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DOE OSTI2025

2D reactive transport model of shale chemical weathering and biogeochemical fluxes along a mountainous hillslope, East River Watershed, Colorado: Input files and simulation results

This data package contains input files and simulation results for a two-dimensional (2D) reactive transport model used to quantitatively analyze the coupled hydrological and biogeochemical processes governing shale weathering and associated biogeochemical fluxes under realistic environmental conditions in the high-elevation East River Watershed. These data support the conclusions presented in Stolze et al. (Water Resources Research, under review), "Model-based interpretation of solute exports and carbon partitioning during shale weathering in a mountainous hillslope". The model simulates atmospheric-subsurface gas exchange, subsurface water flow, and shale weathering processes under dynamic, year-scale conditions along a shale-underlain hillslope located in the East River watershed. The simulations were performed using the PFLOTRAN flow and reactive transport code and executed on the Perlmutter supercomputer to leverage its large-scale parallel computing capabilities. The data package contains two zipped folders, "model_input_files" and "simulation_results", and one readme.txt file. "model_input_files" contains the necessary input files to run the calibrated base-base model presented in Stolze et al. (Water Resources Research, under review). "simulation_results" contains a single hdf5 file ("Output_2D_hillslope_model.h5") which includes the results of simulation performed using the base-case model. This file can be opened with HDFView 3.1.4, Python, or MATLAB. "readme.txt" contains relevant information about the base-case model and provides guidelines on how to run the associated input files provided in the folder "model_input_files". Furthermore, readme.txt provides information regarding the model results provided in "Output_2D_hillslope_model.h5" such as matrix dimensionality and output units. Field datasets used to evaluate model performance were collected at three monitoring wells located along a hillslope transect (PLM1, PLM2, and PLM3). Dissolved ion concentration data were collected from November 2016 to October 2021 for Ca, Mg, DIC, Na, K, SO4 (Dong et al., 2025 - dic_npoc_data_2014_2024.zip - DOI:10.15485/1660459; Williams et al., 2025 - anion_data_2014_2024.zip - DOI:10.15485/1668054; Dong et al., 2025 - cation_data_2014_2024.zip - DOI:10.15485/1668055). Note that we used the files named er_PLM1_xx_yy, er_PLM2_xx_yy, and er_PLM3_xx_yy where xx stands for the name of the aqueous species and yy stands for the depth where the measurements were performed. Soil water content ([0 - 1] m) and water table depth were collected from November 2016 to October 2021 (Wan et al., 2024 - Dynamic_water_table__depthsFig2b.csv and Soil_water_content_Fig4e.csv - DOI:10.15485/2322567). Gaseous CO2 concentration were collected from October 2020 to December 2021(Wan et al., 2024 - Soil_CO2_concentrations_Fig4h.csv - DOI:10.15485/2322567) Gaseous CO2 flux from the subsurface to the atmosphere were collected in the vicinity of PLM2 from October 2019 to May 2022 (Wu et al., 2025). Soil microbial biomass concentration was measured from August 2016 to June 2017 (Sorensen et al., 2019 - 2017_East_River_Pumphouse_Microbial_Biomass__1_.csv - DOI:10.15485/1577267) All field data are published as CSV files compatible with Microsoft Excel, MATLAB, and Python, or as text files. The coordinates of the monitoring wells and the CO2(g) flux sensor in the coordinate system WGS84 are: -PLM1: [38.9197710 ; -106.9492750] -PLM2: [38.9201580 ; -106.9487170] -PLM3: [38.9207843 ; -106.9483668] -PLM4: 38.9210060 ; -106.9479528] -CO2(g) flux sensor: [38.9199180 ; -106.9489906] ------------------------------------------------------------------------------------------- This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BER-ERCAP 23980, BER-ERCAP 28550, and BER-ERCAP 33789.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Terrestrial laser scanning data (Levels 0 and 1) from Urban Biogeochemistry Pilot Project sites, Knoxville, Tennessee, Jul 2024 - Jul 2025

This data package contains data from terrestrial laser scanning (TLS) at five urban park sites in Knoxville, Tennessee, USA. All parks include open-grown and/or closed-canopy trees and mixed nearby land use. These study sites were established as part of the Urban Biogeochemistry Pilot Project, which has an overall goal of better understanding how hydrobiogeochemical cycling is altered within the human environment. These five sites represent a gradient of urbanization, and were instrumented to understand hydrological and biogeochemical cycling (e.g., soil moisture, soil physical properties and biogeochemistry, tree transpiration, species type). The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree- and stand-level characterization of woody structure and leaf area. TLS scans were placed to capture the area around trees with sap flow sensors, and as much of a 50 m radius area around the meteorological station as possible given site property limits. Derived products will allow upscaling of water content and transpiration data. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020

Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.

Great Lakes↗
DOE OSTI2026

Model Data Archive for Manuscript Titled "Evaluation of a Coupled Surface–Subsurface Hydrologic Model Using Dense Water‑Level Sensors in a Mixed Urban–Rural Watershed"

This archive provides scripts, input files, and datasets used for the implementation and evaluation of a fully coupled surface–subsurface hydrologic model in the Neches River Basin, southeast Texas. The study uses the Advanced Terrestrial Simulator (ATS) to simulate coupled surface–subsurface hydrologic processes over a mixed urban–rural watershed and evaluates model performance using a dense network of 136 in situ water-level sensors, nine U.S. Geological Survey (USGS) stream gauges, and SSEBop-derived evapotranspiration estimates during the period October 2014–June 2024. The workflow is implemented primarily in Python 3 using the Watershed Workflow package. The Jupyter notebooks can be executed using open-source software such as Anaconda JupyterLab or Visual Studio Code. Other data files include TXT, CSV, XML, SHP, TIF, NetCDF, HDF5, and ExodusII files, which can be processed using the provided Python scripts. ATS input files are provided in XML format and can be edited using any commonly used text editor. This archive contains: *Scripts and input files used to generate the ATS model setup, including watershed discretization, mesh generation, parameter mapping, and model configuration. *Jupyter notebooks used for preprocessing observational data, evaluating streamflow, water levels, and evapotranspiration, computing performance metrics, and generating the figures presented in the manuscript. *ATS simulation outputs and processed observational datasets, including OneRain and DD6 water-level sensors, USGS streamflow observations, GIS data, and supporting spatial datasets used throughout the study.

Dense water-level sensor network↗
DOE OSTI2025

Electricity Baseline 2022

The Electricity Baseline (2022) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2022" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569193. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Electricity; LCA; data inventory↗
DOE OSTI2025

Electricity Baseline 2021

The Electricity Baseline (2021) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2021" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569576. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Electricity; LCA; LCI; Life Cycle↗
DOE OSTI2025

Electricity Baseline 2020

The Electricity Baseline (2020) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2020" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569605. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).

Electricity; LCA; LCI; data inventory↗
DOE OSTI2024

xesn: Echo state networks powered by Xarray and Dask

Xesn is a Python package that allows scientists to easily design Echo State Networks (ESNs) for forecasting problems. ESNs are a Recurrent Neural Network architecture introduced by Jaeger (2001) that are part of a class of techniques termed Reservoir Computing. One defining characteristic of these techniques is that all internal weights are determined by a handful of global, scalar parameters, thereby avoiding problems during backpropagation and reducing training time significantly. Because this architecture is conceptually simple, many scientists implement ESNs from scratch, leading to questions about computational performance. Xesn offers a straightforward, standard implementation of ESNs that operates efficiently on CPU and GPU hardware. The package leverages optimization tools to automate the parameter selection process, so that scientists can reduce the time finding a good architecture and focus on using ESNs for their domain application. Importantly, the package flexibly handles forecasting tasks for out-of-core, multi-dimensional datasets, eliminating the need to write parallel programming code. Xesn was initially developed to handle the problem of forecasting weather dynamics, and so it integrates naturally with Python packages that have become familiar to weather and climate scientists such as Xarray (Hoyer & Hamman, 2017). However, the software is ultimately general enough to be utilized in other domains where ESNs have been useful, such as in signal processing (Jaeger & Haas, 2004).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Developing a Prototype Methodology to Rank CO2-EOR Wells and Assess Their Reuse Potential for Geologic Carbon Storage

This paper presents a prototype methodology to assess the possible transition of Class II carbon dioxide-enhanced oil recovery (CO2-EOR) wells to Class VI wells. The focus is on wellbore construction materials—casing, cement, tubing, and the packer—and includes comprehensive workflows to evaluate these materials, with primary emphasis on compliance with Environmental Protection Agency (EPA) Class VI well construction and conversion guidelines. These workflows systematically assess material properties and performance criteria to ensure regulatory compliance and optimize long-term wellbore integrity and functionality. Utilizing Python scripts and JavaScript Object Notation (JSON) representations, the study automates checks on digitized Texas Railroad Commission (TRRC) data to rank wells based on workflow criteria. By emphasizing critical factors such as casing integrity, cementing techniques, tubing compatibility, and packer selection, the methodology helps well owners and operators prioritize wells for potential reuse as CO2 injection wells. Given limitations in digitized data, manual user verification is required in some sections. Future improvements include integrating non-digitized data through web scraping and machine learning techniques. This research serves as a practical guide for stakeholders, supporting environmental compliance and sustainable well operations.

geologic carbon sequestration↗
DOE OSTI2024

DASSH-F: Subchannel Based Thermal Analysis

The DASSH thermal analysis code is designed to rapidly allow a reactor design engineer to obtain flow rates requirements that satisfy peak temperature constraints in the domain. The advantage of using DASSH over a hand calculation is that it has a more rigorous treatment of the pin power distribution and coolant heat transfer within an assembly and between assemblies. The advantage of using DASSH over a conventional 3D subchannel code or a computational fluid dynamics code (CFD) is that it can obtain the desired solution in a matter of minutes in serial with minor computer memory needs. The DASSH methodology is virtually identical to SUPERENERGY-2 with additional functionalities taken from follow on work to SUPERENERGY-2 done at ANL in the 1980s. DASSH today is an integral component of the Argonne Fast Reactor analysis suite for reactor design work. DASSH obtains the power distribution from a coupled neutron-gamma heating calculation in GAMSOR (including DIF3D) at each time point of a companion fuel cycle analysis calculation with REBUS. The domain in DASSH assumes a hexagonal grid typical for fast reactors with much of the geometry information taken from the DIF3D model. DASSH assumes the assemblies that are loaded into each grid position are ducted to control the coolant flow. Considerable detail is given on the subchannel formulation of DASSH in this document. Much of the formulation and design of the code builds upon research done by previous authors with little new investigation. Thus the decisions made in developing the subchannel model used in DASSH have their origins over 50 years ago. Much of the heat transfer methodology in DASSH is built upon correlations for both the coolant mixing and heat transfer coefficients for pins and ducts. DASSH is thus not a rigorous treatment of a given problem, but a rapid assessment of the temperature field that has known limitations with respect to an experimental measurement or CFD calculation. The DASSH input and output are detailed along with usage of the software. The DASSH output provides tables of evaluated material properties and key coolant and pin temperature results. DASSH can create Python scripts that generate domain summary pictures. DASSH can also generate assembly temperature maps and VTK output files which allow the DASSH solution to be visualized. As the primary purpose of the DASSH software is to compute the coolant and fuel pin temperature distribution for a given model of a reactor, much of the output focus is giving the user quick summary tables needed to assess the performance of a given orifice flow specification. The present version of DASSH has a crude orifice search capability and an efficient orifice flow search capability. The flow search tries to meet user specified constraints for 1) peak 2-sigma clad midwall temperature, 2) peak coolant temperature, and 3) desired bulk outlet temperature. There are many development shortcomings in DASSH detailed in this document, but this version is functional for modern analysis needs. This document serves as the manual for the Fortran based DASSH software that was developed to replace the Python version of DASSH developed as part of the VTR program.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Implementing a Laser Stabilization System for Trapping Ca+ Ions: an Internship Reflection

At Lawrence Livermore National Laboratory, I contributed to a project developing 3D printed micro ion traps for quantum computing. I designed, implemented, and assessed a laser stabilization system that locked lasers to the frequencies required for calibrating our High Finesse WS8-10 wavelength meter and for laser cooling and trapping of Ca+ ions. I also programmed a Python interface for hardware communication, data collection, and statistical analysis. Additionally, I optimized and aligned laser beam paths, and I implemented a closed digital feedback loop using Proportional, Integral, and Derivative (PID) control parameters. I analyzed both the long-term and short-term behavior of our locked lasers and adjusted PID parameters to enhance performance. Furthermore, I used COMSOL to simulate the capacitance of a linear Paul trap design and predict our trap’s performance. The procedures I developed for the interface, analysis, and simulations will continue to support the ion trapping experiment after my appointment. I strengthened my skills in data analysis, Python coding, and optical alignment for laser systems. My confidence as a researcher grew, particularly in communicating my research. This experience taught me the importance of careful planning and consideration in research and solidified my desire to continue exploring novel quantum technology as an undergraduate

42 ENGINEERING↗
DOE OSTI2024

PV Performance Modeling and Stakeholder Engagement (Final Technical Report)

This core capability project’s objective is to increase the value of photovoltaic (PV) performance models by improving their functionality, demonstrating, and quantifying their validity, and offering a wide range of stakeholder engagement opportunities. In FY22-24, we developed new and improved modeling algorithms and functions to represent PV performance more accurately in a variety of environments and conditions. The “Model parameter toolkit” was developed and includes functions to translate between different module temperature models, incidence angle modifier models, and single-diode models. A new modeling capability named “PV Atlas” was also developed leveraging Sandia’s High Performance Computing resources. This capability allows us to investigate several questions and provide climate-specific best practices and geographic data files; all these are hosted on an interactive website on Sandia’s GitHub and can be used for training, system optimization, or to provide best practices for uncertainty reduction. For model validation, we published high-quality PV performance, and weather data; these data are well documented, filtered, and processed for quality and include examples on how to run PV simulations. We also developed well documented, standardized methods for validating PV models and ran independent model validation and 2 blind modeling intercomparisons engaging with 49 organizations from 17 countries. We co-led and contributed to a growing, well documented and maintained suite of open-source functions for PV modeling (i.e., the pvlib-python) and we outreached to the PV modeling stakeholders via the PVPMC workshops and web resources. In addition, this project supported US representation and leadership for the International Energy Agency (IEA) PVPS Task 13; specifically, members of our team led and supported 3 subtasks on: 1) Best practices for the optimization of bifacial photovoltaic tracking, 2) Extreme weather events and their multiple impact on PV power plants: Risks, failure mechanisms and mitigation strategies, and 3) Best practice guidelines for the use of economic and technical Key Performance Indicators (KPIs). This project resulted in the publications of 14 peer reviewed journal papers, 37 conference presentations, 6 SAND reports, 5 public datasets and 6 new webpages on the PVPMC website. It supported the release of 13 pvlib-python versions where 28 enhancements were from this PV Performance Modeling project. We co-organized 5 PVPMC workshops in FY22-24 with the participation of 214 unique institutions and around 700 participants. The PVPMC website was redesigned, and its reliability was improved; it receives over 50,000 visitors/year from 202 unique countries.

14 SOLAR ENERGY↗
DOE OSTI2024

Light Output Fitting Software

Light output response of scintillators is crucial to the utilization of organic scintillators as effective tools in radiation detection and measurement. While the response is a continuous distribution, the light output corresponding to the maximum energy deposition is crucial in effectively understanding and simulating a detector. There are a variety of fits derived in literature that will vary for every detector material. The Light Output Response Fitter, or LORF Program is a python script designed to easily and quickly compute and plot fits for a variety of scintillator light output models. It includes a stopping power library constructed from SRIM including Organic Glass, EJ309, EJ301, Stilbene, EJ276, and their deuterated counterparts by default, with the ability for the user to add custom stopping power libraries. The user is also capable of importing the python package and utilizing its in-built functions as appropriate. Uses for this capability include plotting and computing a model with known parameters.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Enabling Innovative Analysis on Heterogeneous Clusters through HTCdaskgateway

High energy particle (HEP) physics research is going through fundamental changes as we move to collect larger amounts of data from the Large Hadron Collider (LHC). Analysis facilities and distributed computing, through HTCs, have come together to create the next pythonic generation of analysis by utilizing HTCdaskgateway, a Dask gateway extension, allowing users to spawn workers compatible with both their analysis and heterogeneous clusters in line with authentication requirements. This is enabling physicists to engage with scientific python in ways they had not before because of domain specific C++ tools. An example of HTCdaskgateway’s use is Fermilab’s Elastic Analysis Facility.

Chavez, Elise [U. Wisconsin, Madison (main)]↗
DOE OSTI2025

HFIR Activity Workbook Generator (HAWK) User Guide

The HFIR Activity WorkbooK generator (HAWK) is a Python code that automates and streamlines the activity calculation of samples after irradiation in the High Flux Isotope Reactor (HFIR). HAWK’s results provide estimates of the activity and nuclide inventory of irradiated specimens before they are moved to hot cell facilities, where they undergo post-irradiation examination. The samples’ activity results guide the packing of shipping containers and inform the accountable inventories for the hot cell facilities. The toolkit was originally developed by Charles Daily, a former R&D staff member at Oak Ridge National Laboratory (ORNL). As of May 2025, HAWK is developed by the Radiation Transport & HPC Methods Group (Nuclear Energy and Fuel Cycle Division) at ORNL. Figure 1 presents HAWK’s workflow. To use HAWK, users need to: 1. Develop an Excel input workbook (i.e., XLSX extension) containing data from the experiment’s materials, irradiation history (cycles), and irradiation positions. 2. Make minor edits to an existing template JSON file (i.e., auxiliary_data.JSON) and to the Python driver. The driver sets the necessary environment variables, defines the material compositions, and ultimately calls HAWK. Once configured, HAWK runs the Oak Ridge Isotope Generation code (ORIGEN) to calculate the masses, activities, and heat load at the end of irradiation for each isotope in the specimen. ORIGEN is part of SCALE, ORNL’s in-house computational tool for performing nuclear safety and design calculations. Following this step, HAWK postprocesses the results and generates three output workbooks summarizing the activity calculations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

DASSH-F: Subchannel Based Thermal Analysis

The DASSH thermal analysis code is designed to rapidly allow a reactor design engineer to obtain flow rates requirements that satisfy peak temperature constraints in the domain. The advantage of using DASSH over a hand calculation is that it has a more rigorous treatment of the pin power distribution and coolant heat transfer within an assembly and between assemblies. The advantage of using DASSH over a conventional 3D subchannel code or a computational fluid dynamics code (CFD) is that it can obtain the desired solution in a matter of minutes in serial with minor computer memory needs. The DASSH methodology for pin lattice models is virtually identical to SUPERENERGY-2 with additional functionalities taken from follow on work to SUPERENERGY-2 done at ANL in the 1980s. DASSH today is an integral component of the Argonne Fast Reactor analysis suite for reactor design work. DASSH obtains the power distribution from a coupled neutron-gamma heating calculation in GAMSOR (including DIF3D) at each time point of a companion fuel cycle analysis calculation with REBUS. The domain in DASSH assumes a hexagonal grid typical for fast reactors with much of the geometry information taken from the DIF3D model. DASSH assumes the assemblies that are loaded into each grid position are ducted to control the coolant flow. The user can alternatively provide their own geometry and power profile instead of inheriting it from DIF3D. Considerable detail is given on the subchannel formulation of DASSH in this document. Much of the formulation and design of the code builds upon research done by previous authors with little new investigation. Thus the decisions made in developing the subchannel model used in DASSH have their origins over 50 years ago. Much of the heat transfer methodology in DASSH is built upon correlations for both the coolant mixing and heat transfer coefficients for pins and ducts. DASSH is thus not a rigorous treatment of a given problem, but a rapid assessment of the temperature field that has known limitations with respect to an experimental measurement or CFD calculation. The DASSH input and output are detailed along with usage of the software. The DASSH output provides tables of evaluated material properties and key coolant and pin temperature results. DASSH can create Python scripts that generate domain summary pictures. DASSH can also generate assembly temperature maps and VTK output files which allow the DASSH solution to be visualized. As the primary purpose of the DASSH software is to compute the coolant and fuel pin temperature distribution for a given model of a reactor, much of the output focus is giving the user quick summary tables needed to assess the performance of a given orifice flow specification. The present version of DASSH has a crude orifice search capability and a sufficient orifice flow search capability. The flow search tries to meet user specified constraints for 1) peak 2-sigma clad temperature, 2) peak coolant temperature, and 3) desired bulk outlet temperature. This document serves as the manual for the Fortran based DASSH software that was developed to replace the Python version of DASSH developed as part of the VTR program.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Strategic Petroleum Reserve Cavern Leaching Monitoring CY24

This report provides an analysis of the effects of raw water leaching at the U.S. SPR using the Sandia Solution Mining Code (SANSMIC). A new version of the code has been established in the past year (implemented in Python and C++ code) and was used for this annual report. Additionally, the setup, running, and post-processing of leaching modeling has now been streamlined into a single Python notebook environment for each cavern run.

58 GEOSCIENCES↗
DOE OSTI2026

Measuring the Stress Factors for Photovoltaic (PV) Backsheet Degradation

Back sheet failure has resulted in power loss and large-scale recall of photovoltaic modules, resulting in billions of dollars in lost revenue. The light exposure on the backside of a photovoltaic module comes primarily from reflected light which alters the distribution of natural sunlight. Because of this, modelling the backside exposure and duplicating the exposure is much more difficult than modeling the frontside exposure. This project aims to study how various back sheets and junction box materials degrade under different conditions and to develop Python code to help model and predict degradation. The stress factors for back-sheet degradation must be quantified to extrapolate accelerated stress tests to the field. Test samples were placed in the A3, A4, and A5 conditions, as defined in IEC 62788-7-2, to assess the temperature and humidity dependence of ultraviolet (UV) induced degradation. We are utilizing a custom chamber with exposure from 0.5 UV-suns to 5 UV-suns to understand the dependence of degradation on light intensity. A group of samples put in the A3 condition had glass filters with 50% UV cut-offs of 320 nm, 335 nm, and 360 nm to assess the wavelength dependence of UV degradation. All this data is necessary to assess the impact of non-standard UV light exposure. The material evaluation tests include gloss measurements, attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR), UV-visible reflectance/transmittance utilizing a Cary Ci7000 spectrophotometer, and a nano-indenter for surface hardness and modulus measurements. Alongside the experimental work, there is a computational effort using raytracing and Python open-source tools in PVDeg , PVLib, and Bifacial_Radiance. This code will create specific exposure scenarios and enable the evaluation of chamber degradation relative to field degradation. Equation 1 is a strawman equation used to model degradation on the backside of a PV module. We will create simplified code, based on the results of ray-tracing calculations, which uses a view factor approach to provide fast calculations for the most common exposure scenarios.

14 SOLAR ENERGY↗