.gov. Technical documentation for the model can be found here: https://www.netl.doe.gov/energy-analysis/details?id=3199.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2018

Toward a smart data transfer node

Scientific computing systems are becoming significantly more complex, with distributed teams and complex workflows spanning resources from telescopes and light sources to fast networks and Internet of Things sensor systems. In such settings, no single, centralized administrative team and software stack can coordinate and manage all resources used by a single application. Indeed, we have reached a critical limit in manageability using current human-in-the-loop techniques. We therefore argue that resources must begin to respond automatically, adapting and tuning their behavior in response to observed properties of scientific workflows. Over time, machine learning methods can be used to identify effective strategies for autonomic, goal-driven management behaviors that can be applied end-to-end across the scientific computing landscape. Using the data transfer nodes that are widely deployed in modern research networks as an example, we explore the architecture, methods, and algorithms needed for a smart data transfer node to support future scientific computing systems that self-tune and self-manage.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Epi Archive: automated data collection of notifiable disease data

LANL has built a software program that automatically collects global notifiable disease data—particularly data stored in files—and makes it available and shareable within the Biosurveillance Ecosystem(BSVE) as a new data source. This will improve the prediction and early warning of disease events and other applications. Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health as exemplified by the Biosurveillance Ecosystem(BSVE).While most nations do likely store their data in a machine-readable format, the governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues. For example, an attempt by LANL to obtain a weekly version of openly available monthly data, reported by the Australian government, resulted in an onerous bureaucratic reply. The obstacles to obtaining data included: paperwork to request data from each of the Australian states and territories, a long delay to obtain data (up to 3 months) and extensive limitations on the data’s use that prohibit collaboration and sharing. This type of experience when attempting to contact public health departments or ministries of health for data is not uncommon. A survey conducted by LANL of notifiable disease data reporting in 52 countries identified only 10 as being machine-readable and 42 being reported in pdf files on a regular basis. Within the 42 nations that report in pdf files, 32 report in a structured, tabular format and10 in a non-structured way. As a result, LANL has developed a tool-Epi Archive (formerly known as EPIC)-to automatically and continuously collect global notifiable disease data and make it readily accessible. We conducted a survey of the national notifiable disease reporting systems notating how the data is reported in two important dimensions: date standards and case definitions. The development of software to regularly ingests notifiable disease data frand makes this data available involved four main steps scraping, extracting, parsing and persisting. For scraping: we would examine website designs and determine reporting mechanisms for each country/website as well as what varies across the reporting mechanisms. We then designed and wrote code to automate the downloading of report pdf files, for each country. We stored report pdfs along with appropriate metadata for extracting and parsing. For extracting: we developed software that can extract notifiable disease data presented in tabular form from a pdf file. We combined the methodology of figure placement detection with the in-house developed table extraction and annotation heuristics. For parsing: we determined what to extract from each pdf dataset from the survey conducted. We then parsed the extracted data into uniform data structures correctly accommodating the dimensions surveyed and the various human languages. This task involved ingesting notifiable disease data in many disparate formats extracted from pdf files and coalescing the data into a standardized format. For persisting: We then store the data in the Epi Archive PostgreSQL database and make it available through the BSVE. The EpiArchive tool currently contains subnational notifiable disease data from 10 nations. When a user accesses the EpiArchive site, they are prompted with four fields: country, region, disease, and date duration. These fields allow the user to specify the location(down to the state level), the disease of interest, and the duration of interest. Upon form submission, a time series is generated from the users’ specifications. The generated time series can then be downloaded into a csv file if a user is interested in performing personal analysis. Additionally, the data from EpiArchive can be reached through an API. LANL as part of a currently funded DTRA effort so that it will automatically and continuously collect global notifiable disease data—particularly data stored in pdf files—and make it available and shareable within the Biosurveillance Ecosystem (BSVE) as a new data source. This will provide data to analytics and users that will improve the prediction and early warning of disease events and other applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Improvements in Electron-Photon-Relaxation Data for MCNP6

The Los Alamos Monte Carlo transport code MCNP6 has recently benefitted from significant work to improve the photon-electron transport and atomic-relaxation capabilities of the code. The improvements include extension of existing photon data to lower energies than previously supported; addition of new data such as subshell-specific photoelectric cross sections; completion of the incoherent and coherent form factor data to cover all scattering angles at all supported energies; new data for electron interactions including Bremsstrahlung, elastic scattering, subshell-specific electroionization, and atomic excitation; a much more detailed representation for atomic relaxation; and an entirely new (to MCNP) approach to electron transport: the single-event method. These improvements were made available with the initial production release of MCNP6 along with an extended electron, photon, and relaxation data file eprdata12 to support the new capabilities. More recently new data have become available and have now been incorporated into MCNP6, correcting a few issues with the previous work, and adding new sets of cross sections that were not included in eprdata12. The purpose of this paper is to introduce these newer changes and enhancements, which will be included with the next public release of the MCNP6 code system. The database that supports these developments relies on an evolving set of libraries originally released by D. E. Cullen and collaborators as separate data for photo-atomic interactions (EPDL), atomic relaxation (EADL), and electro-atomic interactions (EEDL). An updated version of this release was officially adopted in the ENDF/B VI.8 release of the Evaluated Nuclear Data File and it was this source that was used to produce the ACE (A Compact ENDF) format library eprdata12 for use in the initial production release of MCNP6.1 (and the subsequent interim release MCNP6.1.1). The latest improved version of this database was released by Cullen in 2014. This is the EPICS2014 collection, which includes both an ENDF-formatted file and separate EPDL, EADL, and EEDL files. In the area of photon-electron transport and atomic-relaxation, the newest enhancements to the current version of MCNP6 (scheduled for release as MCNP6.2) are primarily based on improvements and additions drawn from the EPICS2014 data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Virtual Environment for Testing Software-Defined Networking Solutions for Scientific Workflows

Recent developments in software-defined infrastructures promise that scientific workflows utilizing supercomputers, instruments, and storage systems will be dynamically composed and orchestrated using software at unprecedented speed and scale in the near future. Testing of the underlying networking software, particularly during initial exploratory stages, remains a challenge due to potential disruptions, and resource allocation and coordination needed over the multi-domain physical infrastructure. To overcome these challenges, we develop the Virtual Science Network Environment (VSNE) that emulates the multi-site host, storage, and network infrastructure using Virtual Machines (VMs), wherein the production and nascent software can be tested. Within each VM, which represents a site, the hosts and local-area networks are emulated using Mininet, and the Software-Defined Network (SDN) controllers and service daemon codes are natively run to support dynamic provisioning of network connections. Additionally, Lustre filesystem support at the sites and an emulation of the long-haul network using Mininet, are provided using separate VMs. As case studies, we describe Lustre file transfers using XDD, Red5 streaming service demonstration, and an emulated experiment with remote monitoring and steering modules, all supported over dynamically configured connections using SDN controllers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Air temperature, Utqiagvik (Barrow), Alaska, July 15-17, 2022

Air temperature measured at 15 minute intervals using a HOBO 64K Pendant UA-001-64 housed in a solar radiation shield. Data were collected 15-27 July 2022, on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. The data package files are in .csv format, and include a data file and metadata file. These data were collected in support of stomatal response gas exchange measurements collected during this time period. See related data packages for leaf gas exchange data, leaf trait data (leaf mass per area, leaf nitrogen concentration), and phenocamera images collected at the same time and location. 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 OSTI2014

WE-A-BRE-01: Debate: To Measure or Not to Measure

Recent studies have highlighted some of the limitations of patient-specific pre-treatment IMRT QA measurements with respect to assessing plan deliverability. Pre-treatment QA measurements are frequently performed with detectors in phantoms that do not involve any patient heterogeneities or with an EPID without a phantom. Other techniques have been developed where measurement results are used to recalculate the patient-specific dose volume histograms. Measurements continue to play a fundamental role in understanding the initial and continued performance of treatment planning and delivery systems. Less attention has been focused on the role of computational techniques in a QA program such as calculation with independent dose calculation algorithms or recalculation of the delivery with machine log files or EPID measurements. This session will explore the role of pre-treatment measurements compared to other methods such as computational and transit dosimetry techniques. Efficiency and practicality of the two approaches will also be presented and debated. The speakers will present a history of IMRT quality assurance and debate each other regarding which types of techniques are needed today and for future quality assurance. Examples will be shared of situations where overall quality needed to be assessed with calculation techniques in addition to measurements. Elements where measurements continue to be crucial such as for a thorough end-to-end test involving measurement will be discussed. Operational details that can reduce the gamma tool effectiveness and accuracy for patient-specific pre-treatment IMRT/VMAT QA will be described. Finally, a vision for the future of IMRT and VMAT plan QA will be discussed from a safety perspective. Learning Objectives: Understand the advantages and limitations of measurement and calculation approaches for pre-treatment measurements for IMRT and VMAT planning Learn about the elements of a balanced quality assurance program involving modulated techniques Learn how to use tools and techniques such as an end-to-end test to enhance your IMRT and VMAT QA program.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-183: Clinical Quality Assurance Workflow for Dynamic Tumor Tracking Radiation Dose Delivery

Purpose: One of the most important aspects of implementation of new treatment modalities is an ‘end-to-end’ verification of the treatment process. Radiation treatment based on dynamic tracking of a tumor is highly patient-specific, therefore, special attention should be paid to quality assurance of the treatment delivery. Our goal was to design the clinical workflow that ensures accurate delivery of the planned dose using the Dynamic Target Tracking option of VeroTM (BrainLab,MHI) linac. Methods: A patient simulation is designed to include a pre-treatment session to verify whether the system can reliably track the motion of the implanted marker and build the 4D model of the target motion. The external surrogate and target motion patterns are recorded in the ExactracTM log files. In this work, a spectrum of custom marker and external surrogate motion trajectories closely resembling the patient specific motion patterns was used. 1mm thick/11mm long VisicoilTM marker was placed 15 and 20mm from the center of the spherical tissue equivalent target (centroid to centroid distance) in the 4D motion phantom (CIRSTM). 3D conformal (3 mm block margin) SBRT plans were delivered to 2 moving targets in the phantom: 1) 20mm diameter target that allows ion chamber dose measurement and 2) 25mm target that allows using film to measure CAX dose (GafchromicTM EBT3 used). The measured dose was compared to the iPlanTM TPS results using MonteCarlo algorithm (1% variance, Dose-to-water). Results: On average, film shows 98.9% pass using gamma criterion for 2% and 2mm DTA, 94.3% match for 2% and 1 mm DTA, 98% pass for 1% and 2 mm DTA however only 88% points passing for 1% and 1 mm DTA. Ion chamber measurements agreed with the calculation within 1.5%. Conclusion: The clinical QA workflow was designed for SBRT delivery using real-time tumor tracking on VeroTM linac.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Simplifying and Visualizing the Ontology of Systems Engineering Models

The credibility of an engineering model is of critical importance in large-scale projects. How concerned should an engineer be when reusing someone else's model when they may not know the author or be familiar with the tools that were used to create it? In this report, the authors advance engineers' capabilities for assessing models through examination of the underlying semantic structure of a model--the ontology. This ontology defines the objects in a model, types of objects, and relationships between them. In this study, two advances in ontology simplification and visualization are discussed and are demonstrated on two systems engineering models. These advances are critical steps toward enabling engineering models to interoperate, as well as assessing models for credibility. For example, results of this research show an 80% reduction in file size and representation size, dramatically improving the throughput of graph algorithms applied to the analysis of these models. Finally, four future problems are outlined in ontology research toward establishing credible models--ontology discovery, ontology matching, ontology alignment, and model assessment.

42 ENGINEERING↗

DOE OSTI2014

Intensive Site 1 Vegetation Plot Photos, Barrow, Alaska, 2012

Photographs were taken on 24th June, 15th July and 17th August 2012, using a Canon Ixus 70 7.1 megapixel digital camera. Photographs were taken during the recording of weekly soil moisture, temperature and thaw depth measurements (Sloan et al., 2014), over a time period spanning approximately 6 hours on each day. Photographs were taken from positions where matted trail allowed access to vegetation plots. This dataset contains 14 files. Twelve of the files are zipped folders containing photos of specific plots during one of three days. 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 OSTI2020

Leaf Area Index (LAI) of vegetation at the Teller site, Seward Peninsula, Alaska, 2017

Measurements of leaf area index (LAI) at 299 locations on the "tall shrub transect" at the NGEE Arctic Teller field site. Measurements were made on 2017-07-27 using a LiCor LAI-2200. The dataset includes 9 files as dGPS locations, raw measurements, processed data and R script used for processing. 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 OSTI2025

Automated Direct Perturbation Calculations with SCALE TSUNAMI [Abstract]

In nuclear criticality safety analysis, the sensitivity of the eigenvalue keff to uncertainties in nuclear data and its evaluation are crucial. The TSUNAMI sequences within the SCALE code system offer users various options with both multigroup (MG) and continuous-energy (CE) 3D Monte Carlo (MC) transport capabilities for calculating keff sensitivity coefficients and storing them in a sensitivity data file (SDF). Each methodology available in TSUNAMI offers distinct advantages and limitations, and its effectiveness can vary based on the specific problem being solved. As a best practice, practitioners typically use the direct perturbation (DP) method as a confirmatory step alongside their sensitivity calculations to verify the accuracy of the sensitivity data generated. In this process, DP calculations are usually performed on select nuclides, those considered most important for validating their total sensitivities. However, because of code limitations, analysts use a workaround method when conducting DP calculations for a single nuclide: rather than perturbing the nuclide's microscopic cross section, an equivalent number density for this nuclide is calculated to reflect the effect of a change in the macroscopic cross section due to a perturbation in the microscopic cross section. The current approach requires rerunning the CSAS criticality calculation several times with model changes. Although this method can yield results with acceptable accuracy, it is labor-intensive and prone to errors.

AZURE: SAMMY↗

DOE OSTI2026

Data and scripts associated with a manuscript analyzing ELM-FATES parameter sensitivity under pre-fire and postfire scenarios using machine learning

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Fire Severity-Dependent Shifts in Vegetation Parameter Sensitivity: A Pre- and Post-Fire Analysis Using ELM-FATES and Explainable AI” submitted to Journal of Advances in Modeling Earth Systems (Zahura et al. 2026). The study examines vegetation physiological parameters controlling pre-fire and post-fire vegetation dynamics. To support this analysis, 73 vegetation parameters in Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (Fisher et al., 2018) , which is coupled with E3SM (Energy Exascale Earth System Model) land model (ELM, ELM-FATES), were perturbed using a Sobol sequence to generate 1,024 ensemble members for two plant functional types: needleleaf evergreen extratropical trees (NEET) and C3 grass. Simulations were conducted for the pre-fire period (2016) and post-fire period (2018–2023). Burn severity was represented by modifying the Nesterov index in FATES to 75,000, 150,000, and 300,000 for low, moderate, and high severity, respectively. A no-fire scenario was also included. Simulations were performed for 16 grid cells in the American River Watershed across different burn severities and plant functional types. XGBoost (eXtreme Gradient Boosting) models were trained using the parameter ensembles and ELM-FATES-simulated outputs, including leaf area index (LAI), gross primary productivity (GPP), aboveground biomass, vegetation evaporation, transpiration, and soil evaporation. Models were trained separately for each year and burn severity, followed by SHAP (SHapley Additive exPlanations) analysis to identify changes in dominant parameters after fire disturbance. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. The data package contains the ELM-FATES simulation data. The scripts and data related to the analysis will be added later. The inputs and outputs from ELM-FATES are inside the “FATES” folder. “FATES_domain_surface” contains the domain and surface netcdfs that were used to run ELM-FATES in the study area. “FATES_parameters” contains the 1024 ensembles that were generated using Sobol sequence. “FATES_outputs” folder contains ELM-FATES simulated variables. All files are .csv and .nc (NetCDF).

Aboveground biomass↗

DOE OSTI2024

Drifting Acoustic Measurements around C-Power's SeaRay WEC

The repository contains underwater noise measurements and associated metadata collected around C-Power's SeaRay wave energy converter on July 15, 2024 and July 16, 2024 while it was deployed at the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Measurements were obtained using Drifting Acoustic Instrumentation SYstems (DAISYs). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is a compressed archive (.zip format), containing multiple MATLAB binary data files (.mat format). The structure of each file is included in the repository as a Word document (Data Description MHK-DR.docx). Each file contains time series information for a single DAISY deployment (file naming convention: WETS_DAISY_[Drift #].mat) consisting of processed hydrophone data and associated metadata. During these measurements, C-Power's SeaRay was located at approximately 21.48112 N, 157.74451 W.

16 TIDAL AND WAVE POWER↗