Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Generators”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Electrochemically Regenerated Solvent for Direct Air Capture with Co-generation of Hydrogen at Bench-scale

The goal of this final project report is to summarize the work conducted on project DE-FE0032125. In accordance with the Statement of Project Objectives (SOPO), the University of Kentucky Institute for Decarbonization and Energy Advancement (UK IDEA) (Recipient) developed an intensified process to capture CO 2 from ambient conditions (415 ppm CO 2 ). The process combines low-temperature solvent-aided membrane capture with electrochemically-mediated solvent regeneration to simultaneously capture ambient CO 2 while regenerating the solvent. The technology employs only two primary units, a regenerator and an absorber/contactor, while generating high purity hydrogen as a co-product that can be sold, used for energy storage, or cost-saving depolarization of the direct air capture (DAC) system during the grid peak demand, allowing for flexible operation. Since the technology is powered directly by DC electricity, it can seamlessly tie in with power sources like solar cells without the need for AC/DC converters, therefore allowing for a remote operation to further mitigate greenhouse gas generation toward deploying a negative carbon emissions technology that is completely decoupled from the carbon emissions from the power source for the DAC unit. The completion of the project results in significant progress toward the Department of Energy’s (DOE’s) goal of advancing lab and bench-scale DAC systems to a sufficient maturity level that can justify their continued scale-up through the verification testing of the electrochemically regenerated solvent system for DAC with co-generation of hydrogen at bench-scale. The technology addressed the complexities of incumbent DAC systems by demonstrating at ambient conditions (1) low gas-side pressure-drop facile CO 2 capture via an intensified membrane absorber with in-situ regenerated hydroxide as capture solvent, (2) multi-functional electrochemical regenerator for hydroxide regeneration, CO 2 concentration and hydrogen production at less than 3 V, and (3) stable DAC performance including >90% capture with air influent at the CFM scale. The data from this project enables the completion of Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA). TEA and LCA demonstrate the potential of the proposed electrochemical solvent-based process to be a viable DAC option. The analysis did not identify any obvious concern for the bench-scale operation and no apparent barriers to implementing UK IDEA carbon capture and solvent regeneration system at a larger scale.

08 HYDROGEN↗
DOE OSTI2021

IDAES Enterprise: Generation Expansion Planning with Enhanced Requirements for Capacity Adequacy Under Renewable Intermittency

Achieving net zero carbon emissions likely requires future power systems to integrate new, flexible energy technologies to accommodate higher levels of capacity from variable renewable energy sources. To determine the optimal deployment of new electricity capacity and to study the likelihood of deployments of new energy technologies, an expansion planning model has been developed as part of the IDAES-Enterprise suite of grid models. The Generation Expansion Planning (GEP) model is a multi-period model in which investment decisions occur yearly, and a Unit Commitment (UC) problem is examined on an hourly timescale. To reduce computational complexity of the GEP model, the UC problem is solved for average “representative days” which leaves out extreme, but relatively common, scenarios in which low renewable generation occurs, leaving the system with inadequacy in capacity. The IDAES-Enterprise GEP model has been modified to include these extreme scenarios while keeping the model reasonably tractable. Specifically, a lazy constraint technique was implemented to check for capacity adequacy on an hourly basis over a large data set of aligned load-wind-solar profiles. As a vast majority of the capacity constraints will not be violated, the technique lowers computational expense by searching for violated capacity constraints over an “iterative manner,” adding those infeasible constraints back into the model. Results on a test case of the Southwest Power Pool shows that the lazy constraint technique significantly reduces retirements and increases installments of natural gas combined cycles and flexible natural gas units. It also reduces some retirements of coal units. These modifications provide a more reasonable estimation of required dispatchable power generation capacity to ensure feasibility during peak net load.

Liu, Peng↗
DOE OSTI2024

Performance Testing of a Moving-Bed Gasifier Using Coal, Biomass, and Waste Plastic Blends with Washed and Unwashed Legacy Coals and Other Waste Fuels to Generate White Hydrogen

The objective of this effort, primarily funded by the United States Department of Energy (DOE), and led by the Electric Power Research Institute, Inc. (EPRI), with support by Hamilton Maurer International (HMI) and Sotacarbo S.p.A. (Sotacarbo), has been to qualify coal, biomass, and plastic waste blends based on performance testing of selected fuel pellet compositions in a pilot-scale updraft moving-bed (UDMB) gasifier. The testing provided relevant data to advance the commercial-scale design of the moving-bed gasifier to be able to successfully use these feedstocks to produce hydrogen. In particular, the effects of waste plastics on feedstock development (i.e., blending and pelletizing) and the resulting products (i.e., syngas compositions, organic condensate production, and ash characteristics) are the focus. The gasifier used for testing is HMI’s moving-bed gasifier, which has been proven capable of gasifying nearly all coal ranks. It has also shown the ability in prior testing work to gasify wood chips (biomass). However, mixtures of these fuels with plastic wastes have not been prepared and gasified together. The three feedstocks were densified and pelletized by California Pellet Mill (CPM) to meet the feedstock size required by Sotacarbo’s 30mm ID UDMB gasifier, under contract to HMI. The technical tasks and results from this two-year research project included: (1) Feed Procurement and Preparation: Nine different tri-fuel pellets were prepared from varying compositions of fresh mined PRB coal, corn stover biomass, and car fluff waste plastics. Tri-fuel pellets were produced by CPM and shipped to Sotacarbo’s test facility in Carbonia, Sardinia, Italy. (2) Test Plan Development: A test plan was created to define the test runs to be performed. The test plan detailed the different UDMB gasification tests to be performed in Sotacarbo’s 12-inch ID pilot scale gasifier, the process monitoring instrumentation used, and the extractive samples recovered for analysis of the total gasification process mass and energy balance. (3) Gasifier Testing: Nine different gasification runs were performed in the pilot-scale gasifier at Sotacarbo using nine different fuel feedstock compositions generated from varying mixtures of PRB coal, biomass, and plastic wastes. The testing generated performance data on gasification reaction efficiency and performance, yielding relevant data for models used to scale up the gasifier design. This task also included work to refurbish and reassemble the pilot gasifier at Sotacarbo and perform a baseline 100% PRB coal run. (4) Data Analysis and Reporting: Review of the data, determination of figures of merit, and interpretation of the results are reported in the project’s final report, published in March 2024. The results show that all tri-fuel pellets gasified well and maintained structural integrity throughout the gasification process. The syngas generated can be shifted to hydrogen by using commercial syngas shifting technologies. (5) High Fidelity computational fluid dynamics (CFD) Simulation: The National Energy Technology Laboratory (NETL) team performed CFD simulations of the UDMB gasifier for two of the tri-fuel pellets gasified in Sotacarbo’s pilot scale gasifier. The kinetic mechanisms for the pyrolysis of each constituent, PRB coal, corn stover biomass, and waste plastics are based on thermogravimetric analysis performed by Sotacarbo. The gasification model was validated by comparing the predicted syngas composition at the exit of the gasifier with the measured syngas composition. In addition, the reactor’s measured internal temperature profile agreed well with the predicted internal reactor temperature profile. These results validate that the model can be used to predict the performance of the updraft moving bed gasifier for different feedstocks and operating conditions. This paper summarizes the results of the completed work in which the pelletizing procedure was validated to ensure the viability of the tri-fuel pellets for the gasification runs performed at Sotacarbo’s 30 mm UDMB gasifier. The gasification performance data from this series of nine runs will enable modeling of a full-scale HMI industrial scale gasifier supporting both combined heat and power, and Hydrogen production from coal (both fresh mined and legacy) combined with various biomass and waste plastics. Additionally, plans and progress on a follow-up project, being executed by the same project team, will be presented. In this project, a total of twenty (20) different feedstocks are being prepared from varying compositions of biomass (both woody biomass and corn stover) with a mixture of legacy coal waste, plastic waste, and refuse-derived fuel (RDF). The testing will provide information on gasification reaction efficiency/performance, yielding relevant data for models used to scale up the gasifier design to 50 megawatt electric (MWe) (equivalent hydrogen production). Tests will also be performed on a bench-scale fluidized-bed gasifier for comparison purposes. The results of this testing will be used to specify the range of feedstock blends that can be successfully gasified as well as quantify gasifier outputs based on specific blends.

08 HYDROGEN↗
DOE OSTI2024

Retrieval Augmented Generation for Robust Cyber Defense

In cybersecurity, the ability to efficiently analyze and respond to vulnerabilities, weaknesses, attack patterns, and threat tactics is critical for effective defense strategies. With the increasing complexity and volume of cybersecurity data, traditional methods of querying and retrieving information are often inadequate. To address this challenge, we implemented Retrieval-Augmented Generation (RAG) systems—CyRAG and GraphCyRAG—that integrate large language models (LLMs) with both structured data from relational databases and knowledge graphs such as Neo4j. CyRAG is designed to handle structured data, focusing on CVE (Common Vulnerabilities and Exposures) and CWE (Common Weakness Enumeration) entities to generate accurate and context-rich responses. In contrast, GraphCyRAG leverages Neo4j knowledge graphs to retrieve interconnected information from CVE, CWE, CAPEC (Common Attack Pattern Enumeration and Classification), and ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) datasets. By utilizing Neo4j’s graph-based framework, GraphCyRAG enables deeper traversal of relationships between vulnerabilities and attack patterns, providing cybersecurity analysts with more comprehensive insights into potential attack vectors and mitigation strategies. Our preliminary results demonstrate that integrating knowledge graphs with RAG significantly enhances both the accuracy and depth of threat analysis, allowing for the retrieval of dynamic, real-time data and the generation of contextually aware responses. This approach helps analysts uncover hidden relationships between cyber entities, predict exploit paths, and prioritize mitigation efforts effectively. The integration of RAG with cybersecurity knowledge graphs represents a significant advancement in cybersecurity threat intelligence, enabling more informed decision-making and stronger defense strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Optimization Model and Algorithm for Capacity Planning and Operation of Reliable and Carbon-neutral Power Systems with High Penetration of Renewable Generation

In this work, we propose a Generalized Disjunctive Programming (GDP) model that optimizes both long-term capacity planning (such as the number and size of dispatchable/renewable generators, batteries, and transmission lines) and hourly operation (such as on/off schedules of dispatchable generators, power output from each generator, and power flow) to maximize power system reliability while minimizing total cost and CO2 emissions.

Cho, Seolhee↗
DOE OSTI2024

Towards a standard approach to the analysis of carbon products generated from Natural Gas pyrolysis

Conference presentation for the work entitled “A standard approach to the analysis of carbon products generated from Natural Gas pyrolysis”. Pyrolysis of natural gas (NGP) has been proposed as a near term solution for the production of direct CO2 free (turquoise) hydrogen, to help fuel a more sustainable energy future. NGP can serve as a bridging technology while other (green) hydrogen production technologies mature. In order for NGP to be successful, the management of the generated carbon products needs to be carefully considered. Necessary markets need to be considered to incorporate the (turquoise) carbon products that are generated. A large hurdle in addressing the channeling of carbon products into desired markets is determining exactly what is manufactured from the NGP process. In this work an analysis method is presented that combines a suite of techniques for the assessment of the carbon product from Natural Gas Pyrolysis for appropriate identification and quality control.

Riley, Jarrett↗
DOE OSTI2024

Mitigating Stranded Asset Risks to Utility Customers: an Exploration of Securitization and Retiring Coal Generation

Coal generation currently accounts for approximately 19.5 percent of electricity generated in the United States, down from 51.7 percent of the generation mix in 2000. In 2023, the EIA expects 8.9 gigawatts (GW) of planned retirements of coal-fired capacity. Many of the coal-fired power plant retirements that have already occurred or are planned for the next decade will be plants that have not yet reached the end of their useful life, and are therefore not fully depreciated. Ensuring that utility customers do not face an undue burden in paying for these stranded assets will become an increasingly important issue over the next few years for PUCs overseeing the safety, reliability, and affordability of investor-owned utility service. This report reviews the role that securitization can play in reducing the costs associated with stranded assets due to early coal plant retirements.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Accelerating Neutrino Event Generation in MARLEY Using CUDA-Based RNG and GPU Parallelization

MARLEY is a simulation tool that helps scientists study how low-energy neutrinos interact with matter. To work properly, MARLEY uses random numbers thousands of times in each simulation. These random numbers are important for modeling things like how neutrinos collide with atoms and what particles they produce. Right now, MARLEY runs on a regular computer processor (CPU) and uses a built-in random number generator called the Mersenne Twister. This setup works, but it can be slow, especially when trying to simulate many events. This research focuses on making MARLEY run faster by moving the random number generation and some of the repetitive calculations from the CPU to a graphics processing unit (GPU), which can handle many tasks at the same time. We use CUDA (a tool for programming NVIDIA GPUs) and cuRAND (a GPU-based random number library) to test faster alternatives to the current random number system. We compare different GPU-based generators, like curand_mtgp32, xorwow, and philox, to see which ones are the quickest and still give reliable results. Early tests show that using the GPU can make MARLEY simulations much faster. This project not only helps improve current simulation performance but also moves closer to a full simulation chain where all stages can run on modern GPU hardware.

Dunkley, Kimieka [Florida A-M]↗
DOE OSTI2021

Salt Vessel-Sample Generator Interface

The goal of the salt sampling program at Argonne is to develop and deploy automated molten salt sampling approaches for interfacing relevant unit operations with salt analysis to improve the timeliness and accuracy of sampling-based accountancy measurements. One technology under development is a vacuum sampler loop module. In this system, molten salt is drawn from a process vessel through a heated sampling line and into a charge vessel. Next, one or more samples are split from the volume in the charge vessel and the remaining salt is returned to the process. The vacuum sampler loop module is being developed to interface with other sample processing and analysis modules to enable rapid at-line sample characterization. The main purpose of the vacuum sampling loop module is to enable the collection of samples that are more representative of the bulk salt by replacing traditional point samplers (i.e., dip probes) with a sampling approach that captures a larger cross section of salt. Additionally, the vacuum sampling approach eliminates the risk of dross contamination of samples and avoids the use of moving parts in the salt. Two methods of interfacing the vacuum sampling loop with a precision pneumatic sample generator were investigated in FY21. This report covers the testing of fluidic coupling between the two modules. Two iterations of the fluidically coupled modules were tested. The first iteration system coupled the two modules using a freeze valve to seal the vacuum sampler during filling and to control flow into the pneumatic sample generator. While this integrated system functioned as intended, some changes were implemented to make the system more robust and better suited to remote deployment. Specifically, the system was made to be more modular and active control of the vacuum filling operation was replaced with a passive control mechanism. For passive filling, the salt charge vessel was vented to a small gas tank that was at negative pressure, causing salt to be drawn into the vessel until the force of the fluid head was in equilibrium with the gas pressure. The passive control system performed well and will be used in future iterations. Another change in the second system was a newly configured pneumatic sample generator in which sample ejection occurred through a hole in the reservoir’s stainless-steel side wall instead of through a non-wetted sapphire orifice on the bottom. This alternate configuration may be better suited for near-process deployment because it enables on-line orifice maintenance and an orifice bypass drain back to the process. A third change in the second iteration system was the transition to a two-chamber charge vessel which split off a fraction of the sampled salt as a liquid aliquot. The goal was to create a buffer mechanism that would allow reproducible aliquoting of samples, independently of variability in the charge vessel fill height. While the two-chamber design was functional, the sample size reproducibility was below target values. To improve reproducibility and overcome many of the impediments to remote deployment of the vacuum sampling loop module, separate work was conducted to replace the two-chamber liquid aliquoting mechanism with aliquoting into single-use sample tubes. Solid salt transfer in the sample tubes will replace fluidic coupling for integrating the vacuum sampler with downstream modules. Because the proposed operations can all be executed with simple overhead actuation mechanisms or other existing hot-cell technology, there will be no need for large investments in novel hot cell sample handling technologies using this alternate approach. As such, near-term deployment of the vacuum sampling technology will be achievable. This new approach for automated coupling of sample tubes with down-stream modules is covered in a separate FY21 report, and a remotely operated version of the vacuum sampler loop module with the tube aliquoting feature is planned for FY22.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Evaluation of the Self Retrieval Augmented Generation Technique on Common Security Advisory Framework Data

This small experimental report evaluates a variation of Retrieval Augmented Generation (RAG), called Self-RAG. This method uses a generative language model that incorporates retrieved facts into its generation and is explicitly trained to be able to determine whether retrieved information is enough to answer the input query, with a user-defined threshold for confidence. We performed an experiment using data from the publicly available CISA Common Security Advisory Framework (CSAF) repository (https://github.com/cisagov/CSAF) as the database of facts to be used in retrieval. Qualitative results from the experiment demonstrate that the Self-RAG method has some ability to provide reasonable answers to queries that are in the dataset and will often ignore irrelevant information when asked outside of domain questions (e.g., general facts). In settings with deliberately confusing questions (the question is within domain, but asks about a fabricated advisory), it was able to refuse 40% of the time without further adjustments to the original framework. While this performance is not sufficient for current practical use, further improvements to data formatting, disambiguating results, and leveraging threshold values could improve performance significantly. However, evaluating this will require more extensive evaluations on larger datasets and potentially better models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Development of Engine Valve Materials for Next Generation Higher Efficiency Engines

The growing demand to increase the performance and efficiency of light-, medium-, and heavy-duty engines continues to drive increases in combustion intensities and cylinder pressures, which result in higher exhaust gas temperatures. Thus, there is a critical need for new materials that can meet the performance and cost targets for components such as exhaust valves which are exposed to these higher exhaust gas temperatures. Oak Ridge National Laboratory (ORNL) has developed several lower-cost, high-strength alloys that have the potential to be adopted into intake and/or exhaust valves in the next generation, high-efficiency engines and other high temperature applications. These alloys are covered by two issued patents: 1. G. Muralidharan, U. S. Patent No. 9,605,565 B2, “Low-cost Fe--Ni--Cr alloys for high temperature valve applications,” March 28, 2017. 2. G. Muralidharan, U. S. Patent 9,752, 468 B2, “Low-Cost, High-Strength Fe-Ni-Cr Alloys for High Temperature Exhaust Valve Applications, Issued Sep. 5, 2017. The overall scope of this CRADA project was for Oak Ridge National Laboratory to collaborate with Tenneco Powertrain to: 1) better define the properties required for intake and/or exhaust valves for next generation vehicles, 2) fabricate industrial scale heats of alloys down-selected from existing patents, 3) generate critical high temperature property data that will help evaluate the suitability of these new alloys for high temperature intake and/or exhaust valves, and finally to 4) fabricate and evaluate the performance of prototype intake/and or exhaust valves.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Second generation non-aqueous solvents (gen2nas) for co 2 capture from natural gas combined cycle plants

This final technical report submitted to DOE/NETL presents all the research activities performed during the Cooperative Agreement DE-FE0032218 entitled Second Generation Non-Aqueous Solvents (GEN2NAS) for CO 2 Capture from Natural Gas Combined Cycle Plants, which spanned from April 2023 through March 2025. In this project, Research Triangle Institute (RTI) International has developed the second-generation of its non-aqueous solvent (NAS), herein referred to as GEN2NAS, to remove carbon dioxide (CO 2 ) from natural-gas combined cycle (NGCC) flue gas. The technology aims to substantially reduce the cost of CO 2 capture while minimizing the environmental impacts through lower secondary emissions and wastewater generated from the CO 2 capture plant.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

HFIR Activity Workbook Generator (HAWK) User Guide

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

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Automatic Generation of Algorithms for High-Speed Reliable Lossy Data Compression (Final Report)

Fast reliable data compression is urgently needed for many leading-edge scientific instruments and for exascale high-performance computing applications because they produce vast amounts of data at extremely high rates. The goal of this project has been to develop a framework named LC that is able to automatically generate high-speed lossless and reliable lossy compression and decompression algorithms that can be customized for different kinds of data. The resulting LC framework is freely available on GitHub. To achieve high-speed operation, LC outputs optimized and parallelized CPU and GPU implementations of the generated algorithms. To ensure the quality of lossily compressed data, LC guarantees the user-provided error bound. To be able to customize the compression algorithm to various use cases, LC can synthesize millions of different algorithms and automatically search for the one that works best for the given data. We have already employed LC to create state-of-the-art lossless and lossy compressors for scientific data as well as leading lossless compressors for images. We hope that LC and the customized, fast, reliable, and CPU/GPU-compatible compression algorithms that it can generate will greatly benefit the many scientific applications that need not only high trustworthiness but also high performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

LLM Generation of Online Courses from a Curated Set of Documents in the Nuclear Safeguards Domain

A multidisciplinary team at Argonne National Laboratory explores the application of advanced technologies to enhance knowledge transfer and retention within the nuclear safeguards domain. Specifically, it examines the feasibility of leveraging secure large language models (LLMs) to streamline the creation of e-learning modules for the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Safeguards (NA-241). The initiative addresses the critical need for preserving institutional memory and accelerating skill development amidst the imminent retirement of senior professionals in the field in addition to supporting good knowledge management practices. The project integrates instructional design theory with cutting-edge AI technologies to transform curated document sets from the Safeguards Knowledge Repository (SKR) into modular online courses. By automating the generation of learning objectives and instructional content, the effort aims to reduce manual effort while maintaining high-quality educational outcomes. A limited measure of human supervision, however, ensures accuracy, relevance, and alignment with NNSA’s strategic priorities. Key findings highlight the potential of AI-assisted course generation to support safeguards professionals by creating structured, interactive learning experiences. The report underscores the importance of SME validation to address limitations in AI-generated content, such as terminology errors and gaps in coverage. Recommendations include adopting a structured workflow combining LLM acceleration with expert oversight to ensure accuracy, usability, and alignment with learner needs. This work demonstrates Argonne’s commitment to advancing national security and scientific excellence through innovative knowledge management solutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Redesign of the Timeline Generator at Fermilab using a web-based Flutter Application, GraphQL API and an IOC

Redesign of the Timeline Generator at Fermilab using a web-based Flutter application, GraphQL API and an IOC ABSTRACT = The control system at Fermilab is undergoing an evolution with a shift towards web-based applications with connections to the EPICS infrastructure. The Timeline Generator (TLG) is an application that serves to coordinate events across the lab using different timing links. These links include the Tevatron clock (TCLK), a 10 MHz serial link with events encoded at 20Hz and Ma-chine Data (MDAT), a communication link with states encoded at 720Hz. This paper covers the redesign of the major components of the TLG. This includes a web-based Flutter application for building timelines. A placement service is in use that has a GraphQL interface and uses a timeline input to compute a schedule of events and states. The Flutter application sends this computed schedule to the TLG IOC via a GraphQL interface to the Data Pool Manager (DPM). The TLG IOC runs on an Arria FPGA, the Accelerator Clock Generator (ACLK-GEN), which is responsible for writing the events and states on to the different timing links.

Carmichael, Linden [Fermilab]↗
DOE OSTI2026

Wind Power as a Virtual Synchronous Generator (WindVSG)

This project investigated the theory, implemented it in hardware, and validated the Wind as a Virtual Isochronous Generator (WindVSG) concept by combining the advantages of modern dynamic inverter technologies with static, dynamic, and transient electromechanical properties of synchronous machines. During this project we demonstrated how to control the inverters of wind turbine generators (wind alone or in parallel with other GFM sources, such battery energy storage) so that wind power behaves like a synchronous machine-based power plant with a conventional prime mover. For this purpose, testing was conducted at NLR ARIES facility with real 2.5 MW wind-turbine generator operating in GFM mode under dynamic and transient conditions. The team also developed models and conducted simulations for GFM wind power to evaluate stability impacts of GFM operation on power grid. This report describes efforts by the NLR team working in collaboration GE Vernova during 3-year project.

17 WIND ENERGY↗
DOE OSTI2026

Synthetic Atmospheric River Ensembles Generated by Deep-AR

This dataset contains 35,850 synthetic landfalling atmospheric river (AR) realizations generated by the Deep-AR two-stage deep-learning framework over the Northeast Pacific and U.S. West Coast. The archive contains 25 stochastic ensemble members for each of 1,434 held-out observed seed events. Each synthetic realization is initialized from conditions 48 hours before the corresponding observed AR landfall and is generated autoregressively at 6-hour intervals over a 144-hour period. Deep-AR combines a deterministic residual network (ResNet) that advances the large-scale atmospheric state with a Wasserstein generative adversarial network (WGAN) that produces stochastic, high-resolution fields. Each HDF5 file contains 0.25° gridded synthetic integrated vapor transport components (qu, qv), 10 m wind components (u10, v10), and 6-hour accumulated precipitation on a common 200 × 480 grid. The files also include coordinate and datetime arrays. This dataset supports AR hazard analysis, ensemble-based uncertainty characterization, precipitation-extremes research, and regional stress testing. Synthetic files follow the naming convention deepar.model.YYYYMMDD.HHMMSS.vNN.h5. YYYYMMDD.HHMMSS identifies the UTC initial-condition timestamp, which occurs 48 hours before the diagnosed observed landfall, and vNN identifies the zero-padded ensemble member, ranging from v01 through v25. Each synthetic file can be paired with its corresponding observed file by matching the initial-condition timestamp. The paired observed file follows the naming convention deepar.obs.YYYYMMDD.HHMMSS.h5 and is available in the separately registered oracle/deepar.obs dataset at https://wdh.energy.gov/ds/oracle/deepar.obs (DOI: https://doi.org/10.21947/3377671).

17 WIND ENERGY↗