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DOE OSTI2025

HyRAM+ (Hydrogen Plus Other Alternative Fuels Risk Assessment Models) v.6.1

SAND2025-11565O HyRAM+ (Hydrogen Plus Other Alternative Fuels Risk Assessment Models) is a tool for conducting quantitative risk assessment (QRA) in transportation systems. HyRAM+ contains validated, simplified release behavior models, engineering models, and generic data relevant to hydrogen installations. HyRAM+’s platform integrates models and data to conduct QRA on user-defined hydrogen or other alternative fuel systems. The software will enable the international safety research community to add validated models to the HyRAM+ platform for use in QRAs. HyRAM (hydrogen-only) versions 1.0 to 3.1 were developed by Sandia for the Department of Energy (DOE) Hydrogen and Fuel Cell Technologies Office. The following agencies contributed to the development of HyRAM+ version 4.0 regarding the addition of methane (natural gas) and propane models: the DOE Vehicle Technologies Office and the Department of Transportation Pipeline and Hazardous Material Safety Administration. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Groth, Katrina [Sandia National Lab. (SNL-NM), Alb↗

DOE OSTI2024

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

36 MATERIALS SCIENCE↗

DOE OSTI2022

Harmonizing solar induced fluorescence across spatial scales, instruments, and extraction methods using proximal and airborne remote sensing: A multi-scale study in a soybean field

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. Here, in this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O 2 A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

National Renewable Energy Laboratory, South Table Mountain Campus, Golden, Colorado

The National Renewable Energy Laboratory (NREL) South Table Mountain campus, located in Golden, Colorado, has multiyear datasets from buildings with approximately 1,100,000 sq ft of floor area. These 16 buildings include a large office, nine laboratories, and other education or public assembly facilities such as an education center, a warehouse, quick service restaurants, two site entrance security buildings, and a parking garage—all constructed and improved from 1982 to 2014. These datasets are also available from the NREL data storage and management platform SkySparks. The datasets contain electricity meter data (poser, voltage, and current) with 1-minute resolution and building automation system (BAS) data with resolutions of 5 or 15 minutes. Some subsystems (e.g., lighting) or equipment electricity data have the same intervals as above. Some mechanical equipment state or condition data, such as flow and temperature, are captured with 5- to 15-minute intervals. The types of measurement points (i.e., electrical meter power and zone temperature) and associated data quality across the campus are typical, which helps reduce the unreliability of data quality.

STM, Systems and equipment operational, Energy use↗

DOE OSTI2020

Assessing Current and Future Infrastructure Hazards

Project Objective Execute intelligent analytics via an advanced analytical framework, to assess the current state of offshore infrastructure, evaluate infrastructure life, and identify technologies to reduce infrastructure hazards, costs, and extend infrastructure life. Approach & Results thus Far• Build comprehensive dataset• Perform data-driven analytics to evaluate infrastructure integrity 1. Remaining lifespan 2. Likelihood of future risk• Apply data-driven advanced spatial, statistical, and Machine Learning (ML) models to quantify existing infrastructure integrity• Release data and models through a smart, online platform hosted by Energy Data eXchange (EDX)

Romeo, Lucy F.↗

DOE OSTI2022

MIRACL Co-Simulation Platform Lab assets and tools integration

Pacific Northwest National Laboratory's (PNNL) co-simulation platform (CSP) for the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project, also known as MIRACL-CSP, is a functional layer designed and developed to oversee the operational exchanges of data at the application level to and from different resources residing on the MIRACL Data Hub shared platform. MIRACL-CSP allows virtual interactions between various data hub resources during co-simulation runtime.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

University of Colorado and Black Swift Technologies RPAS-based measurements of the lower atmosphere during LAPSE-RATE

Abstract. Between 14 and 20 July 2018, small remotely piloted aircraft systems (RPASs) were deployed to the San Luis Valley of Colorado (USA) together with a variety of surface-based remote and in situ sensors as well as radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The observations from LAPSE-RATE were aimed at improving our understanding of boundary layer structure, cloud and aerosol properties, and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. The current paper describes the observations obtained using four different types of RPASs deployed by the University of Colorado Boulder and Black Swift Technologies. These included the DataHawk2, the Talon and the TTwistor (University of Colorado), and the S1 (Black Swift Technologies). Together, these aircraft collected over 30 h of data throughout the northern half of the San Luis Valley, sampling altitudes between the surface and 914 m a.g.l. Data from these platforms are publicly available through the Zenodo archive and are co-located with other LAPSE-RATE data as part of the Zenodo LAPSE-RATE community (https://zenodo.org/communities/lapse-rate/, last access: 27 May 2021). The primary DOIs for these datasets are https://doi.org/10.5281/zenodo.3891620 (DataHawk2, de Boer et al., 2020a, e), https://doi.org/10.5281/zenodo.4096451 (Talon, de Boer et al., 2020d), https://doi.org/10.5281/zenodo.4110626 (TTwistor, de Boer et al., 2020b), and https://doi.org/10.5281/zenodo.3861831 (S1, Elston and Stachura, 2020).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Innovation in Radiological Security, Part 2 of 2 – Insights into Developing a Cloud-hosted Security Technology

A Sentry Remote Monitoring System (Sentry-RMS) is a stand-alone security system that provides detection, assessment, and communication of priority alarms as an additional means of thwarting internal and external threats to sites that maintain radiological material. The SEntry-RMS CommUnications and REsponse (Sentry-SECURE) platform is an optional feature of the Sentry-RMS that relays priority alarm information to the identified response stakeholders. Sentry-SECURE is hosted in a cloud environment that abstracts the data owner’s and data consumer’s platforms to allow for greater information sharing. This promotes situational awareness amongst authorized users and enables future innovation among modern response platforms. When securely architecting a cloud solution such as this, the use of design paradigms can be an effective tool to increase the accuracy and reliability of cyber- and information-security-related decisions made throughout the development process. This approach also supports the categorization of design considerations into three levels: industry concepts, project approaches, and data protections for digital processes. Industry concepts consist of the notional underpinnings that guide or motivate a security process, system, or design but often lack any tangible attributes. Project approaches represent decisions made during the design and development process to prioritize a solution, method, or practice above another that may provide a comparable functional output but lacks a desired security benefit. Data protections for digital processes represent the selection, integration, and implementation of specific controls for a given asset. This paper will explore specific examples of how Sentry-SECURE has been designed to account for considerations at each of these three levels, while balancing the operational intent of the platform with the security enhancements necessary to maintain data integrity, availability, and confidentiality.

assessment, RMS, security, physical, cloud, Cyber ↗

DOE OSTI2022

The Astronomy Commons Platform: A Deployable Cloud-based Analysis Platform for Astronomy

Abstract We present a scalable, cloud-based science platform solution designed to enable next-to-the-data analyses of terabyte-scale astronomical tabular data sets. The presented platform is built on Amazon Web Services (over Kubernetes and S3 abstraction layers), utilizes Apache Spark and the Astronomy eXtensions for Spark for parallel data analysis and manipulation, and provides the familiar JupyterHub web-accessible front end for user access. We outline the architecture of the analysis platform, provide implementation details and rationale for (and against) technology choices, verify scalability through strong and weak scaling tests, and demonstrate usability through an example science analysis of data from the Zwicky Transient Facility’s 1Bn+ light-curve catalog. Furthermore, we show how this system enables an end user to iteratively build analyses (in Python) that transparently scale processing with no need for end-user interaction. The system is designed to be deployable by astronomers with moderate cloud engineering knowledge, or (ideally) IT groups. Over the past 3 yr, it has been utilized to build science platforms for the DiRAC Institute, the ZTF partnership, the LSST Solar System Science Collaboration, and the LSST Interdisciplinary Network for Collaboration and Computing, as well as for numerous short-term events (with over 100 simultaneous users). In a live demo instance, the deployment scripts, source code, and cost calculators are accessible. 4 4 http://hub.astronomycommons.org/

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary

This data was collected between October 12 and December 15 of 2021 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). This data set includes over 29 days of grid connected turbine operation during this 65 day time frame. The priority for this measurement campaign was to collect data while the turbine was electrically connected to the grid by means of a rectifier and inverter. The Fall_2021_UNH_Measurement_Timeline.png highlights when each instrument was functioning and the Fall_2021_UNH_Test_Log.jpg indicates the four main regions for analysis available from this measurement campaign. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal forcing with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Kaelin Chancey (Assessment Of The Localized Flow And Tidal Energy Conversion System At An Estuarine Bridge - UNH MS Thesis 2019). The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. This project was pursued to quantify device performance for cross flow turbines operating in a marine environment. Accurate physical models, to characterize cross flow turbine performance, require real operational data sets due to the complexity of blade fluid interactions. This data can help support model development which will help predict turbine performance when analyzing perspective project locations in the future. Instrumentation was deployed to measure; water speed/direction, electrical power output, turbine shaft speed, turbine thrust force, and platform motion. Concurrent measurements of these parameters allow for correlations (cause and affect) to be inferred, allowing for characterization of device performance over a range of operating conditions. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) directly upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier and the Shark power meter. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. This submission includes zipped folders for each instrument containing quality controlled (QC'd) data in daily .csv files for the relevant duration specific to each instrument, along with separate .csv file that contains the units for each variable. Some instrument daily files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. Note the degree of QC varied between each instrument due to time constraints. Particular time and attention was given to perform quality control tests on the acoustic based instruments that are particularly suscep...

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

A Hardware Platform for Studying Naval Power Electronic Power Distribution Systems

Abstract – Future intelligent ship system designs will likely include electric propulsion, numerous highpower sensors, and directed energy weapons. Supply and control of these large nonlinear loads will require a networked, multi-converter power electronic power distribution system. This work presents a hardware platform to emulate a microgrid power system with multiple power converters and a power data communication network. The platform is reconfigurable and can include both AC and DC power distribution zones, representative of shipboard power systems. It also allows for the study of both power control actuation and power data communication delays. Since the platform is based on electric power hardware, spatial and temporal uncertainties are inherently embedded in the system. Specifically, this work examines the control actuation of multiple pulsed power loads in a single microgrid. Several pulse load levels and operating scenarios have been implemented and measured. A framework for control parameter quantification is presented, and various metrics are explored to capture pulse signal characteristics. The sensitivity of pulse load metric parameters is analyzed. Dynamic shipboard, mission-specific load profiles coupled with pulse loads can also be emulated in the hardware platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

A CIM Based Data Integration Framework for Distribution Utilities

With the proliferation of distributed energy resources and advanced metering, modern electric power distribution systems are data rich and include advanced capabilities for distribution automation. Distribution utilities need applications for planning and operations that can integrate all the available data from the enterprise applications and may incorporate distributed approaches to operate and control. This paper describes a framework for standardizing and integrating the data available at different vendor specific applications in a utility utilizing the Common Information Model (CIM). Theframework incorporates the conversion of these the standardized CIM models into models compatible with GridLAB-D, an open source distribution system simulator, for developing advanced planning and operation strategies.

data integration platform, object-oriented data mo↗

DOE OSTI2024

DOE Data Days 2023 Report

The DOE Data Days (D3) workshop brings together data managers, developers, researchers, and program managers across the Department of Energy (DOE) and national laboratories to highlight data management successes, identify potential synergies and common problems, and establish channels for collaboration across the DOE data management community. The fourth D3 workshop was held on October 24th to 26th, 2023 held entirely in-person at Lawrence Livermore National Laboratory (LLNL). The workshop was organized by a multi laboratory committee in an effort to bring data management practitioners at the DOE laboratories together to share their work and results, facilitating knowledge transfers and best practices across project teams. Tools and platforms to support data management and analysis are rapidly evolving and provide enormous opportunities. This report summarizes the important discussions and recommendations from the different working sessions and contains the agenda, submitted abstracts, presentations with links to recorded presentations, breakout session summaries, list of registered attendees, and lessons learned for future organizing committee. The report will be distributed to the DOE, each participating institution’s programmatic stakeholders, and attendees. The dedicated D3 website will host presentations, agenda, and report that is accessible by all labs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

S0 Uncrewed Aircraft System Measurement Characterization Field Campaign Report

The lower atmosphere contributes directly to the modulation of weather and climate. Understanding the importance of this part of the atmosphere, scientists have worked to improve the representation of the atmospheric boundary layer in numerical prediction tools. Such work depends upon information from observing systems, including remote sensors, weather balloons, and research aircraft. Recent years have seen significant advances in uncrewed aircraft systems (UAS) for atmospheric research. Those efforts have provided new perspectives on atmospheric and surface conditions, particularly from smaller UAS (sUAS) platforms. This field campaign included collection of data using several sUAS, and comparing the sUAS data to those from different observing facilities at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory in Oklahoma. Over the course of one week, the Black Swift Technologies and University of Nebraska teams conducted flights for the sole purpose of system evaluation and intercomparison. In addition, these teams conducted joint flight operations at the Marshall, Oklahoma Mesonet site alongside teams from the University of Colorado, University of Oklahoma, and Oklahoma State University to collect additional data for side-by-side comparison. Platforms operated by Black Swift Technologies and the University of Nebraska under this project included one fixed-wing and two rotary-wing platforms, as well as an instrumented surface vehicle. These include the Black Swift Technologies S0 UAS, the University of Nebraska M600 UAS, a University of Nebraska-operated Meteomatics Meteodrone UAS, and the University of Nebraska CoMeT (Combined Mesonet and Tracker) vehicle. Additional details on all of these platforms can be found in de Boer et al. (in prep). In total, 95 sUAS flights were conducted for a combined total of 18.6 flight hours. The S0 was operated both to the south and north of the SGP 60 m tower, while the M600 and Meteodrone were operated directly to the east of the tower. Weather conditions were generally good, with moderate winds. Flights were conducted both to follow the extra radiosondes that were launched as part of this campaign, as well as to conduct extended statistical sampling at tower instrument heights. The later flights also provided data for statistical platform intercomparison.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Synchronized Waveforms – A Frontier of Data-Based Power System and Apparatus Monitoring, Protection, and Control

Voltage and current waveforms contain the most authentic and granular information on the behaviors of power systems. In recent years, it has become possible to synchronize waveform data measured from different locations. Thus large-scale coordinated analyses of multiple waveforms over a wide area are within our reach. This development could unleash a set of new concepts, strategies, and tools for monitoring, protecting, and controlling power systems and apparatuses. This paper presents an in-depth review and analysis of the advancements in synchronized waveform data, including measurement devices, data characteristics, use cases, and comparisons with synchrophasor data. Based on the findings, five strategies are proposed to discover and develop synchronized waveform based applications over multiple application areas. The paper also presents three complementary measurement platforms and two data screening algorithms for application implementation. It further discusses committee activities and standard developments useful to explore the full potential of the data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Position Papers for the 2024 ASCR Workshop on Energy-Efficient Computing for Science

On behalf of the Advanced Scientific Computing Research (ASCR) program in the US Department of Energy (DOE) Office of Science, we are organizing a Workshop on Energy-Efficient Computing for Science (EECS). Energy efficiency involves coordination across all the interoperating components of a computing system—in particular, applications, algorithms, system software, programming models, data management, and the hardware on which they run. Looking 10-15 years into the future, the goal is to dramatically lower the energy costs of the computational platforms (from the data center to the edge) serving DOE science while expanding the capabilities of these systems, broadening their applicability to science challenges of interest to DOE and the nation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

An Open-Source Framework for Characterizing Urban Energy Models: Integrating Top-Down and Bottom-Up Methods to Predict Residential Buildings Characteristics: Preprint

Bottom-up urban energy models are crucial for understanding current energy use patterns and informing design strategies. However, accurately characterizing these models to represent different communities remains a challenge due to the extensive data needed for simulating existing energy use behavior. This data includes information related to human activities and building characteristics, all of which correlate with socioeconomic factors. To overcome this challenge, we developed an automated framework that utilizes both top-down and bottom-up data, to predict unknown building and occupant characteristics that are needed for more accurate and equitable modeling and analytics. Our framework, integrated into the URBANopt district energy modeling platform, uses statistical data models from ResStock. URBANopt models co-located buildings and neighborhoods. At this scale there are data gaps in building characteristic data, such as materials, insulation, occupancy, income, and energy usage of the buildings. To address this data gap, we use ResStock data, representative at the census tract scale, and develop machine-learning and deeplearning techniques to disaggregate it to individual buildings. By mapping unique occupant, building and economic properties to URBANopt energy models, we gain detailed insights into the variability of building energy use across different neighborhoods. This insight helps deploy technologies for co-located buildings and supports targeted upgrades for communities with unique economic and demographic characteristics, ensuring energy equity. Accurate characterization of energy models allows us to develop equitable strategies tailored to diverse neighborhoods, whether underserved or affluent. Our automated framework streamlines energy modeling and provides a reliable tool for building energy characterization.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗