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Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics

A large and ever-growing body of geophysical information is measured in campaigns and at specialized observatories as a part of scientific expeditions and experiments. These collections of observed data include many essential climate variables (as defined by the Global Climate Observing System) but are often distinguished by a wide range of additional non-routine measurements that are designed to not only document the state of the environment but also the drivers that contribute to that state. These field data are used not only to further understand environmental processes through observation-based studies but also to provide baseline data to test model performance and to codify understanding to improve predictive capabilities. To address the considerable barriers and difficulty in utilizing these diverse and complex data for observation–model research, the Merged Observatory Data File (MODF) concept has been developed. A MODF combines measurements from multiple instruments into a single file that complies with well-established data format and metadata practices and has been designed to parallel the development of corresponding Merged Model Data Files (MMDFs). Using the MODF and MMDF protocols will facilitate the evolution of model intercomparison projects into model intercomparison and improvement projects by putting observation and model data “on the same page” in a timely manner. The MODF concept was developed especially for weather forecast model studies in the Arctic. The surprisingly complex process of implementing MODFs in that context refined the concept itself. Thus, this article explains the concept of MODFs by providing details on the issues that were revealed and resolved during that first specific implementation. Detailed instructions are provided on how to make MODFs, and this article can be considered a MODF creation manual.

54 ENVIRONMENTAL SCIENCES↗

Parallel Event Selection on HPC Systems

In their recent measurement of the neutrino oscillation parameters,NOvA uses a sample of approximately 25 million reconstructed spills to searchfor electron-neutrino appearance events. These events are stored in an n-tupleformat, in 250 thousand ROOT files. File sizes range from a few hundred KiB toa few MiB; the full dataset is approximately 1.4 TiB. These millions of eventsare reduced to a few tens of events by the application of strict event selectioncriteria, and then summarized by a handful of numbers each, which are used inthe extraction of the neutrino oscillation parameters.The NOvA event selection code is currently a serial C++ program that readsthese n-tuples. The current table data format and organization and the selection/reduction processing involved provides us with an opportunity to explorealternate approaches to represent the data and implement the processing. Werepresent our n-tuple data in HDF5 format that is optimized for the HPC environmentand which allows us to use the machine’s high-performance parallelI/O capabilities. We use MPI, numpy and h5py to implement our approach andcompare the performance with the existing approach. We study the performanceimplications of using thousands of small files of different sizes as comparedwith one large file using HPC resources. This work has been done as part of theSciDAC project, “HEP analytics on HPC” in collaboration with the ASCR teamsat ANL and LBNL.

Paterno, Marc↗

A Monte Carlo calculation model of electronic portal imaging device for transit dosimetry through heterogeneous media

Purpose: To develop and evaluate a fast Monte Carlo (MC) dose calculation model of electronic portal imaging device (EPID) based on its effective atomic number modeling in the XVMC code. Methods: A previously developed EPID model, based on the XVMC code by density scaling of EPID structures, was modified by additionally considering effective atomic number (Z{sub eff}) of each structure and adopting a phase space file from the EGSnrc code. The model was tested under various homogeneous and heterogeneous phantoms and field sizes by comparing the calculations in the model with measurements in EPID. In order to better evaluate the model, the performance of the XVMC code was separately tested by comparing calculated dose to water with ion chamber (IC) array measurement in the plane of EPID. Results: In the EPID plane, calculated dose to water by the code showed agreement with IC measurements within 1.8%. The difference was averaged across the in-field regions of the acquired profiles for all field sizes and phantoms. The maximum point difference was 2.8%, affected by proximity of the maximum points to penumbra and MC noise. The EPID model showed agreement with measured EPID images within 1.3%. The maximum point difference was 1.9%. The difference dropped from the higher value of the code by employing the calibration that is dependent on field sizes and thicknesses for the conversion of calculated images to measured images. Thanks to the Z{sub eff} correction, the EPID model showed a linear trend of the calibration factors unlike those of the density-only-scaled model. The phase space file from the EGSnrc code sharpened penumbra profiles significantly, improving agreement of calculated profiles with measured profiles. Conclusions: Demonstrating high accuracy, the EPID model with the associated calibration system may be used for in vivo dosimetry of radiation therapy. Through this study, a MC model of EPID has been developed, and their performance has been rigorously investigated for transit dosimetry.

60 APPLIED LIFE SCIENCES↗

SU-F-T-276: Source Modeling and VMAT Quality Assurance Referring to the TrueBeam Representative Beam Data for Eclipse

Purpose: To study quality assurance (QA) of volumetric modulated arc therapy (VMAT) after the 6MV and 10MV photon beam source modeling, referring to the Varian TrueBeam representative beam data for Eclipse. Methods: The source model needs specific measured beam data, such as PDDs and profiles, diagonal profile, output factors (OFs), and MLC transmission factor (TF) and dosimetric leaf gap (DLG), et al. We downloaded the representative data from myVarian website, which includes TrueBeam 4MV-15MV photon beam data and 6MeV-22MeV electron beam data in w2CAD file format for use with Eclipse and in Excel spreadsheet format for use in data comparison. The beam data in W2CAD format can be imported into the Eclipse system and calibrated for use, as appropriate. We used PTW MP3 water tank to measure the beam data in some typical field sizes, and compared the measured data with the representative data. We found that the PDDs, profiles and OFs are similar. However according to some papers and our measurements, we decided that our MLC TF and DLG are 1.58 and 1.33 (6MV), 1.79 and 1.57 (10MV), respectively. After we had configured the anisotropic analytical algorithm (AAA) with the representative data in Eclipse, we also have done dosimetric verification for 88 VMAT plans. Results: The end-to-end test procedures of VMAT were performed for 6MV and 10MV energy modes. The NE Farmer ion chamber mean measurements showed 1.2% (6MV, 38 cases) and 1.2% (10MV, 50 cases) between measurement and calculation; the Sun Nuclear ArcCheck mean measurements demonstrated gamma pass rates are as followings: 98.9%, 93.2%, 61.0% for 6MV, and 98.9%, 91.9%, 59.5% for 10MV, using 3%/3mm, 2%/2mm, 1%/1mm, 10% threshold criteria, respectively. Conclusion: The representative data is applicable to our TrueBeam for the VMAT plan, though our MLC factors are a little different, and its patientspecific QA is good.

60 APPLIED LIFE SCIENCES↗

Cyote-attack Chain Estimator

Attack Chain Estimator (ACE) Application Overview The Attack Chain Estimator (ACE) Application is a sophisticated tool designed for the ingestion, classification, sequencing, and enrichment of cybersecurity threat reports. This application leverages advanced machine learning models and extensive historical data to provide comprehensive insights into cyber threats, specifically targeting Industrial Control Systems (ICS). Purpose The primary functions of the ACE Application include: Ingestion of Cybersecurity Threat Reporting: Capable of ingesting text-based threat reports in markdown or text file format. Supports ingestion of structured data from other sources in STIX/JSON format. Classification of Report’s Text-Based Events: Utilizes a DeBERTa classifier, specifically trained on cybersecurity data, to map the events to MITRE ATT&CK for ICS Tactics and Techniques. Classification is performed using multiple Jupyter notebooks and machine learning workflows hosted as FastAPI microservices: regex_data deberta_base_35_train_hft_classifier_mlflow.ipynb hft_regex_classifier_mlflow.ipynb param_train_hft_classifier_mlflow.ipynb regex_tactic_tech.ipynb Ordering of Tactics, Techniques, and Observable Events: Sequences the identified tactics, techniques, and events to form a coherent attack chain. Enrichment with Historical Attack Chain Details: Enhances the attack chain with details from historical attacks using a Markov model developed from CyOTE Precursor Analysis Report data. The Markov model is available as a FastAPI endpoint for seamless integration. Enrichment with Adversary Emulation Capabilities Data: Integrates adversary emulation capabilities data using MITRE Caldera for OT adversary abilities UUIDs. Export of Output Files: Provides options to export the enriched attack chain in JSON or CSV formats. Routing of Output to Other Applications: Facilitates routing of output to various platforms and applications, including: Threat Intelligence Platforms COREII Scout for Threat Intelligence Analysis COREII Modeling and Simulation for Adversary Emulation Technical Description The ACE Application is an advanced cybersecurity tool designed to provide detailed threat analysis and sequence generation. It is built on a robust architecture that integrates natural language processing, machine learning, and historical data modeling. Key Components: Data Ingestion Module: Handles the input of threat reports and data from various formats, ensuring flexibility in data sources. Classification Engine: Employs DeBERTa-based classifiers hosted as FastAPI microservices to analyze and classify threat report events in accordance with the MITRE ATT&CK framework for ICS. Sequence Generator: Orders the classified events into a logical attack chain, providing clear insight into the sequence of tactics and techniques used in the threat. Enrichment Engine: Integrates historical data and adversary emulation capabilities to enhance the attack chain with valuable context and additional details. The historical data enrichment is powered by a Markov model, which is available as a FastAPI endpoint. Export and Routing Module: Facilitates the export of the enriched attack chain in multiple formats and routes the output to designated applications for further analysis or emulation.

Paul, Tony [Idaho National Laboratory (INL), Idaho↗

EGS Collab Experiment 2: Testbed Well Orientation Data

This dataset contains the north seeking gyro data for each of the 11 boreholes drilled at the Experiment 2 testbed on the 4100 foot level of the SURF (Sanford Underground Research Facility). Each gyro file is in individual folders for each well. A single folder called 'E2 All Well Trajectories' contains the trajectories for all the wells but in the mine coordinate system. An image of the well layout is provided in a PowerPoint.

15 GEOTHERMAL ENERGY↗

Depth and elevation of floodplain fine sediment along the East River near Crested Butte, Colorado measured in 2016 and 2017

The depth of floodplain fine sediment was measured at 1,601 points along the East River near Crested Butte, Colorado spanning a study reach approximately 800 m in length in 2016 and 2017. These depths are being used to estimate the volume of floodplain sediment present along the study reach, converted into mass of sediment using bulk density measurements, and finally used to estimate the mass of organic carbon stored in the floodplain based on elemental analysis of percent organic carbon content. Recorded depths include a minimum of 1 cm, a median of 56 cm, a maximum of 175 cm, and a mean with standard deviation of 61 ± 34 cm. This data include a csv file of the coordinates for each point in NAD 83 UTM zone 13, measured sediment depths, distance from the river channel active during 2015 measured using geographic information systems, the elevation of the floodplain surface derived from 0.5-m resolution lidar, and the elevation of the underlying gravel/bedrock surface, and in some cases the distance from the active channel measured in the field along the sampling transect. Also included is an ESRI ArcMap shapefile of the measurement points complete with the data listed above.

54 ENVIRONMENTAL SCIENCES↗

Airborne Imagery Collections, Utqiagvik (Barrow), Alaska, 2013

The data here are orthomosaics, digital surface models (DSMs), and individual frames captured during low altitude airborne flights in 2013 at the Barrow Environmental Observatory. The orthomosaics, thermal IR mosaics, and DSMs were generated from the individual frames using Structure from Motion techniques. Data were collected to characterize the optical and thermal properties of the landscape and how they differ between two points in time with different vegetative and hydrologic conditions. The orthomosaic tiles, thermal IR mosaics, and DSMs are geotiffs in NAD 83 UTM Zone 4 named by their lower-left coordinate. Metadata for the raw imagery are contained in the .exif headers in the files for the optical imagery only. XYZ files contain the IR temperatures at various longitudes and latitudes. Supported by the U.S. DOE Arctic NGEE program and the Alaska Climate Science Center. The LiDAR dataset used for accuracy assessment was provided by Cathy Wilson(cjw@lanl.gov) and Chandana Gangodagamage(chandana@lanl.gov). File types included: *.tif; *.png; *.kmz; *.bmp; *.pgw, *xyz, *.jpg. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NGEE Arctic Plant Traits: Shrub Transects, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, 2016-2017

Transect data is from point center quarter (PCQ) surveys for shrub density performed in July 2016 at the Kougarok site and in July 2017 at the Kougarok, Teller, and Council sites. For each sample point along the transects, moving averages for shrub density and shrub basal area are provided along with GPS coordinates. Average shrub height and active layer depth are recorded for each sample point as well. Target shrub species differ across the transects: some surveys targeted a mixture of tall shrub species while others targeted specific species. In 2017 insect damage was also quantified. The dataset contains two *.csv data files and one *.pdf. Missing Values are represented by "NA". The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Performance regression manager for large scale systems

System and computer program product to perform an operation comprising generating, based on a first output generated by a first execution instance of a command, a first output file specifying a value of at least one performance metric, wherein the first output file is formatted according to a predefined format, comparing the value of the at least one performance metric in the first output file to a value of the performance metric in a second output file, the second output file having been generated based on a second output generated by a second execution instance of the command, and outputting for display an indication of a result of the comparison of the value of the at least one performance metric of the first output file to the value of the at least one performance metric of the second output file.

97 MATHEMATICS AND COMPUTING↗

Performance regression manager for large scale systems

System and computer program product to perform an operation comprising generating, based on a first output generated by a first execution instance of a command, a first output file specifying a value of at least one performance metric, wherein the first output file is formatted according to a predefined format, comparing the value of the at least one performance metric in the first output file to a value of the performance metric in a second output file, the second output file having been generated based on a second output generated by a second execution instance of the command, and outputting for display an indication of a result of the comparison of the value of the at least one performance metric of the first output file to the value of the at least one performance metric of the second output file.

97 MATHEMATICS AND COMPUTING↗

INTEGRATED WORKFLOW MANAGEMENT FOR PARTICLE ACCELERATOR SIMULATION

Supercomputing systems are used for a wide range of computationally demanding tasks in many fields of science and engineering. They play a key role in numerical simulation, in which mathematical models are computed in order to simulate the behavior of physical systems. Scientists and engineers that use supercomputers for numerical simulation often have their productivity limited by the need to manually organize and manage extremely large amounts of data that are often produced and consumed by the software programs run on these systems. Recognizing these limitations, Kitware Inc. (Clifton Park, NY) and SLAC National Accelerator Laboratory (Menlo Park, CA) are developing an advanced software platform that can reduce the cognitive overhead required by knowledge workers when using supercomputers for numerical simulation. Phase I of the project is complete and includes the development of new capabilities for organizing simulation project files, improvements to the user interface and overall usability, and deployment of a “middle tier” server to sit between user desktop machines and supercomputers to offload much of the data management workload. The project also developed prototype software for executing sequences of numerical simulations, and a prototype for migrating supercomputing software to cloud-based computing systems to provide a potential alternative to supercomputers with different logistical and price-to-performance tradeoffs.

Tourtellott, John↗

Final Report UCLA-Thermochemical Storage with Anhydrous Ammonia

In ammonia-based thermochemical energy storage (TCES), ammonia is dissociated endothermically as it absorbs solar energy during the daytime. When energy is required, the reverse reaction releases energy to heat a working fluid such as steam, to produce electricity. Ammonia-based TCES has great advantages of simplicity, low cost reactants, and a strong industrial base in the conventional ammonia industry. The concept has been demonstrated over three decades of research at Australian National University, achieving a 24-hour demonstration of a complete system. At the start of this project, three challenges were identified that would have to be addressed to show that the system is technically and economically viable for incorporation into a CSP plant with an advanced, high temperature power block. All three of these challenges have now been addressed: 1. The ammonia synthesis reaction had not, to our knowledge, been carried out at temperatures consistent with modern power blocks (i.e., ~650°C). The technical feasibility of operating a reactor under high-temperature, near-equilibrium conditions was an unknown, and was therefore a technical risk. The project has successfully demonstrated steam heating to 650°C and energy recovery to steam at the 5 kWt level. 2. The ammonia system has a relatively low enthalpy of reaction combined with gas phase reactants. This is not a direct disadvantage since the reactants themselves are low cost. The challenge lies in storing the required volume of reactants cost effectively. Therefore, a second key goal was to show, through techno-economic analysis, that underground storage technologies can be used to store the energy-rich gas at a cost that is consistent with the SunShot cost goal. We have identified two promising technologies for gas storage: storage in salt caverns has an estimated cost of 1(USD)/kWht and storage in drilled shafts could be on the order of 7(USD)/kWht. Together these two options answer the technical challenge associated with storage of gas phase components. 3. While this project is primarily concerned with high-temperature heat recovery and methods to store the gaseous components, it is also important to consider the feasibility of the entire system. Consequently, an additional goal was to perform analysis to show the feasibility of integrating endothermic reactors within a tower receiver. A conceptual design of an ammonia dissociation receiver/reactor has been developed that fits into the same size cylindrical envelope as the molten salt receiver in SAM, and has the same design thermal capacity. The calculated thermal efficiency of this receiver is 94.6%. Thus, this investigation has established the technical feasibility of a surround field tower system using ammonia dissociation. With these challenges addressed, we proceeded to design a full-scale synthesis and heat recovery system. A model was developed and validated by comparison with our experimental data. A parametric study showed, among other things, the importance of using small tube diameters and spacing to enhance heat transfer. Multi-parameter optimization was used to find a design that minimizes the wall material volume. Finally, cost estimation shows that the ammonia system has good prospects of meeting the Sunshot 15(USD)/kWht target: estimated costs of the entire synthesis system for the 220 MWt plant with 6 hours of storage are 13(USD)/kWht using salt cavern storage and 18(USD)/kWht using shaft drilling. Costs per kWht are even lower with more hours of storage. With the established technology of ammonia synthesis as a starting point, the successes of the project have mitigated technical risks associated with high-temperature synthesis reaction, underground storage, and tower receiver design. Estimated costs are less than 15(USD)/kWht with salt cavern storage. It is now possible to map a time line to commercial deployment that is likely to be shorter and less risky than other thermochemical cycles under active investigation. UCLA has filed a patent that protects the new ideas developed during this project. Discussions are ongoing with potential investors with the aim of partnering for further work. As well as immediate improvements and extra work with the existing experimental system, a key goal is to extend it to a small solar-driven project at an early opportunity.

14 SOLAR ENERGY↗

A Python Tool for Aqueous Plutonium Nitrate Density Law Input Preprocessing in MCNP6

Here, this work develops a predictive density tool in Python, named Plutonium Nitrate Solutions (PuNS), to reduce bias and uncertainty in nuclear criticality safety calculations for plutonium nitrate systems. The Pitzer method and an empirical method were implemented into the PuNS tool to generate atom densities for use in MCNP6 material cards. These material cards are directly prepared into an MCNP6 input text file and are calculated based on customizable user inputs of plutonium content, nitric acid content, temperature, and plutonium isotope weight percentages. The PuNS tool is validated and verified against the International Criticality Safety Benchmark Evaluation Project Handbook experiments and is observed to predict densities within a root mean square error of 0.89% for the Pitzer method and 1.82% for the empirical method. These errors in density lead to up to 1569 pcm difference in MCNP6 calculated k eff for the Pitzer method and up to a 1751 pcm difference for the empirical method when compared to experimental benchmarks. Simultaneous work is also being performed at Los Alamos National Laboratory and the University of New Mexico to create a similar tool for plutonium chloride solutions, named Plutonium Chloride Solution, which aims to provide the accreditation of the chlorine absorption. These capabilities will not only provide more accurate models but also facilitate an improved understanding of solution systems and a potential relaxation in the conservatism of current aqueous plutonium processing criticality safety limits.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Active Layer and Moisture Measurements for Intensive Site 0 and 1, Utqiagvik (Barrow), Alaska

Thaw depth, relative dielectric permittivity, and soil temperature were collected along three transects within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) near Utqiagvik (Barrow), Alaska. Measurements were acquired along the transect at 3 m intervals on 2011-09-25, 2011-09-26, 2012-07-18, 2012-09-30, 2014-07-02, 2014-08-15, and 2014-09-14 for the L2 transect (intensive site 0), which is about 470 m long. Similarly, measurements were collected every 3 m on 2012-09-30, 2013-09-30, 2014-07-02, 2014-08-14, and 2014-09-23, and every 1 m on 2013-09-30 along the AB transect (intensive site 1) which is about 500 m long. File types in the package are all csv spreadsheets, following the ESS-DIVE csv guidelines (ESS-DIVE CSV v1), organized by transect name, with accompanying data dictionary files containing the filename suffix "_dd" (also csv format). Data collection details can be found in the Methods description of the metadata record. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

Web-based Preprocessing and Visualization of 3D FIB Tomography Data for Nuclear Fuel Characterization

Three-dimensional (3D) focused ion beam (FIB) tomography enables reconstruction of internal nuclear fuel features that can't be fully evaluated through surface imaging alone. This capability supports characterization of fuel constituents and defects under thermal and irradiation conditions relevant to microreactor development. However, large tomography datasets can create data-handling, loading, and visualization challenges, especially when image-stack preparation and file conversion must be completed with separate tools. The Computational Ultraspatial Tomography Toolkit for High-Resolution Object Analysis Tools (CUTTRHOAT) is an open-source web application being developed to display FIB tomography datasets available through the Nuclear Research Data System (NRDS). The current alpha version requires prepared HDF5 datasets and has limited integrated data-preparation capabilities. This project improves CUTTHROAT by adding dataset-folder selection, automatic input detection, dataset scanning, missing-slice identification, blank-slice insertion, and image-stack-to-HDF5 conversion. Two applications will be compared: the baseline CUTTHROAT alpha workflow and the updated application containing the integrated data-handling and preprocessing functions. Evaluation will consider dataset detection accuracy, conversion success, loading time, rendering responsiveness, application stability, and user interaction. Preliminary results demonstrate successful loading of existing HDF5 files and converted image stacks, while testing also identified performance reductions caused by excessive blank-slice generation. The updated workflow reduces reliance on external preparation tools and supports more direct movement from image stacks to color-code 3D visualization. Future work includes refining missing-slice handling, integrating additional preprocessing functions, like a denoising feature, parsing TIFF metadata for automatic voxel scaling, and adding manual X, Y, and Z voxel-spacing inputs for PNG and JPEG.

36 - MATERIALS SCIENCE↗

Hydroboost

HydroBoost is the most realistic revenue optimization tool for the hybridization of hydropower and battery energy storage systems to date. The innovative representation of how operators actually schedule hydropower in practice results in more realistic predictions of revenue and operations. Unlike other optimization tools, HydroBoost generates forecast energy prices with uncertainty to use in the optimization. This allows HydroBoost to give users a range of potential revenue with an upper bound using the perfect foresight pricing and a lower bound using a naive persistence forecast model. Additional forecast can be generated and used in the optimization, such as additive models, random forest, and neural networks to give further insight into potential revenue. HydroBoost has been designed to be applicable for both run-of-river and reservoir storage sites. The primary focus is on the day-ahead market and requires year-long data with an hour time-step. All time-series input and constraints are contained in an Excel worksheet for convince. The user will run the forecasting generation first with a Python script to give the optimization model the necessary requirements. Next the optimization is ran using Julia and results are generated and stored into a directory as csv files. HydroBoost includes an additional module to generate figures based on the results of the optimization simulation. The results help analyze the results and users to draw insights into how the hydro and battery systems are operated and the revenue each is producing. Additionally, the difference between the perfect foresight model and models that include forecast can easily be inspected.

Phillips, TylerB. [Idaho National Laboratory (INL)↗

The Role of the U.S. Electric Distribution System in Serving Data Center and Other Large Loads

The rapid expansion of data centers in the United States is reshaping how the electric distribution system must plan for and accommodate large load interconnections. This report evaluates the role of the distribution grid in serving these loads, from small edge facilities to hyperscale campuses. Using national datasets, utility filings, and industry studies, we assess demand growth, reliability requirements, interconnection thresholds, and infrastructure needs at substations and feeders. The analysis highlights the mismatch between fast data center development timelines and slower utility planning and construction cycles, as well as strategies such as phased energization, on-site generation, hosting capacity maps, and structured interconnection frameworks. While focused on data centers, the insights also apply to other high-density loads such as advanced manufacturing, hydrogen production, and electrified transportation. The report concludes with approaches to align planning processes, transparency tools, and regulatory frameworks so utilities can manage new large loads in ways that support a reliable and resilient grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗