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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Resistive contributions to the stability of DIII-D ITER baseline demonstration discharges

Here, measurements of the plasma response to applied n = 1 perturbations from DIII-D ITER baseline scenario (IBS) demonstration discharges exhibit increasing amplitude prior to disruption inducing mode locking events. Simulations reveal that changes in ideal and resistive stability impact the response in the DIII-D IBS regime, well below the pressure limit of the external kink mode. The dependencies of the response measurements on the plasma normalized internal inductance ℓ i and beta β N are qualitatively consistent with ideal MHD, although in most cases the amplitude of the measurements exceeds predictions, indicating that the experimental discharges are less stable than expected. Resistive MHD simulations of the response show improved compatibility with the measurements, and the closest agreement is obtained by including the experimental plasma rotation in the simulations. Although the input neutral beam torque is near zero, the simulations show that the corresponding level of rotation can lead to nearly complete screening of the pitch-resonant field component at the q = 2 surface. However, the simulation results exhibit a significant weakening of this screening effect at the lowest rotation levels in the data set, concurrent with predictions of elevated response amplitudes that are consistent with measurements. These comparisons with DIII-D data provide a qualitative validation of a linear, resistive MHD response model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Structural Simluation Toolkit (SST) v.11.0

The SST provides a parallel framework to perform system simulation of computer architectures to determine their performance and power consumption. Additionally, the SST contains basic models of a computer processor, and interconnect and can connect to an external memory simulator (DRAMSim II). The SST framework provides a simple interface by which other computer simulation models can be combined under a common parallel discrete event-based simulation environment. This allows design exploration of future architectures, analysis of how current computer programs will function on future architectures. The SST provides a parallel discrete event simulation framework, including partitioning and object distribution over MPI. It also provides a mechanism by which components can report their power consumption for analysis.

Rodrigues, ArunF.↗
DOE OSTI2021

Machine learning methods for probabilistic locked-mode predictors in tokamak plasmas

A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbations, also known as error fields. This can lead to locking and then to disruptions. We leverage machine learning (ML) methods to predict the locking events. We use a coupled third-order nonlinear ordinary differential equation model to represent the interaction of the magnetic perturbation and the plasma rotation with the error field. This model is sufficient to describe qualitatively the locking and unlocking bifurcations. Here, we explore using ML algorithms with the simulation data and experimental data, focusing on the methods that can be used with sparse datasets. These methods lead to the possibility of the avoidance of locking in real-time operations. We describe the operational space in terms of two control parameters: the magnitude of the error field and the rotation frequency associated with the momentum source that maintains the plasma rotation. The outcomes are quan- tified by order parameters that completely characterize the state, whether locked or unlocked. We use unsupervised ML methods to classify locked/unlocked states and note the usefulness of a certain normalization of the order parameters. Three supervised ML classifiers are used in suite to estimate the probability of locking in the region of control parameter space with hysteresis, i.e., the set of control parameters for which both locked and unlocked states can exist. The results show that a neural network gives the best estimate of the locking probability. An analogy of the present locking model with the van der Waals equation of state is also provided.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Sawtooth suppression by flux pumping on HBT-EP

Abstract This study examines the mechanisms underlying sawtooth suppression in the High Beta Tokamak-Extended Pulse (HBT-EP) device. It is observed that strong-intensity sawtooth activities correlate with reduced-amplitude magnetohydrodynamics (MHD) edge modes which are identified as m / n = 3 / 1 external kink modes, while sawtooth suppression correlates with larger and saturated edge mode amplitudes. To further investigate these correlations, the plasma–wall coupling was manipulated by adjusting the positions of the conducting walls in HBT-EP. It was found that strong sawtooth events occur when the normalized wall radius b / a is within a critical value. This implies that the plasma–wall distance must be sufficiently small to ensure effective stabilization of the edge mode. Even slight differences in major radius result in significantly different discharge styles, categorized as ‘sawtoothing discharges’ and ‘sawtooth-suppressed discharges’ respectively. Through a series of mode structure analyses, we confirm the coexistence and coupling of the m / n = 1 / 1 helical core, m / n = 2 / 1 tearing mode, and m / n = 3 / 1 external kink mode during sawtooth-suppression, and that this coupling induces anomalous current broadening. Based on these findings, we conclude that sawtooth suppression in the HBT-EP tokamak is consistent with the process of magnetic flux pumping.

Physics↗
DOE OSTI2026

Toroidal phase of post-disruption runaway electron loss to wall in presence of applied 3-D fields in DIII-D

The toroidal distribution of runaway electrons (REs) striking the centre post in DIII-D post-disruption RE final loss events is simulated utilizing the MARS-F code combined with the REORBIT module, by tracing the guiding-centre drift orbits of test REs in the presence of external (applied) plus internal (intrinsic instability) 3-D fields. To better recover the experimental results, three different equilibria with different safety factor profiles are adopted. Dominant resistive magnetohydrodynamic instabilities are found to be m/n=2/1, 3/2 and 1/1 modes (where m and n are the poloidal and toroidal mode numbers). Externally applied n=1 resonant magnetic perturbations (RMPs), with both even and odd parity configuration, are considered in each equilibrium. Locking of MHD instability to the applied RMPs is simulated by minimizing the total perturbed magnetic energy in the plasma. Here, the simulated toroidal locations, where most REs strike in the presence of an edge m/n= 2/1 instability and the n=1 RMPs in even parity, are found to agree well with experiments. Modeling also captures the measured n = 2 RE loss pattern on the wall for experiments where an edge m/n=3/2 instability is dominant instead of the m/n=2/1 mode. The key overall findings include (a) the measured global RE toroidal impact pattern in experiments can be explained by a locked resistive kink instability, (b) the toroidal phase of the locked mode is well predicted by minimizing the total perturbed magnetic energy, and (c) this edge locked mode determines the toroidal phase of the peak RE wall impact.

Bai, Xue [University of California San Diego, La J↗
DOE OSTI2026

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2025

Geant4 Event Biasing and Fast Simulation

Geant4 offers advanced event biasing techniques to significantly accelerate simulations involving rare events. Various biasing methods, such as leading particle selection, cross-section biasing, radioactive decay enhancement, and bremsstrahlung splitting, enable efficient event sampling, though they require careful handling. Additionally, Geant4 provides a Fast Simulation Interface, allowing the replacement of standard processes in specific region and for selected particles, enabling faster execution or external code integration. Applications of fast simulation include electromagnetic shower modeling in calorimeters, machine learning inference, and offloading tasks to specialized hardware like GPUs, making Geant4 a powerful tool for computationally demanding simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Timely Reporting of Heavy Hitters Using External Memory

Given an input stream S of size N, a Φ-heavy hitter is an item that occurs at least ΦN times in S. The problem of finding heavy-hitters is extensively studied in the database literature. In this work, we study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = Φ N-th occurrence (and hence it becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (Ω (N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable tradeoff between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device’s random I/O throughput, i.e., ≈100K observations per second.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Anisotropic Electron Fluid Closure Validated by in Situ Spacecraft Observations in the far Exhaust of Guide-field Reconnection

Anisotropic electron heating predictions of the equations of state of Le et al. (2009); https://doi.org/10.1103/PhysRevLett.102.085001 (“the EoS”) are applied to reconnection in the Earth's magnetosheath. The model is applicable to open systems where electrons are streaming along magnetic field lines into the reconnection region, sourced by fixed external reservoirs of plasma ambient to the reconnection region. While spacecraft observations have previously shown the EoS to hold in the region near the X-line, we find that for an event observed far downstream (~100 d i ) from the X-line both inflows and the exhaust follow the predictions of the EoS. Furthermore, the model underlying the EoS is extended to include additional perpendicular heating terms relevant to the considered event undergoing active magnetic compression with local electron trapping. Finally, the agreement between the spacecraft observations and the EoS emphasizes the roles of the ambient plasma sources and the dynamics of trapped and passing electrons in setting and controlling the anisotropic electron heating at large scale in naturally occurring plasma configurations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Evaluating and Countering the Insider Threat to the Radioactive Source Supply Chain

The modern supply chain is a global enterprise and little drove this home more than the global COVID-19 pandemic which sent economic shockwaves throughout the world. Many goods became scarce, as products were delayed, in limited supply, or simply not available. The suddenly diminished supply collided with still high demand and led to greatly increased costs. This was particularly true for the radioactive source supply chain. The pandemic introduced extensive delays for construction projects, slowed the transport of radiological materials to facilities, interrupted treatment deliveries, and impaired the mobility of contractors across the industry. All of these concerns not only adversely affected the economy, but also impacted the safety and security of radiological material, potentially raising national security concerns. The vulnerability of the supply chain, a critical element in an increasingly interconnected world, was exposed. One example that challenged the adaptive capacity of the overall supply chain is the Ever Given container ship, which became stuck in the Suez Canal in 2020. This accident immediately shut down shipments that accounted for 12% of global trade, with long-term impacts estimated to be much larger. Developing the ability to anticipate and react in real-time to sudden changes has quickly become a necessity, particularly in industries that deal with the transport of hazardous material. The reaction to these dramatic incidents was to largely focus attention and resources on protecting the supply chain from external threats. However, the threat to the radioactive material supply chain from insiders intimately involved in the process may be even greater and remains a blind spot that requires increased attention. Recent events revealed the blueprint for targeting and disrupting that supply chain, so the potential for a malicious insider—or a manipulated, unwitting insider—to take advantage of this vulnerability is elevated, creating security concerns for radiological industries. This paper examines and analyzes the potential insider threat to the radioactive source supply chain and recommends steps to take to counter this possibility.

Kinney, Justin↗
DOE OSTI2023

Dichotomy between freshwater and heat flux effects on oceanic conveyor belt stability and global climate

The Atlantic meridional overturning circulation is an important global-scale oceanic circulation, and its changes may be responsible for past abrupt climate change events. By using two versions of a coupled climate model, here we show that the stability of this circulation depends not only on the background climate, but also on the type of primary external forcing: freshwater vs. greenhouse gases. When freshwater forcing is dominant, hysteresis of this circulation (an abrupt collapse/reactivation) becomes possible only under simulated glacial conditions with closed Bering Strait. Under present day and future conditions, both freshwater and greenhouse gas forcings could collapse this circulation, but only greenhouse gas forcing produced a bi-stable equilibrium state comparable to abrupt climate change. Our results demonstrate that the Bering Strait status (open vs. closed) may facilitate or prohibit the existence of this circulation’s hysteresis, irrespective of the background climate conditions, but is directly related to the primary forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Reviving QFT in ( 2 + 1 )-dimensional de Sitter spacetime

We consider a conformally coupled scalar quantum field theory (QFT) on ( 2 + 1 )-dimensional static Einstein universe R × S 2 and write down the free theory Hilbert space. We explain that this theory is secretly a QFT in ( 2 + 1 )-dimensional de Sitter space because all the quantum observables experience “quantum revivals,” which naturally restricts the timelike R to the appropriate de Sitter time range. Our construction circumvents the causal obstruction to formulating QFT in de Sitter due to event horizons. There are not any in static Einstein. The “unitary gauge” description of the theory is realized by the zonal harmonics P ℓ ( n ^ · n ^ ′ ) . We verify that interactions with conformally invariant external sources are mediated only by these modes. Hence these modes comprise the complete basis of the “bulk” theory. When the theory is cut off in the UV, the basis dimension scales as the Bekenstein-Hawking formula. Published by the American Physical Society 2024

D’Amico, Guido (ORCID:0000000281831214)↗
DOE OSTI2024

Deep Learning for Fish Identification from Sonar Data (CRADA 481 Final Report)

In eastern regions of the United States, the American eel is a species of management and regulatory concern because of significant population declines, despite the species’ previous abundance in all tributaries of rivers flowing into the Atlantic Ocean. The American eel is also a candidate for listing under the U.S. Endangered Species Act. While hydropower construction and operation are only one of several factors contributing to this population decline, such a listing could impose additional regulatory challenges for a large number of hydropower projects. In this CRADA project, we improved technologies for identifying migrating eels with the goal of reducing the cost and time required for future American eel hydropower impact assessment and mitigation studies, while maintaining accuracy. We built on results from a previous FOA project (FOA# DE-FOA-0001662), led by the Electric Power Research Institute (EPRI), which developed a highly accurate, deep-learning method for identifying migrating eels from imaging sonar data. The current study aimed to further optimize this deep-learning model, originally designed for image classification, and to develop an object detection software capable of identifying fish from sonar videos in real time, enabling the detection of events like fish migrations and specific species, such as the American eel, at hydropower dams. The data conversion algorithms were packaged as software with a graphical user interface, and the software is evaluated by external collaborators. We focused on the American eel in this project and explored the transferability of the developed deep learning models to the sea lamprey, given the similar body shape and swimming behavior between the two species.

13 HYDRO ENERGY↗
DOE OSTI2021

Small-angle X-ray and neutron scattering

Small-angle scattering (SAS) is a technique that is able to probe the structural organization of matter and quantify its response to changes in external conditions. X-ray and neutron scattering profiles measured from bulk materials or materials deposited at surfaces arise from nanostructural inhomogeneities of electron or nuclear density. Furthermore, the analysis of SAS data from coherent scattering events provides information about the length scale distributions of material components. Samples for SAS studies may be prepared in situ or under near-native conditions and the measurements performed at various temperatures, pressures, flows, shears or stresses, and in a time-resolved fashion. In this Primer, we provide an overview of SAS, summarizing the types of instrument used, approaches for data collection and calibration, available data analysis methods, structural information that can be obtained using the method, and data depositories, standards and formats. Recent applications of SAS in structural biology and the soft-matter and hard-matter sciences are also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Transition between Instability and Seeded Self-Modulation of a Relativistic Particle Bunch in Plasma

We use a relativistic ionization front to provide various initial transverse wakefield amplitudes for the self-modulation of a long proton bunch in plasma. We show experimentally that, with sufficient initial amplitude [ ≥ ( 4.1 ± 0.4) MV / m ], the phase of the modulation along the bunch is reproducible from event to event, with 3%–7% (of 2 π ) rms variations all along the bunch. The phase is not reproducible for lower initial amplitudes. We observe the transition between these two regimes. Phase reproducibility is essential for deterministic external injection of particles to be accelerated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

This paper is the basis for a presentation help at the 2026 Georgia Tech Fault & Disturbance Analysis Conference, which can be found at OSTI # 3168287 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danovo Energy Solutions]↗