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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ANS Winter 2024 Summary: Optimizing the ATF-2Ramp Power Profile

When the Halden Boiling Water Reactor closed down in 2018, a need to restore the capability for in-reactor power ramp testing arose. Such testing is valuable for studying pellet-clad interaction phenomena in nuclear fuels. The data from these studies is of great interest to a number of research programs, including the accident-tolerant fuel (ATF) program at Idaho National Laboratory (INL). In 2022, Woolstenhulme et al. proposed several power ramp testing ideas using facilities at INL, including irradiation in the Transient Reactor Test Facility (better known as TREAT) and the Advanced Test Reactor (ATR) [1]. Worrall et al. [2] and Labossiere-Hickman et al. [3] subsequently performed feasibility studies for the ATR testing options in 2023. This summary further investigates the three-pin trefoil design (Fig. 1) for the proposed ATF-2Ramp Experiment discussed in Labossiere-Hickman et al. [3]. ATF-2Ramp is designed to operate in the center flux trap (CFT) of the ATR during a powered axial locator mechanism (PALM) cycle: a short, variable-powered cycle with an asymmetric power distribution. Previously, it was shown that tailoring the thickness of the hafnium (Hf) neutron shields (“mini-shrouds”) surrounding each pin offered a degree of control sufficient to achieve the programmatic linear heat generation rate (LHGR) targets for ATF-2Ramp during the high-power period of a PALM cycle. New work involves shortening the experiment test train for consistency with the fuel pins in ATF-2D [4] and then shaping the axial power profile of the three test pins.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

TEMPEST

This repository solves the problem of driver identification through vehicular and biometric data. Through an embedding-based approach and a novel loss function, we're able to distinguish between different drivers' behaviors. This also provides preprocessing for reproducibility of results.The code preprocesses vehicular data, trains neural networks, and outputs predictions.This code introduces a novel embedding-based neural network with a 91% rank-1 accuracy, as well as all code to reproduce training and results.

Musgrove, Kyle↗
DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2024

Assessing the nature of large language models: A caution against anthropocentrism.

Generative AI models garnered a large amount of public attention and speculation with the release of OpenAI’s chatbot, ChatGPT in November of 2022. At least two opinion camps exist – one that is excited about the possibilities these models offer for fundamental changes to human tasks, and another that is highly concerned about the power these models seem to have – especially since the release of GPT-4, which was trained on multimodal data and has ~1.7 trillion (T) parameters. We evaluated some concerns regarding these models’ power by assessing GPT-3.5 using standard, normed, and validated cognitive and personality measures. These measures come from the tradition of psychometrics in experimental psychology and have a long history of providing valuable insights and predictive distinctions in humans. For this seedling project, we developed a battery of tests that allowed us to estimate the boundaries of some of these models’ capabilities, how stable those capabilities are over a short period of time, and how they compare to humans.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Integrated Resistance and Aerobic Training Maintains Cardiovascular and Skeletal Muscle Fitness During 14 Days of Bed Rest

Background: Known incompatibilities exist between resistance and aerobic training. Of particular importance are findings that concurrent resistance and aerobic training reduces the effectiveness of the resistance training and limits skeletal muscle adaptations (example: Dudley & Djamil, 1985). Numerous unloading studies have documented the effectiveness of resistance training alone for the maintenance of skeletal muscle size and strength. However the practical applications of those studies are limited because long ]duration crew members perform both aerobic and resistance exercise throughout missions/spaceflight. To date, such integrated training on the International Space Station (ISS) has not been fully effective in the maintenance of skeletal muscle function. Purpose: The purpose of this study was to evaluate the efficacy of high intensity concurrent resistance and aerobic training for the maintenance of cardiovascular fitness and skeletal muscle strength, power and endurance over 14 days of strict bed rest. Methods: 9 subjects (8 male and 1 female; 34.5 +/- 8.2 years) underwent 14 days of bed rest with concurrent training. Resistance and aerobic training were integrated as shown in table 1. Days that included 2 exercise sessions had a 4-8 hour rest between exercise bouts. The resistance training consisted of 3 sets of 12 repetitions of squat, heel raise, leg press and hamstring curl exercise. Aerobic exercise consisted of periodized interval training that included 30 sec, 2 min and 4 min intervals alternating by day with continuous aerobic exercise.

Ploutz-Snyder, Lori↗
NASA NTRS1974

Correlation analysis of X-ray emission from Cygnus X-1

Normalized autocorrelation and cross-correlation coefficients have been calculated for the X-ray emission of Cygnus X-1 in two energy bins, 2.1-5.1 keV and 5.1-12 keV. The analysis shows a strong correlation between the pulsations in the two energy bins, with the low-energy pulsations lagging behind the high-energy ones, or alternatively a shorter duration of the pulse trains in the high-energy bin. The power spectral density derived from the autocorrelation functions indicates a larger contribution of the high-frequency components for the higher-energy X-rays.

Brinkman, A. C.↗
NASA NTRS1990

From data rich to information wealthy: A case for CLIPS

Webster's dictionary defines the two terms, data and information almost synonymously and, in fact, uses each term in the other's definition. More important for this discussion, it associates data with facts and information with knowledge. It is this distinction that this paper will focus upon while describing a CLIPS-based system that I have built that processes data into information. The goal is to leverage the power of CLIPS without requiring anyone to be trained in the CLIPS syntax or features. In short, a pseudo-CASE tool for CLIPS.

Mason, Larry↗
NASA NTRS2008

Effects of Different Lifting Cadences on Ground Reaction Forces during the Squat Exercise

The purpose of this investigation was to determine the effect of different cadences on the ground reaction force (GRF(sub R)) during the squat exercise. It is known that squats performed with greater acceleration will produce greater inertial forces; however, it is not well understood how different squat cadences affect GRF(sub R). It was hypothesized that faster squat cadences will result in greater peak GRF(sub R). METHODS: Six male subjects (30.8+/-4.4 y, 179.5+/-8.9 cm, 88.8+/-13.3 kg) with previous squat experience performed three sets of three squats using three different cadences (FC = 1 sec descent/1 sec ascent; MC = 3 sec descent/1 sec ascent; SC = 4 sec descent/2 sec ascent) with barbell mass equal to body mass. Ground reaction force was used to calculate inertial force trajectories of the body plus barbell (FI(sub system)). Forces were normalized to body mass. RESULTS: Peak GRF(sub R) and peak FI(sub system) were significantly higher in FC squats compared to MC (p=0.0002) and SC (p=0.0002). Range of GRF(sub R) and FI(sub system) were also significantly higher in FC compared to MC (p<0.05), and MC were significantly higher than SC (p<0.05). DISCUSSION: Faster squat cadences result in significantly greater peak GRF(sub R) due to the inertia of the system. GRF(sub R) was more dependent upon decent cadence than on ascent cadence. PRACTICAL APPLICATION: This study demonstrates that faster squat cadences produce greater ground reaction forces. Therefore, the use of faster squat cadences might enhance strength and power adaptations to long-term resistance exercise training. Key Words: velocity, weight training, resistive exercise

Bentley, Jason R.↗
NASA NTRS2008

Stay Alive--Simulation for Situational Safety Awareness

STAY ALIVE is an idea for a safety awareness simulation prototype, powered by gaming technology, that would make safety training enlightening, engaging and fun. Recalling initial instructions and using situational awareness principles, participants would escape a fire by choosing the appropriate door. Escape times would be measured while stressors increased. This presentation describes how STAY ALIVE utilizes first person point of view (PoV), a generic scenario, immersion- and presence-enhancing design, and ease of distribution to provide more people opportunity to realize, review, analyze and practice effective awareness behaviors. The goals for this prototype include facilitating interest in first-person PoV safety training and eliciting further suggestions on prevention technologies.

Ruder, Michelle↗
NASA NTRS2014

Exploration Technologies for Operations

Although the International Space Station (ISS) assembly has been completed, the Operations support teams continue to seek more efficient and effective ways to prepare for and conduct the ISS operations and future exploration missions beyond low earth orbit. This search for improvement has led to a significant collaboration between the NASA research and advanced software development community at NASA Ames Research Center and the Mission Operations community at NASA Johnson Space Center. Since 2001, NASA Ames Research Center has been developing and applying its advanced intelligent systems and human systems integration research to mission operations tools for several of the unmanned Mars missions operations. Since 2006, NASA Ames Research Center has also been developing and applying its advanced intelligent systems and human systems integration research to mission operations tools for manned operations support with the Mission Operations Directorate at NASA Johnson Space Center. This paper discusses the completion of the development and deployment of a variety of intelligent and human systems technologies adopted for manned mission operations. The technologies associated with the projects include advanced software systems for operations and human-centered computing. Human-centered computing looks to the processes and procedures that people do to perform any given job, then attempts to identify opportunities to improve these processes and procedures. In particular, for mission operations, improvements are quantified by specifically identifying how a tool can increase a persons efficiency, enhance a persons functional capability, andor improve the assurance of a persons decisions. The Ames development team has collaborated with the Mission Operations team to identify areas of efficiencies through technology infusion applications in support of the Plan, Train, and Fly activities of human-spaceflight mission operations. The specific applications discussed in this paper are in the areas of mission planning systems, mission operations design modeling and workflow automation, advanced systems monitoring, mission control technologies, search tools, training management tools, spacecraft solar array management, spacecraft power management, and spacecraft attitude planning. We discuss these specific projects between the Ames Research Center and the Johnson Space Centers Mission Operations Directorate, and how these technologies and projects are enhancing the mission operations support for the International Space Station. We also discuss the challenges, problems, and successes associated with long-distance and multi-year development projects between the research team at Ames and the Mission Operations customers at Johnson Space center. Finally, we discuss how these technology infusion applications and underlying technologies might be used in the future to support on-board operations of the crew and spacecraft systems as human exploration expands beyond low earth orbit to destinations in the solar system where communications delays will require more on-board autonomy and planning by the crew. Longer communications delays will require that the ground mission operations support will be primarily strategic in nature, while the tactical level of planning, systems monitoring and control, and failure analysisisolationrecovery will be the responsibility of both the spacecraft autonomous systems and the crew. Our expectation is that the technologies

mission operations↗
DOE OSTI2025

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Beyond Price Taker: Optimizing Integrated Energy Systems Considering Market/Grid Interactions

Integrated Energy Systems (IES) combine two or more processes to increase the efficiency, flexibility of operation, and the overall reliability. However, analyzing IESs in volatile electricity markets is challenging, since the volatility in electricity prices makes the conventional levelized cost-type analysis less realistic. This work presents two approaches to address the challenge: price-taker and a surrogates-based approach for incorporating market interactions. The price-taker approach formulates a multiperiod optimization problem that takes the time-varying electricity prices into account, and solves the optimization problem to determine the optimal operational schedule that maximizes the chosen economic metric. This approach is successfully applied to investigate the performance of flexible power and hydrogen co-production systems. The market surrogates approach trains a machine learning model to predict the market behavior as a function of the characteristics of the IES. The trained surrogate model is used to optimize the design and operation of the given IES in an electricity market. This approach is demonstrated on a case study involving a nuclear power plant retrofitted with a low-temperature electrolysis unit to co-produce power and hydrogen.

beyond price taker↗
DOE OSTI2025

Surrogate Model for Distribution Networks Influenced by Weather

Here, we propose a method for generating reduced representations of time series and for constructing low dimensional surrogate models for time dependent calculations of power and voltage in distribution networks. We employ Fourier polynomials. The surrogate model strategy is aimed at reducing the computational cost of time dependent simulations, albeit, at the expense of fidelity. The reduced representation is achieved by identifying a small and most consequential subset of degrees of freedom. In power and voltage distribution networks dynamics that are heavily influenced by strong cyclic weather events, e.g., the hourly, diurnal and seasonal cycles, the weather/climate time series spectrum exposes these most energetic components. Once the degrees of freedom are identified their amplitudes are optimized using training data. The key challenge in using spectral methods in power network surrogates is addressing the computation of quotients. For this we propose a numerically-stable deconvolution strategy.

Weather events↗
DOE OSTI2024

Experimental Setup and Learning-Based AI Model for Developing Accurate PV Inverter Models

The integration of power electronics-based interfaces presents challenges due to the absence of detailed models and the high computational complexity. Generic models used in system studies lack accuracy in capturing converter dynamics. This paper proposes a data-driven approach developed from experimental setup data. This approach enhances accuracy in photovoltaic inverter modeling. We used two types of PV inverters in the experiment. The recorded experimental data undergo processing through a machine learning model. Results from the model trained through machine learning is also presented.

artificial intelligence↗
DOE OSTI1991

Nuclear Safety [Vol. 32, No. 4, October-December 1991]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 477 Report on the International Symposium on the Use of Probabilistic Safety Assessment for Operational Safety—PSA '91, S. Chakraborty and M. Khatib-Rahbar; 488 Good Relationships Are Pivotal in Nuclear Data Bases, A. S. Heger and B. V. Koen; 494 Technical Note: The Interagency Nuclear Safety Review Panel's Evaluation of the Ulysses Space Mission, J. A. Sholtis, Jr., D. A. Huff, L. B. Gray, N. P. Klug, and R. O. Winchester; ACCIDENT ANALYSIS: 502 The Severe Accident Analysis Program for the Savannah River Nuclear Production Reactors, M. L. Hyder; CONTROL AND INSTRUMENTATION: 511 A Framework for Selecting Suitable Control Technologies for Nuclear Power Plant Systems, R. A. Kisner; DESIGN FEATURES: 521 Containments for Gas-Cooled Power Reactors History and Status, P.W. Williams; ENVIRONMENTAL EFFECTS: 537 Indoor Radon: A Natural Risk, N. H. Harley and J.H. Harley; On the Importance of the Atmospheric Parameters in the Fission Products Distribution of a Severe Reactor Accident, M. C. Barla and A. R. Bayulken; Book Review: Health Effects of Exposure to Low Levels of Ionizing Radiation BEIR V, C. R. Richmond; WASTE AND SPENT FUEL MANAGEMENT: 555 Activities Related to Waste and Spent Fuel Management, M. D. Muhlheim and E. G. Silver OPERATING EXPERIENCES: 567 Effects of Component Aging on the Westhinghouse Control Rod Drive System, K. Sullivan and W. Gunther; 577 Reactor Shutdown Experience, Compiled by J. W. Cletcher, 580 Selected Safety-Related Events, Compiled by G. A. Murphy; 582 Operating U 8 Power Reactors, Compiled by M. D. Muhlheim and E. G. Silver, RECENT DEVELOPMENTS: 596 General Administrative Activities, Compiled by M. D. Muhlheim and E. G. Silver; 610 Reports, Standards, and Safety Guides, D. S. Queener; 614 Proposed Rule Changes as of June 30, 1991; ANNOUNCEMENTS: 520 Workshop on PC PRAISE: A Probabilistic Fracture Mechanics Personal Computer Code for Nuclear Power Plant Piping Reliability Assessment; 536 Fifth Workshop on Nuclear Power Plant Containment Integrity; 624 New OECD "International Information System on Occupational Exposure (ISOE)"; 625 Harvard School of Public Health Announces Short Courses; 625 Eighth Power Plant Dynamics, Control and Testing Symposium; 626 Second Training Course on Off-Site Emergency Planning and Response for Nuclear Accidents; 618 The Authors; 622 Reviewers of Nuclear Safety, Vol 32.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2007

Development and Assessment of a Novel Training Package for Basic Maneuvering Tasks on a Flight Simulator Using Self Instruction Methods and Above Real Time Training (ARTT)

One result of the relatively recent advances in computing technology has been the decreasing cost of computers and increasing computational power. This has allowed high fidelity airplane simulations to be run on personal computers (PC). Thus, simulators are now used routinely by pilots to substitute real flight hours for simulated flight hours for training for an aircraft type rating thereby reducing the cost of flight training. However, FAA regulations require that such substitution training must be supervised by Certified Flight Instructors (CFI). If the CFI presence could be reduced or eliminated for certain tasks this would mean a further cost savings to the pilot. This would require that the flight simulator have a certain level of 'intelligence' in order to provide feedback on pilot performance similar to that of a CFI. The 'intelligent' flight simulator would have at least the capability to use data gathered from the flight to create a measure for the performance of the student pilot. Also, to fully utilize the advances in computational power, the simulator would be capable of interacting with the student pilot using the best possible training interventions. This thesis reports on the two studies conducted at Tuskegee University investigating the effects of interventions on the learning of two flight maneuvers on a flight simulator and the robustness and accuracy of calculated performance indices as compared to CFI evaluations of performance. The intent of these studies is to take a step in the direction of creating an 'intelligent' flight simulator. The first study deals with the comparisons of novice pilot performance trained at different levels of above real-time to execute a level S-turn. The second study examined the effect of out-of-the-window (OTW) visual cues in the form of hoops on the performance of novice pilots learning to fly a landing approach on the flight simulator. The reliability/robustness of the computed performance metrics was assessed by comparing them with the evaluations of the landing approach maneuver by a number of CFIs.

Ali, Syed Firasat↗
NASA NTRS1961

Applications of Power Spectral Analysis Methods to Maneuver Loads Obtained on Jet Fighter Airplanes During Service Operations

Power spectral densities of normal load factor have been obtained for two service operational training flights of a Republic F-84G airplane and three service operational training flights of a North American F-86A airplane in order to indicate the load-factor frequency content and possible uses of power spectral methods in analyzing maneuver load data. It was determined that the maneuvering load-factor time histories appeared to be described by a truncated normal distribution. The power spectral densities obtained were relatively level at frequencies below 0.03 cycle per second and varied inversely with approximately the cube of the frequency at the higher frequencies. In general, the frequency content was very low above 0.2 cycle per second. The load-factor peak distributions were estimated fairly well from the spectrum analysis. In addition, peak load data obtained during service operations of fighter-type airplanes with flight time totaling about 24,000 hours were examined and appeared to agree reasonably well with the type of equations obtained from spectrum peak-load distributions.

Mayer, John P.↗