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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗
DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

The His-tag as a decoy modulating preferred orientation in cryoEM

The His-tag is a widely used affinity tag that facilitates purification by means of affinity chromatography of recombinant proteins for functional and structural studies. We show here that His-tag presence affects how coproheme decarboxylase interacts with the air-water interface during grid preparation for cryoEM. Depending on His-tag presence or absence, we observe significant changes in patterns of preferred orientation. Our analysis of particle orientations suggests that His-tag presence can mask the hydrophobic and hydrophilic patches on a protein’s surface that mediate the interactions with the air-water interface, while the hydrophobic linker between a His-tag and the coding sequence of the protein may enhance other interactions with the air-water interface. Our observations suggest that tagging, including rational design of the linkers between an affinity tag and a protein of interest, offer a promising approach to modulating interactions with the air-water interface.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

MaNGA DynPop – I. Quality-assessed stellar dynamical modelling from integral-field spectroscopy of 10K nearby galaxies: a catalogue of masses, mass-to-light ratios, density profiles, and dark matter

ABSTRACT This is the first paper in our series on the combined analysis of the Dynamics and stellar Population (DynPop) for the MaNGA survey in the final SDSS Data Release 17 (DR17). Here, we present a catalogue of dynamically determined quantities for over 10 000 nearby galaxies based on integral-field stellar kinematics from the MaNGA survey. The dynamical properties are extracted using the axisymmetric Jeans Anisotropic Modelling (JAM) method, which was previously shown to be the most accurate for this kind of study. We assess systematic uncertainties using eight dynamical models with different assumptions. We use two orientations of the velocity ellipsoid: either cylindrically aligned JAMcyl or spherically aligned JAMsph. We also make four assumptions for the models’ dark versus luminous matter distributions: (1) mass-follows-light, (2) free NFW dark halo, (3) cosmologically constrained NFW halo, (4) generalized NFW dark halo, i.e. with free inner slope. In this catalogue, we provide the quantities related to the mass distributions (e.g. the density slopes and enclosed mass within a sphere of a given radius for total mass, stellar mass, and dark matter mass components). We also provide the complete models which can be used to compute the full luminous and mass distribution of each galaxy. Additionally, we visually assess the qualities of the models to help with model selections. We estimate the observed scatter in the measured quantities which decreases as expected with improvements in quality. For the best data quality, we find a remarkable consistency of measured quantities between different models, highlighting the robustness of the results.

Astronomy & Astrophysics↗
DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Nth-plant supply: corn stover supplies and costs in a fleet of biorefineries

Feedstock cost and cost variability is expected to increase with the number of biorefineries. To quantify this effect, this spatial-economic analysis simulates feedstock cost and cost variability of an industry based on corn stover as a function of the number of biorefineries. Results are reported for nine scenarios (a base case and sensitivity analysis of four variables – harvest efficiency, sustainability constraints, opportunity cost, and corn grain yield) under deterministic and stochastic simulations, assuming biorefineries using 658 000 Mg (725 000 tons) year- 1 of corn stover in 2019. The resulting supply curves are highly elastic (i.e. little change in cost) for the first 50 of the 121 biorefineries, with price increases in subsequent biorefineries depending on scenario. In the base-case deterministic scenario, weighted-average stover costs are $66 Mg -1 ($60 ton- 1 ), $69 Mg -1 ($62 ton -1 ), and $156 Mg -1 ($142 ton -1 ), at the first, 60th, and 121st biorefineries, respectively. The stochastic simulations, subject to observed 30-year corn yield variability, follow a similar pattern, with price distributions that vary by scenario. The base-case stochastic simulations illustrate minimal cost variability for the first 60 biorefineries, but rapid increases in cost variability in the second half of potential biorefineries, with similar patterns observed in the other scenarios. Of the four variables explored, price was most sensitive to harvest efficiency, followed by sustainability constraints, corn yield, and opportunity cost. Results suggest that, under conventional logistics, about half of the US corn stover resource is reliably available with minimum cost increase and variability. Interactive visualization is available at https://doi.org/10.11578/1828779.

09 BIOMASS FUELS↗
DOE OSTI2020

Elastic depths for detecting shape anomalies in functional data

This this paper, we propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable from the bulk of the data with visualization methods. The proposed elastic depths use the recently developed elastic distances to directly measure the centrality of functions in the amplitude and phase spaces. Measuring shape outlyingness in these spaces provides a rigorous quantification of shape which, in turn, gives the elastic depths a strong theoretical and practical advantage over other methods in detecting shape anomalies. A simple boxplot and thresholding method are introduced to identify shape anomalies using the elastic depths. We assess the elastic depth's detection skill on simulated shape outlier scenarios and compare them against popular shape anomaly detectors. Finally, bond yields, image outlines, and hurricane trajectories are used to demonstrate our method's applicability to functional data observed on three different manifolds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation

Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.

Ricchiuti, Giovanna↗
DOE OSTI2026

Lake-Effect Snowstorm Events and Associated Snowfall Totals Integrated from NOAA Storm Reports, ERA5, and HRRR for the Laurentian Great Lakes (1997–2024)

Lake-effect snowstorms are localized, impactful winter weather phenomena that can generate substantial snowfall totals and pose significant challenges for forecasting, transportation, and regional infrastructure. To support the analysis and modeling of these events, this dataset compiles observational reports of lake-effect snowstorms alongside corresponding snowfall estimates derived from gridded atmospheric datasets. The observational component of the data originates from the National Weather Service (NWS) winter storm report, subset to lake-effect snow event type, covering 1997–2024. For each lake-effect snow event, this data provides the impacted county, event start and end datetimes at an hourly resolution, as well as relevant storm narratives. The complementary reanalysis-derived data is sourced from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and High-Resolution Rapid Refresh (HRRR) gridded data. For both gridded datasets, the maximum total snowfall (in units mm) was extracted, constrained by the county and datetimes specified by the observational report. ERA5 data covers the entire observational period (1997–2024), whereas HRRR data is only available from November 2016 – December 2024. Three CSV files are provided here: (1) the observational lake-effect snow event report, (2) ERA5 maximum snowfall detections for each event, and (3) HRRR maximum snowfall detections for each event. Relevant data from the observational files, such as impacted state and county, event datetimes, and event IDs, were included for convenience. Users can inspect and visualize the data using tools such as Microsoft Excel and Python pandas/matplotlib packages. This dataset may support a variety of applications, including climatological analyses of lake-effect snowfall, evaluation of snowfall representation in atmospheric datasets and numerical weather prediction models, and the development of machine learning approaches for detecting or predicting lake-effect snowfall events.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > SOLID↗
DOE OSTI2021

Visualizing Intrinsic 3D-Strain Distribution in Gold Coated ZnO Microstructures by Bragg Coherent X-Ray Diffraction Imaging and Transmission Electron Microscopy with Respect to Piezotronic Applications

Novel devices ranging from bio magnetic field sensors to energy harvesting nano machines utilize the piezotronic effect. For optimal function, understanding the interaction of electrical and strain phenomena within the semiconductor crystal is necessary. Here, studies of a model piezotronic system are presented, consisting of a ZnO microrod coated by a thin layer of gold, which forms a Schottky contact with the piezoelectric ZnO material. Coherent X-ray diffraction imaging (CXDI) and transmission electron microscopy (TEM) are used to visualize the structure and strain distribution, showing that the ZnO microrod exhibits strains of multiple origins in the bulk and at the interface. Strain values of -6 × 10 -4 have been measured by CXDI at the ZnO/Au interface. The origin is shown to be a combination of an interface strain, possibly caused by the Schottky contact formation, and distinct, localized electrical fields inside the crystal which are assigned to electron depletion and screening in a bent ZnO/Au piezotronic rod. These findings will contribute to sensor development and to a better understanding of piezotronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A novel assay to measure low-density lipoproteins binding to proteoglycans

Background: The binding of low-density lipoprotein (LDL) to proteoglycans (PGs) in the extracellular matrix (ECM) of the arterial intima is a key initial step in the development of atherosclerosis. Although many techniques have been developed to assess this binding, most of the methods are labor-intensive and technically challenging to standardize across research laboratories. Thus, sensitive, and reproducible assay to detect LDL binding to PGs is needed to screen clinical populations for atherosclerosis risk. Objectives: The aim of this study was to develop a quantitative, and reproducible assay to evaluate the affinity of LDL towards PGs and to replicate previously published results on LDL-PG binding. Methods: Immunofluorescence microscopy was performed to visualize the binding of LDL to PGs using mouse vascular smooth muscle (MOVAS) cells. An in-cell ELISA (ICE) was also developed and optimized to quantitatively measure LDL-PG binding using fixed MOVAS cells cultured in a 96-well format. Results: We used the ICE assay to show that, despite equal APOB concentrations, LDL isolated from adults with cardiovascular disease bound to PG to a greater extent than LDL isolated from adults without cardiovascular disease (p<0.05). Conclusion: We have developed an LDL-PG binding assay that is capable of detecting differences in PG binding affinities despite equal APOB concentrations. Future work will focus on candidate apolipoproteins that enhance or diminish this interaction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Multi-Dimensional Scaling on Groups

Leveraging the intrinsic symmetries in data for clear and efficient analysis is an important theme in signal processing and other data-driven sciences. A basic example of this is the ubiquity of the discrete Fourier transform which arises from translational symmetry (i.e. time-delay/phase-shift). Particularly important in this area is understanding how symmetries inform the algorithms that we apply to our data. In this paper we explore the behavior of the dimensionality reduction algorithm multi-dimensional scaling (MDS) in the presence of symmetry. We show that understanding the properties of the underlying symmetry group allows us to make strong statements about the output of MDS even before applying the algorithm itself. In analogy to Fourier theory, we show that in some cases only a handful of fundamental ``frequencies'' (irreducible representations derived from the corresponding group) contribute information for the MDS Euclidean embedding.

Dimensionality reduction, Representation theory, D↗
DOE OSTI2026

Impacts of Electric Space Heating in Multifamily Apartments in Juneau, Alaska [Slides]

The study objective was to understand the change in electrical consumption and cost after the addition of a ductless mini-split air-source heat pump (ASHP) across rental apartments heated by electrical resistance heating systems in multifamily buildings in Juneau, Alaska. Alaska Electric Light and Power Company (AELP), the utility in Juneau, Alaska, funded installation of 19 heat pumps across four buildings. Using electrical meter interval data, including the total apartment consumption and submeter data for each heating appliance (electric resistance baseboard and heat pump), the National Laboratory of the Rockies (NLR) calculated energy use metrics before and after heat pump installation and visualized results. Through this research, NLR compares electrical use (total and specific to heating appliances), adjusted for weather and apartment size, and electrical demand in the year before and after installation of an ASHP across study units. The results will inform the utility and residents of documented energy and cost savings from transitioning building heating systems to a higher-efficiency electricity-based appliance, deliver a basis for building energy modeling across other multifamily housing dwellings, and enable estimates of potential electricity savings to AELP grid under widespread adoption scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Data & Code from Phoenix CPPP Phase 2 Analysis

This data and code package supports the analysis presented in “Beyond Surface Cooling: Comprehensive Field Assessment of Reflective Pavement Thermal Performance in Phoenix, Arizona” and provides fully reproducible workflows for evaluating the thermal performance of cool pavement treatments in a hot urban environment. The dataset integrates multi-modal field measurements collected across residential and nonresidential settings, including mobile air temperature traverses, stationary air temperature monitoring, residential mean radiant temperature (MRT) measurements, subsurface temperature profiles, and controlled testbed observations. The data package contains raw and processed datasets in comma-separated value (CSV) format, accompanying metadata files describing site characteristics and measurement protocols, and R scripts (.R files) used for data cleaning, time synchronization, spatial and temporal matching, quality control filtering, statistical comparison, and figure generation. All analyses were conducted using R (version ≥ 4.2.0) with commonly available packages (e.g., tidyverse, lubridate, data.table, ggplot2). No proprietary software is required to reproduce results. Field campaigns were designed to quantify the effects of high-reflectance pavement coatings on surface temperature, near-surface air temperature, subsurface heat propagation, and radiative heat exposure. Temporal alignment procedures include standardized timestamp conversion and nearest-neighbor matching of high-frequency sensor measurements to stop-based metadata within defined tolerance windows to ensure comparability across instruments. The workflows generate summary statistics, treatment–control contrasts, depth-dependent thermal gradients, and time-series visualizations used in the associated publication. By integrating mobile, stationary, radiative, and subsurface measurements within a unified and transparent processing framework, this package enables comprehensive evaluation of cool pavement performance across multiple thermal exposure pathways and supports reuse in future urban heat mitigation and climate resilience studies.

AIR TEMPERATURE↗
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 OSTI2023

HarDWR - Raw Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, along with the geospatial data describing the spatial units where water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. We collected water rights databases from 11 western United States states either by downloading them from publicly accessible web portals, or by contacting state water management representatives; detailed descriptions of where and when the data was collected is provided in the README.txt, as well as Lisk et al.(in review). This collection of data are those raw water rights. Each state formats their data differently, meaning that file types, field availability, and names vary from state to state. Note, the data provided here reflects the state of the water rights databases at the time we collected the data; updates have likely occurred in many states. Some pieces of information are common among all states. These are: priority date, volume or flow of water allowed by the right, stated water use of the right, and some means of identifying the geography and source of the water pertaining to the right - typically the coordinates of the Point of Diversion (PoD) of a waterbody or well. Arizona regulates water in a different way than the other 10 states. Outside of some relatively small critical agricultural areas called Active Management Areas (AMAs), Arizona does not maintain any water rights. However, the state does require registration of surface and groundwater pumping devices, which includes disclosing the mechanical specifics of the devices. We used these records as a proxy for water rights. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. In addition, the following states have requested specifically worded disclaimers to be included with their data. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." The available data is provided as a series of compressed files, which each containing the full data collected from each state. Some of the files have been renamed, to more easily know which state the data belongs to. The file renaming was also required as some files from different states had the same name. In other cases, the data for a state has been placed in a folder indicating which state it belongs to - as the state organized its data by selected subregions. Below is a brief description of the format of the collected data from each state. ArizonaRights_StatementOfClaimants: A folder containing a database of interconnected CSV files. The soc_erd.pdf file contains a visual flowchart of how the various files are connected, beginning with SOC_MAIN.csv in the center of the page. ArizonaRights_SurfaceWaterRightsData: A folder containing a database of a single Shapefile and 10 associated CSVs. SurfaceWater.pdf contains a visual flowchart of how the various files are connected, beginning with ADWR_SW_APPL_REGRY.csv. ArizonaRights_Well55Registry: A folder containing a database of a single Shapefile and 59 associated CSVs. Wells55.pdf contains a visual flowchart of how the various files are connected, beginning with WellRegistry.shp. CaliforniaRights_eWRIMS_directDatabase: A folder containing a collection of four "series" Microsoft Excel files, as either XLS or XLSX. The four "series": byCounty, byEntity (what type of legal entity holds the right), byUse (stated water use), and byWatershed, are various methods by which the California water rights are organized within the state's database. However, it was observed that by only collecting a single series, not all water rights were being provided. So, essentially, the majority of records within each "series" are copies of each other, with each "series" containing some unique records. ColoradoRights_NetAmounts: A folder containing 78 CSV files, with one file per Colorado Water District. IdahoRights_PointOfDiversion: A Shapefile containing the Points of Diversion for the entire state of Idaho. IdahoRights_PlaceOfUse: A Shapefile containing the Place of Use polygons for the entire state of Idaho. MontanaRights_WaterRights: A Geodatabase file containing the Points of Diversion and Places of Use for the entire state of Montana. The name of the Points of Diversion Feature Layer within the Geodatabase is "WRDIV", and the name of the Places of Use Feature Layer is "WRPOU". NevadaRights_POD_Sites: A Shapefile containing the Points of Diversion for the entire state of Nevada. NewMexicoRights_Points_of_Diversion: A Shapefile containing the Points of Diversion for the entire state of New Mexico. OregonRights_state_shp: A folder containing 36 Shapefiles and are split between "pod" (Point of Diversion) and "pou" (Place of Use) for each water management basin within Oregon. In other words, each basin has one "pod" file and one "pou" file. The "pod" files are point shapes, and the "pou" files are polygons. UtahRights_Points_of_Diversion: A Shapefile containing the Points of Diversion for the entire state of Utah. WashingtonRights_WaterDiversions_ECY_NHD: A Geodatabase file containing both the Points of Diversion for the entire state of Washington. The name of the Feature Layer within the Geodatabase is "WaterDiversions_ECY_NHD". WyomingRights: A folder containing four subdirectories, one for each Wyoming Water Division. Each Division directory includes a varying number of subdirectories for each Wyoming Water District. Each District folder contains two copies of the Point of Diversion records for that area, with one copying being in CSV and one copy in Microsoft Excel XLS format.

Lisk, Matthew↗
DOE OSTI2025

Experimental Analysis of Advanced Turbine for Supercritical CO2 Power Cycle

An advanced first stage high pressure turbine blade for a supercritical CO2 power cycle is tested in the Big Rig for Aerothermal Stationary Turbine Analysis (BRASTA) annular cascade at the Purdue Experimental Turbine Aerothermal Laboratory (PETAL) high pressure blow down facility alongside a baseline blade for comparison of aerodynamic performance. Both geometries are tested simultaneously using a novel off-axis rig design to allow for the blade geometries to be scaled up to achieve higher Reynolds numbers. The off-axis design necessitates the use of discrete sectors of airfoils, which are designed and additively manufactured in house using an mSLA printer. Printing the blades allows for unique routing of passage for blade surface static pressure taps that contour to the blade shape instead of requiring a straight line view from surface tap to egress, allowing all instrumentation for 15%, 50%, and 85% span to exist in the same passage. A flow conditioning gauze [1] is placed upstream of the blade passages to impart pressure, Mach, and swirl profiles to mimic the rotor relative frame inlet conditions to the blade. Downstream, a sonic valve is used to alter the backpressure to achieve different pressure ratios for different blowdown setpoints. The design of the rig, sonic wheel, and instrumentation are discussed along with the manufacturing and GD&T of the blades with respect to the build-up of this novel experimental apparatus. Initial commissioning data is obtained from inlet total pressure and temperature rakes, blade static pressure taps, and exit total pressure rakes. Oil visualization is performed using a silicone oil mixture containing titanium dioxide and pigmented for contrast of the blade suction side surface with the hub and shroud endwalls. The viscosity of the oil is tuned so that the oil does not fully thin and blow away during the time of the test, and endoscopic cameras are placed in the rig for live monitoring and recording. The preliminary oil visualization and rake profiles are presented, showcasing the operability of this additively manufactured off-axis flow path design.

Tuite, Logan [Purdue University]↗