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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Critical Clearing Time for Droop-Controlled Grid-Forming Inverters With Circular Current Limiting and Virtual Impedance Current Limiting

The critical clearing time is a crucial parameter for assessing the large-signal stability of grid-forming (GFM) inverters. Variations in current limiters can significantly impact an inverter's transient behavior during large disturbances. Here, this letter introduces a method based on the power-angle relationship to evaluate the fault recovery of a droop-controlled GFM inverter under a three-phase bolted fault scenario, considering both the circular current limiter and the virtual impedance method. High-fidelity simulations validate the accuracy of the proposed formulas in estimating the critical clearing time of the GFM inverter.

42 ENGINEERING↗
DOE OSTI2022

A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study

Transient stability is one of the critical aspects of power system stability assessment. The increasing integration of inverter-based resources and the retirement of conventional synchronous generators result in the decreasing system inertia and growing complexity of system operating conditions. Using a few selected typical operating conditions cannot guarantee system transient stability in all operating conditions, and the time-domain simulation of all operating conditions requires tremendous time and is often infeasible. This paper proposes a more efficient transient stability assessment method based on deep learning. The binary search method is used to determine the critical clearing time (CCT) in creating training databased by time-domain simulation. This method is fast and accurate with 1 ms resolution. The buses whose CCTs are lower than 200 ms are considered critical buses. Buses close to each other are grouped based on their mutual admittance matrix to reduce the search space of the critical buses. This paper also proposes the generator feature normalization based on the physical model. Case study on the reduced 240-bus WECC system model demonstrates that the proposed method can predict CCT accurately and efficiently.

critical clearing time↗
DOE OSTI2023

ML-Based Power System Stability Assessment Considering Network Topology Changes: WECC 20,000+ Bus System Case Study

Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy.

critical clearing time↗
DOE OSTI2025

Large-signal Stability Analysis of Grid-forming Inverters with Equivalent-circuit Models

Here, this paper proposes an energy function-based direct method for large-signal stability assessment of grid-forming (GFM) inverters leveraging an equivalent-circuit representation of all involved control- and physical-layer dynamics. Three different primary controls, a standard inner-current outer-voltage cascaded-control architecture, output LCL filter, and reference-current saturation limiting are featured in the modeling and analysis framework. A composite energy function for the GFM inverter is obtained by summing up individual energy contributions gleaned from the circuit representation. The approach can readily be generalized to different primary controls, output-filter arrangements, and current limiters since it is based on a circuit-theoretic foundation. Numerical simulations validate the efficacy of the approach in estimating the critical clearing time following a large-signal disturbance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Feedback Control Strategy for Transient Stability Application

Power systems are subjected to a wide range of disturbances during daily operations. Severe disturbances, such as a loss of a large generator, a three-phase bolted fault on a generator bus, or a loss of a transmission line, can lead to the loss of synchronism of a generator or group of generators. The ability of a power system to maintain synchronism during the few seconds after being subjected to a severe disturbance is known as transient stability. Most of the modern methods of controlling transient stability involve special protection schemes or remedial action schemes. These special protection schemes sense predetermined system conditions and take corrective actions, such as generator tripping or generation re-dispatch, in real time to maintain transient stability. Another method is the use of a real-time feedback control system to modulate the output of an actuator in response to a signal. This paper provides a fundamental evaluation of the use of feedback control strategies to improve transient stability in a power system. An optimal feedback control strategy that modulates the real power injected and absorbed by distributed energy-storage devices is proposed. Its performance is evaluated on a four-machine power system and on a 34-machine reduced-order model of the Western North American Power System. The result shows that the feedback control strategy can increase the critical fault clearing time by 60%, thereby improving the transient stability of the power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Cognitive Aging as a Human Factor: Effects of Age on Human Performance

Nuclear power plant (NPP) control room operators must make ongoing computations and decisions that maximize production and ensure safety, which places a high cognitive burden on the operators. How cognitions such as attention, visuospatial ability, and working memory interact with socio-technical systems to achieve optimal operations is well studied. However, there is an absence of research that examines how cognitive functioning within the NPP control room environment is moderated by developmental aging processes. This is of critical importance because different types of cognitive actions are known to develop and peak at different times across the adult life span, and it is becoming increasingly clear that there is no age at which all cognitive faculties operate at maximum capacity. Thus, given that NPPs are experiencing an aging workforce, it is vital to identify how mission critical cognitions change with age. This paper reviews implications of aging on reactor operators in the current and new fleet. We highlight lessons that can be learned from state-of-the-art human factors research that considers aging, lessons from the large cognitive aging literature, and lessons from aging workers in other industries that use sophisticated socio-technical systems, such as aviation. We also consider the important subject of aging effects versus expertise and present preliminary data that support the premise that age of operator is linked to effective and efficient operations but that this relationship may be moderated by level of operations expertise. In conclusion, we apply these lessons to future considerations for aging research in current nuclear operations and with the advent of advanced modernized control rooms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

From Cryogenic to High Temperatures: A Microstructural Evolution of δ-phase Pu-Ga Alloys Investigated by Time-of-Flight High-resolution Neutron Diffraction

Phase stability and phase transformations in binary Pu alloys are critical for multiple applications. Long-term phase stability affects mechanical properties, microstructure, corrosion behavior, and structural integrity. A clear and detailed understanding of phase transformations and phase stability mechanisms is necessary to assess the behavior and response to unexpected stimuli. Gaining advanced knowledge on the behavior of Pu and Pu alloys at a variety of temperatures, pressures, and time will ensure better predictions and control on evolution outside laboratory measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Strategic styles of hardware product development could accelerate commercialization in cleantech startups

Hardware-based startups risk having longer times-to-market, deterring investment in the clean technologies that are critical to a sustainable future. We interviewed 55 leaders at hardware startups, 20 of which are cleantech, mapped their development timelines, and found prototyping to be the longest development step (median of 19 weeks per prototype) regardless of prototype complexity or iteration. Qualitative interview analysis reveals the prototyping team’s choice of development style is a major factor affecting timeline. We define two development styles: natural and structured, typified by free-form exploration and rule-based execution, respectively. On average, natural development takes 35% less time than structured, and is thus preferred for early iterations, but adopting structure at strategic points is needed for timely commercialization. Critical points of transition to a structured style include adding new team members or engaging external partners, which demand clear communication and expectations. When pivoting to a new product or market, returning to a natural style is beneficial.

Looney, Erin (ORCID:0000000168959312)↗
DOE OSTI2025

Angularly resolved spectral reconstruction of x rays via filter pack attenuation

We have designed a new filter pack array to measure angular variations in x-ray spectra during a single shot. The filter pack was composed of repeating identical columns of aluminum and copper filters of varying thicknesses. These columns were located at different positions to measure the spectrum at each corresponding angle. This array was utilized in an experiment to measure the energy evolution of betatron x rays in a laser wakefield accelerator by curving the wakefield with a transverse density gradient, streaking the x rays across the array in front of an x-ray charge-coupled device (CCD) camera. After subtracting the background and “flattening” the image to remove spatial nonuniformities, a critical energy was calculated for each position that produced the best agreement with the measured signal. There was a clear change in critical energy with angle, shedding light on the dynamics of the electrons that traveled through the accelerator. Furthermore, these angles correspond to distinct emission times, covering a timescale of tens of picoseconds. The filter pack was capable of recovering these angular details without the impact of errors introduced by shot-to-shot variability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Nuclear Criticality Experiments Research Center Futures: A Report of a Workshop held September 6-9, 2022, Los Alamos, NM, November, 2022

Experiments and training with critical assemblies and fissionable material (at or near the critical state) that explore reactivity phenomena are central to a number of national security challenges. From fission energy to nuclear weapons to a broad suite of scientific challenges, it is clear that additional capacity and capability are needed. The National Criticality Experiments Research Center (NCERC) marked 10 years of operations in 2021. This anniversary was an opportune time to celebrate our successes and progress, and to evaluate the remaining and emergent challenges. Against this backdrop, a workshop of approximately 140 national and international leaders in nuclear research was convened in Los Alamos, New Mexico to explore “NCERC Futures.” Therefore, the present workshop focused specifically on needed capabilities and tools to meet the research challenges in eight topical mission areas served by NCERC. As each Topical Group summarized their discussions in the out brief, it was recognized that key challenges could be met through enabling infrastructure investments and new critical assemblies. Enabling infrastructure includes staffing, additional space/buildings, developing an agile bounding safety basis, the ability to keep pace with technological advances in detectors and data acquisition systems (allowing use of those with Bluetooth™ and similar technologies), an expanded set of materials options (especially plutonium), a “Plug and Play” design and implementation mindset, and a facility that enabled free-field measurements. The new critical assemblies that were identified as having the most impact were a bare plutonium (Pu) Critical Assembly, a Horizontal Split Table (HST), a Super Comet, and a Uranium Solution Burst Assembly. NECRC is a unique, one-of-a-kind facility in the United States. If all the improvements were to be made, NCERC would enable the United States and its partners to address many important research questions related to criticality. These include but are not limited to: (1) Covering the entire neutron energy spectrum for both highly enriched uranium (HEU) and Pu in configurations for virtually all conceivable applications; (2) Performing multi-physics solution experiments and irradiations with a Uranium Solution Burst Assembly, which more closely resembles actual criticality accidents; (3) Conducting free-field experiments to make basic fission physics measurements and much cleaner benchmarks with various experimental observables; and (4) Providing more training classes and more experiments annually at greater cost efficiency enabled by additional buildings and machines and an agile, bounding, risk-balanced Safety Basis. In the end, workshop attendees enthusiastically concluded that NCERC Futures are bright and the workshop helped to identify a roadmap of capability gaps that need to be addressed. This report documents the results of those efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Water limiting current measurements in anion exchange membrane fuel cells (AEMFCs); part 1: Water limiting current method development

We report water management is critical in optimizing anion exchange membrane fuel cell (AEMFCs) performance and durability. The role of membranes on determining cell water balance in AEMFCs has received limited study. A more thorough understanding of membrane water transport parameters is necessary for thorough cell optimization. In this study, a novel method for probing limiting current based on water flux across an AEMFC is presented for the first time. The water limiting current has been investigated as a function of relative humidity of anode and cathode streams, cathode backpressure and cell operating temperature. From these studies, it is clearly shown that water diffusion through the membrane from the anode is the most critical mechanism for achieving high current density performance and durability. The developed method can act as a novel screening technique that focuses on the impacts of water transport parameters and helps identify promising membrane materials based on their water transport properties. A critical but underappreciated facet of AEMFC performance and durability.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗
DOE OSTI2024

Machine Learning and Data Science to Advance Laboratory Earthquake Prediction and Illuminate the Mechanics of Precursors to Failure

Earthquakes represent one of our greatest natural hazards and in recent years human induced seismicity is adding to the threat. Even a modest improvement in the ability to forecast devastating large earthquakes or smaller shallow events associated with fluid injection could save thousands of lives and billions of dollars. Current efforts to forecast earthquakes are limited by knowledge of earthquake physics and hampered by a lack of reliable lab or field observations. However, recent work has provided a critical opportunity for advancement. We have found: 1) clear and consistent precursors prior to earthquake-like failure in the laboratory and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the fault zone stress state, the failure time and in some cases the magnitude of lab earthquakes. In addition, the observations include clear precursors to failure in the form of changes in fault zone properties prior to lab earthquakes. Precursors have been observed in previous laboratory studies but their origin is poorly understood and their possible connection to ML based earthquake prediction is unknown. The work conducted under our project has dramatically expanded these efforts. We have developed an integrated data science approach to illuminate the physics of earthquake precursors and lab earthquake prediction. Our work has accelerated the development of ML, artificial intelligence (AI), and related data science approaches by providing massive data sets that are tightly connected to critical scientific problems and by bringing together leading subject matter experts and data scientists. Earthquake physics involves phenomena that are far from equilibrium. Our work has leveraged data science methods to illuminate these phenomena and investigate how they relate to earthquake prediction. In addition to a large database with many types of labeled events that is available to everyone, our work has advanced the fundamental understanding of seismic forecasting, earthquake physics, and fault rheology

58 GEOSCIENCES↗
DOE OSTI2022

Impacts of LEU+ and ATF on Fresh Fuel Storage Criticality Safety

The use of increased fuel enrichment, which is still in the realm of low-enriched uranium (LEU) fuel, has been of interest to commercial light water reactor operators as part of the next iteration in fuel cycle technological advances and research and development. Using increased enrichment fuel, or high-assay LEU (HALEU), in power plants has clear benefits for being able to load cores with additional power-producing fuel. Although HALEU enrichments can range up to 20%, the more guarded approach of investigating enrichments above current fuels within 10% enrichment is referred to as LEU plus (LEU+) to reflect the less drastic change in operating conditions and requirements and similarity to current fuel cycles. Of additional interest and increasing maturity is the incorporation of accident-tolerant fuel (ATF) concepts, which are also applicable to the current fleet. This class of technologies involves changes such as cladding (e.g., chromium coating or FeCrAl) and fuel composition (e.g., chromia dopant) alterations to demonstrate improved fuel performance under accident scenarios. The ability to properly store fuel before and after residence time in the reactor is crucial to plant operation. Typically, this is done in either a new fuel vault (NFV) or spent fuel pool (SFP). Storing, loading, and unloading dozens of fuel assemblies within the same general area provides opportunities for obvious criticality concerns. These concerns are addressed with regulations to the subcriticality margin that the NFV and SFP must maintain in certain conditions. Adopting LEU+ fuel results in inherent reactivity increases, which are extremely relevant for safe fuel storage. Therefore, a clear understanding of the effects of LEU+ fuel and ATF on criticality safety margins to regulatory limits is required, as well as an understanding of the degree of absorber crediting under normal and accident conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Resolve the Clear-Sky Continuous Diurnal Cycle of High-Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Although numerous evapotranspiration (ET) estimates have been developed from remote sensing measurements, a diurnally resolved product with high spatial resolution (<100 m) is still lacking, which is critically needed for agricultural and ecological monitoring. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides, for the first time, ET at 70 m resolution with diurnal samplings, enabling a step toward filling this data gap. This study developed the first framework to resolve the full diurnal cycle of ET and Land Surface Temperature (LST) from sporadic ECOSTRESS measurements. We first constructed 70 m diurnal LST utilizing a diurnal temperature cycle (DTC) model that fused ECOSTRESS and Geostationary Operational Environmental Satellite (GOES) LST. Next, we derived 70 m diurnal ET from the diurnally resolved LST, along with ancillary meteorological and reflectance data sets, using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm. This framework was tested in two regions: A relatively homogeneous semiarid grassland landscape (centered at the US-Seg Ameriflux site) and a heterogeneous humid cropland landscape (centered at US-ARM). Our diurnally resolved LST and ET successfully reproduced the spatial patterns in the native ECOSTRESS measurements during overpasses for both landscapes (LST: r > 0.97, RMSE ~2°C; ET: r > 0.99, and RMSE = 1–7 W m -2 ). The constructed time series also well captured the temporal variability of the in situ measurements (especially under clear-sky conditions) at both US-Seg and US-ARM, albeit performance varying with availability and quality of ECOSTRESS samples. This study sets the stage for testing and applying our framework to broader climates, biomes, and landscapes toward eventually generating diurnally resolved 70 m global operational LST/ET products to enhance ecological/agricultural applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Deconstructing dynamics of symmetry breaking

The Kibble–Zurek mechanism (KZM) successfully predicts the density of topological defects deposited by the phase transitions, but it is not clear why. Its key conjecture is that, near the critical point of the second-order phase transition, critical slowing down will result in a period when the system is too sluggish to follow the potential that is changing faster than its reaction time. The correlation length at the freeze-out instant $\hat{t}$ when the order parameter catches up with the posttransition broken symmetry configuration is then decisive, determining when the mosaic of broken symmetry domains locks in topological defects. To understand why the KZM works so well, we analyze the Landau–Ginzburg model and show why temporal evolution of the order parameter plays such a key role. In conclusion, the analytical solutions we obtain suggest experimentally accessible observables that can shed light on symmetry-breaking dynamics while testing the conjecture on which the KZM is based.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Average and Marginal Capacity Credit Values of Renewable Energy and Battery Storage in the United States Power System

As deployment of renewable resources and storage continue to significantly grow in the coming decades, these technologies will play increasingly important roles in maintaining power systems' resource adequacy. Few analyses so far offer comprehensive comparisons of forward-looking average and marginal capacity credits of variable renewable energy and storage in the U.S. interconnections across a wide range of possible futures. To fill this research gap, we quantify the average and marginal capacity credits of solar PV, onshore and offshore wind, and batteries between 2026 and 2050 across the U.S power systems to examine the temporal trends, spatial patterns, and trade-offs between these two capacity accreditation approaches. Across technologies, capacity credits of solar PV most clearly follow downward trends over time, reflecting the significant rise in solar PV generation share as the grid decarbonizes. While battery storages' generation shares also rise significantly over time, their capacity credits always remain stably high due to their capabilities to be dispatched strategically during critical periods to maintain reliability. On the other hand, capacity credits of wind technologies in general follow slight upward trends as their generation shares level off. There are strong spatial variabilities of both average and marginal capacity credits across technologies, but capacity credits of solar PV displaying the most obvious spatial patterns with high capacity credits concentrating in wind-rich, solar-poor regions in SPP, PJM, and MISO, suggesting potential reliability benefits of interconnection-wide planning for renewable energy deployments. Additionally, except for offshore wind, average capacity credits of all other renewable technologies tend to be higher than their marginal capacity credits, indicating that existing renewable resources tend to be accredited higher than new resources at almost any time.

25 ENERGY STORAGE↗