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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Computing erasure metadata and data layout prior to storage using a processing platform

Techniques are provided for computing data and metadata layout prior to storage in a storage system using a processing platform. An exemplary processing platform comprises one or more of a compute node and a burst buffer appliance. The processing platform communicates with a plurality of the compute nodes over a network, wherein a plurality of applications executing on the plurality of compute nodes generate a plurality of data objects; computes erasure metadata for one or more of the data objects on at least one of the compute nodes; and provides the erasure metadata with the corresponding one or more data objects to a storage system. The processing platform optionally determines a full set of the data objects to be stored and queries the storage system to determine an anticipated layout of the full set of the data objects to be stored. The anticipated layout allows special handling, for example, for small files and large files that are identified based on predefined criteria.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

An investigation of irreproducibility in maximum likelihood phylogenetic inference

Phylogenetic trees are essential for studying biology, but their reproducibility under identical parameter settings remains unexplored. Here, we find that 3515 (18.11%) IQ-TREE-inferred and 1813 (9.34%) RAxML-NG-inferred maximum likelihood (ML) gene trees are topologically irreproducible when executing two replicates (Run1 and Run2) for each of 19,414 gene alignments in 15 animal, plant, and fungal phylogenomic datasets. Notably, coalescent-based ASTRAL species phylogenies inferred from Run1 and Run2 sets of individual gene trees are topologically irreproducible for 9/15 phylogenomic datasets, whereas concatenation-based phylogenies inferred twice from the same supermatrix are reproducible. Our simulations further show that irreproducible phylogenies are more likely to be incorrect than reproducible phylogenies. These results suggest that a considerable fraction of single-gene ML trees may be irreproducible. Increasing reproducibility in ML inference will benefit from providing analyses’ log files, which contain typically reported parameters (e.g., program, substitution model, number of tree searches) but also typically unreported ones (e.g., random starting seed number, number of threads, processor type).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Updates to the ATLAS Data Carousel Project

The High Luminosity upgrade to the LHC (HL-LHC) is expected to deliver scientific data at the multi-exabyte scale. In order to address this unprecedented data storage challenge, the ATLAS experiment launched the Data Carousel project in 2018. Data Carousel is a tape-driven workflow whereby bulk production campaigns with input data resident on tape are executed by staging and promptly processing a sliding window to disk buffer such that only a small fraction of inputs are pinned on disk at any one time. Data Carousel is now in production for ATLAS in Run3. In this paper, we provide updates on recent Data Carousel R&D projects, including data-on-demand and tape smart writing. Data-on-demand removes from disk data that has not been accessed for a predefined period, when users request them, they will be either staged from tape or recreated by following the original production steps. Tape smart writing employs intelligent algorithms for file placement on tape in order to retrieve data back more efficiently, which is our long term strategy to achieve optimal tape usage in Data Carousel.

42 ENGINEERING↗
DOE OSTI2023

Verification Testing of OLI Systems Mixed Solvent Electrolyte Model for the Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 ) System to High Ionic Strength at 25°C

This technical report summarizes model verification results and summary statistics for 41 evaporite mineral solubility cases evaluated by Savannah River National Laboratory using OLI Systems’ aqueous electrolyte thermodynamic modeling software. The 41 verification cases containing a total of 60 solubility curves comprise mineral solubility data from low to high ionic strength at 25°C for the eight-component system Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 O as reported by Harvie et al. (1984). Thermodynamic calculations were executed using OLI Systems’ Stream Analyzer computation module within the OLI Studio software platform (Ver. 11.0, Rev. 11.0.1.9). The Mixed Solvent Electrolyte (MSE) thermodynamic framework was chosen for this investigation because of its superiority in modeling high ionic-strength inorganic salt solutions and actinide redox chemistry and solubility, both of which are relevant to the geological repository conditions at the Waste Isolation Pilot Plant in Carlsbad, New Mexico. Mineral solubility data in various inorganic salt solutions were digitized and extracted from figures generated by Harvie et al. (1984). For each of the 60 solubility curves, a case-specific chemistry model and input file were generated in OLI Studio using OLI Stream Analyzer and the MSE (H 3 O + ion) public databank provided by OLI Systems. Model simulation results were exported to Microsoft Excel to calculate summary statistics and to generate graphs comparing the OLI model predictions to the solubility data. Summary statistics include residuals (model – data) and concordance (accuracy × precision, where precision is indicated by the Pearson correlation coefficient and accuracy accounts for bias and scale differential). Private databanks were not developed, and activity coefficient model regressions were not performed to improve OLI model fits to the data. Of the 41 model verification plots, 83% have a mean of the percent residuals less than or equal to 25%. Similarly, 75% display a concordance greater than or equal to 0.75. Only seven of the 41 verification plots fail to show good agreement between the model and data. Of these seven, three are relevant to the WIPP repository because they involve the Mg-OH-Cl-SO 4 -CO 3 aqueous system. The remaining four address salt solubilities at the pH extremes (strong acid and strong base). It should be noted that in two of the three Mg-OH-Cl-SO 4 -CO 3 system cases, the regressed Harvie et al. (1984) solubility curve also deviated from the data. Lack of agreement between the OLI model-predicted solubility curves and the data is attributable to one or more of the following: specific solid species are not included in the OLI MSE databank; there is significant variation among the different solubility datasets chosen by Harvie et al. (1984); the OLI MSE model’s thermodynamic parameters were determined using different solubility datasets; and the activity coefficient parameters for certain relevant ion-ion and ion-molecule pairs have not been optimized via data regression. Two recommendations for future work are to (1) evaluate solubility data for the Mg-OH-Cl-SO 4 -CO 3 system at high ionic strength and, if necessary, develop a private OLI MSE database that includes missing species and, where necessary, regressed standard state properties and interaction parameters; (2) perform similar verification testing of the OLI model for actinide solubility data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

The Global DAS Month of February 2023

During February 2023, a total of 32 individual distributed acoustic sensing (DAS) systems acted jointly as a global seismic monitoring network. The aim of this Global DAS Month campaign was to coordinate a diverse network of organizations, instruments, and file formats to gain knowledge and move toward the next generation of earthquake monitoring networks. During this campaign, 156 earthquakes of magnitude 5 or larger were reported by the U.S. Geological Survey and contributors shared data for 60 min after each event’s origin time. Participating systems represent a variety of manufacturers, a range of recording parameters, and varying cable emplacement settings (e.g., shallow burial, borehole, subaqueous, and dark fiber). Monitored cable lengths vary between 152 and 120,129 m, with channel spacing between 1 and 49 m. The data has a total size of 6.8 TB, and are available for free download. Finally, organizing and executing the Global DAS Month has produced a unique dataset for further exploration and highlighted areas of further development for the seismological community to address.

58 GEOSCIENCES↗
DOE OSTI2026

Memory-Aware External Facelist Calculation: A Data-Parallel Atomic Hash Counting Approach

Unstructured volumetric meshes serve as fundamental data representations in various scientific simulations and analyses. They play a crucial role in representing complex computational domains and are essential for important numerical techniques, such as finite element analysis. Whenever such a mesh is read from a file, streamed in-situ, or generated by algorithms, scientific visualization libraries rely on calculating the external surface of a geometry, named “external facelist”, to produce a polygonal mesh for rendering. Consequently, external facelist calculation has become one of the most widely used algorithms in the scientific visualization domain, necessitating optimal performance. In this paper, we explore relevant work on external facelist calculation algorithms in two common visualization libraries, VTK and Viskores, assess their performance and memory constraints, and introduce a novel memory-aware external facelist calculation algorithm employing an atomic hash counting approach. This algorithm fully leverages Viskores' data-parallel primitive operations, facilitating its execution across diverse many-core architectures. Our algorithm features the lowest memory footprint on the GPU and the second-lowest on the CPU among all evaluated methods, and it also delivers the fastest performance on both CPU and GPU. It has been made available under an open-source license in the VTK and Viskores visualization systems.

Tsalikis, Spiros [Kitware] (ORCID:0000000151137195↗
DOE OSTI2026

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

funcX: Federated Function as a Service for Science

Here, funcX is a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. Unlike centralized FaaS systems, funcX decouples the cloud-hosted management functionality from the edge-hosted execution functionality. funcX's endpoint software can be deployed, by users or administrators, on arbitrary laptops, clouds, clusters, and supercomputers, in effect turning them into function serving systems. funcX's cloud-hosted service provides a single location for registering, sharing, and managing both functions and endpoints. It allows for transparent, secure, and reliable function execution across the federated ecosystem of endpoints-enabling users to route functions to endpoints based on specific needs. funcX uses containers (e.g., Docker, Singularity, and Shifter) to provide common execution environments across endpoints. funcX implements various container management strategies to execute functions with high performance and efficiency on diverse funcX endpoints. funcX also integrates with an in-memory data store and Globus for managing data that may span endpoints. We motivate the need for funcX, present our prototype design and implementation, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than 130000 concurrent workers. We show that funcX's container warming-aware routing algorithm can reduce the completion time for 3,000 functions by up to 61% compared to a randomized algorithm and the in-memory data store can speed up data transfers by up to 3x compared to a shared file system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

ESGF Future Architecture Report (V.1.1)

This document reports the findings of the Earth System Grid Federation Future Architecture Face-to-Face Meeting held in the UK 5-7 November 2019, attended by technical representatives from the ESGF partner organisations. ESGF is an International federated archive that provides access to Earth science data. It is fundamentally a distributed file management system with its main function being data dissemination through the provision of services for efficient replication and search. It has been in operation for over ten years and has undergone continuous improvements and enhancements over this time span. The Future Architecture is a major review of the software system including a fundamental reassessment of its requirements with a view to develop a new overall system architecture. This has been initiated in the light of challenges around the maintenance of the existing software legacy and a changing technology landscape and practices in the communities it serves. The work is being carried out under the oversight of the ESGF Executive Committee1,2 and is coordinated activity between ESGF and the European IS-ENES3 project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Precursor Analysis Report: Conti Ransomware Attack on the Health Service Executive of Ireland 2021

The Conti Ransomware Attack on the Health Service Executive (HSE) of Ireland 2021 Precursor Analysis Report leverages publicly available information about the attack and catalogs anomalous observables for each technique employed by the adversary. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. The HSE provides public healthcare corporate services and operational services throughout Ireland, with critical functions including the acute national ambulance service, acute hospital service, and community healthcare service. On 14 May 2021, Conti ransomware encrypted 80 percent of the HSE’s Information Technology (IT) infrastructure across corporate, hospital, community, and electronic health record services. Conti is a ransomware-as-a-service operation that encrypts local files, uses double extortion against victims, and is facilitated by many intrusion tools. The attack forced the HSE to shut down its entire IT infrastructure to contain the ransomware, forcing employees to revert to pen and paper recordkeeping and leading to the cancellation of many appointments and procedures. The adversary also exfiltrated 700 GB of data, compromising the confidentiality of patients’ protected health information. Had the adversary targeted the COVID-19 cloud systems or operational technology assets, such as Internet of Medical Things medical devices or smart building management systems, the impact of the attack would almost certainly have been far more severe. Researchers and analysts identified 21 unique techniques (used in a sequence of 23 steps) likely utilized during the attack with a total of 1,185 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Twenty-one of the identified techniques used during the attack on the HSE were precursors to the triggering event. Analysis identified 1,086 observables associated with these precursor techniques, 850 of which were assessed to have an increased likelihood of being perceived in the 57 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

SMC 2021 Data Challenge: Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU: RUR dataset is the job scheduler traces collected from the Titan supercomputer from 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected using resource Utilization Report (RUR), a Cray-developed resource-usage data collection and reporting system. It contains the usage information of its critical resources (CPU, Memory, GPU, and I/O) of each running job on Titan during that period (https://ieeexplore.ieee.org/abstract/document/8891001). It includes ProjectAreas as additional information, every job is associated with a project ID. TheProjectAreas.csv dataset provides a mapping of the project ID to its domain science. GPU dataset has information regarding GPU failure on Titan. There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has seven attributes, we provided a short description of these attributes in the ReadMe file. To learn more about this dataset, please refer to the git repository https://github.com/olcf/TitanGPULife and the related publication (https://ieeexplore.ieee.org/abstract/document/9355319).

42 ENGINEERING↗
DOE OSTI2022

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. Finally, we expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Verification of RESRAD-OFFSITE Code (V.4)

This report documents the verification of RESRAD-OFFSITE Version 4.0 and describes, where necessary, the verification of the following: • The data comprising the standard dose and risk coefficient libraries in the RESRAD database files Master_dcf_ICRP07.mdb and Master_dcf_2k.mdb. • The extraction and transfer of the data from the selected database file to the computational code by the RESRAD-OFFSITE 4.0 interface, ResOWin.exe. • The different processes that are modeled by the main computational code in RESRAD OFFSITE 4.0, ResOMain.exe. • The data displayed in the graphical and text reports. Many verifications were performed as part of the quality assurance quality control program associated with the development and release of RESRAD-OFFSITE 4.0, namely: • developer testing, • internal independent testing, and • release testing. Some were also performed in response to questions from users regarding the performance of the code. The main text of the report focuses on summarizing a subset of those tests, both independent and developer tests that verified the computations performed by the code. The verifications included in this report served as the basis for the development of the release tests of the computational executables and provided the quantitative results to be compared with the code output. The input and output interfaces and the data transfers between the various executables of the code were tested while performing the verification testing. They were tested intentionally during release testing. This report also provides some basic information to help in understanding the activities that were verified. The report: • outlines the components of RESRAD-OFFSITE 4.0 and the interconnections between these components, • outlines the processes modeled by the computational code, • provides summary figures and tables to offer confirmation of the verification of the computational components of the code, • reproduces the verifiers’ reports, if available, in individual appendices, • refers to the previous verification report (Yu et al. 2011) for more details about some of the verifications, and • reproduces the test cases and the testers’ reports from the release testing in individual appendices, when possible.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2

This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexibly extended for in situ analysis. The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware heterogeneity lies in the decoupling of data description in domain sciences, such as plasma physics simulations, from concrete implementations in hardware and IO. The streaming backend is provided by the ADIOS2 framework, developed at Oak Ridge National Laboratory. This paper surveys two openPMD-based loosely-coupled setups to demonstrate flexible applicability and to evaluate performance. In loose coupling, as opposed to tight coupling, two (or more) applications are executed separately, e.g. in individual MPI contexts, yet cooperate by exchanging data. This way, a streaming-based workflow allows for standalone codes instead of tightly-coupled plugins, using a unified streaming-aware API and leveraging high-speed communication infrastructure available in modern compute clusters for massive data exchange. We determine new challenges in resource allocation and in the need of strategies for a flexible data distribution, demonstrating their influence on efficiency and scaling on the Summit compute system. The presented setups show the potential for a more flexible use of compute resources brought by streaming IO as well as the ability to increase throughput by avoiding filesystem bottlenecks.

Poeschel, Franz↗
DOE OSTI2018

Planning of distributed data production for High Energy and Nuclear Physics

Modern experiments in High Energy and Nuclear Physics heavily rely on distributed computations using multiple computational facilities across the world. One of the essential types of the computations is a distributed data production where petabytes of raw files from a single source has to be processed once (per production campaign) using thousands of CPUs at distant locations and the output has to be transferred back to that source. The data distribution over a large system does not necessary match the distribution of storage, network and CPU capacity. Therefore, bottlenecks may appear and lead to increased latency and degraded performance. In this paper we propose a new scheduling approach for distributed data production which is based on the network flow maximization model. In our approach a central planner defines how much input and output data should be transferred over each network link in order to maximize the computational throughput. Such plans are created periodically for a fixed planning time interval using up-to-date information on network, storage and CPU resources. The centrally created plans are executed in a distributed manner by dedicated services running at participating sites. In conclusion, our simulations based on the log records from the data production framework of the experiment STAR (Solenoid Tracker at RHIC) have shown that the proposed model systematically provides a better performance compared to the simulated traditional techniques.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A Historic Context and Mitigation Documentation for a Portion of the U12n Tunnel Ventilation and Containment Systems, Area 12, Nevada National Security Site, Nye County, Nevada

The National Nuclear Security Administration Nevada Field Office (NNSA/NFO) has proposed repurposing the U12n Vent Hole #2 (SHPO Resource S2488) and the U12n.10 Vent Hole (S2489) for water sampling in the U12n Tunnel. The vent holes are part of the historic ventilation system for the tunnel and are contributing elements to the U12n Tunnel Historic District (D84). In consultation with the SHPO, NNSA/NFO determined the undertaking will have an adverse effect on the vent holes. The NNSA/NFO and the SHPO negotiated a memorandum of (MOA) with stipulations to mitigate adverse effects. This submission is to comply with the stipulations in the executed Memorandum of Agreement Between the National Nuclear Security Administration Nevada Field Office, Environmental Management Nevada Program Office, and the Nevada State Historic Preservation Officer Regarding Repurposing of Portions of the Ventilation System of the U12n Tunnel Complex Located in Area 12 at the Nevada National Security Site. In accordance with the MOA, the manuscript provides a historic context that describes the development and functioning of the historic U12n Tunnel ventilation system for underground nuclear tests (Stipulation III.C). High resolution digital color images of the vent hole containment doors, nearby elements, and overviews were obtained and keyed to a map and a photo index (Stipulation III.A) (Appendix A). Lastly, Architectural Resource Assessment (ARA) forms were completed for each of the vent holes and Historic District D84 (Stipulation III.B) (Appendix B). The documentation including the historic context report, photographs, image files, and resource forms produced to fulfill the terms of the MOA will be archived with the Nuclear Testing Archive in Las Vegas (Stipulation III.D).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Queued Up: 2025 Edition – Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2024 [Slides]

Electric transmission system operators (ISOs, RTOs, or utilities) require proposed power plants seeking to connect to the transmission grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. The lists of projects in this process are known as “interconnection queues”. In collaboration with interconnection.fyi, Berkeley Lab compiled, aggregated, and cleaned interconnection queue data from >50 transmission grid operators (7 ISO/RTOs and 49 non-ISO balancing areas), which collectively represent ~97% of currently installed U.S. electric generating capacity. The dataset includes requests submitted to queues through the end of 2024, and only includes requests seeking to connect to the transmission grid (not distribution-connected or behind-the-meter projects). The files below include both a PDF report and an Excel data file. The PDF report analyzes interconnection data and metrics through the end of 2024. The Excel data file includes (a) the full project-level interconnection queue dataset through 2024, (b) a codebook (data dictionary) describing each data field, and (c) 35 additional tabs featuring tables summarizing a range of interconnection metrics. Key highlights from the Queued Up: 2025 Edition (featuring data through 2024) include: • As of the end of 2024, there were ~10,300 projects actively seeking grid interconnection in the U.S., representing 1,400 GW of generation and approximately 890 GW of storage. • Historic withdrawal rates alongside relatively fewer new requests resulted in a 12% decrease in total active queue volume compared to the prior year. • Active natural gas capacity (136 GW, +72% year-over-year) increased in 2024, while solar (956 GW, -12%), storage (890 GW, -13%), and wind (271 GW, -26%) capacity decreased. • 408 GW of capacity already has a draft or executed interconnection agreement (IA) but has not yet reached commercial operations. • The time projects spend in queues before reaching COD is increasing. For the regions with available data, the median duration from IR to COD has doubled from <2 years for projects built in 2000-2007 to over 4 years for those built in 2018-2024. • Ultimately, most of this proposed capacity will not be built. Only 13% of capacity that submitted interconnection requests from 2000-2019 had reached commercial operations by the end of 2024; 77% of that capacity had been withdrawn and 10% was still active. • FERC Order 2023 and various other reforms are being implemented. These are important measures to reduce interconnection bottlenecks and enhance grid system reliability, but it is too early to measure and assess their full impact. • New additions for the 2025 edition include: (a) additional detail on data processing and gaps; (b) updates on interconnection reforms; (c) new analysis on interconnection agreements, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗