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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Infrared Observations of the Orion Capsule During EFT-1 Hypersonic Reentry

High-resolution infrared observations of the Orion capsule during its atmospheric reentry on December 5, 2015 were made from a US Navy NP-3D. This aircraft, equipped with a long-range optical sensor system, tracked the capsule from Mach 10 to 7 from a distance of approximately 60 nmi. Global surface temperatures of the capsule's thermal heatshield were derived from near infrared intensity measurements. The global surface temperature measurements complemented onboard instrumentation and were invaluable to the interpretation of the in-depth thermocouple measurements which rely on inverse heat transfer methods and material response codes to infer the desired surface temperature from the sub-surface measurements. The full paper will address the motivations behind the NASA Engineering Safety Center sponsored observation and highlight premission planning processes with an emphasis on aircraft placement, optimal instrument configuration and sensor calibrations. Critical aspects of mission operations coordinated from the NASA Johnson Spaceflight Center and integration with the JSC Flight Test Management Office will be discussed. A summary of the imagery that was obtained and processed to global surface temperature will be presented. At the capsule's point of closest approach relative to the imaging system, the spatial resolution was estimated to be approximately 15-inches per pixel and was sufficient to identify localized temperature increases associated with compression pad support hardware on the heatshield. The full paper will discuss the synergy of the quantitative imagery derived temperature maps with in-situ thermocouple measurements. Comparison of limited onboard surface thermocouple data to the image derived surface temperature will be presented. The two complimentary measurements serve as an example of the effective leveraging of resources to advance the understanding of high Mach number environments associated with an ablated heatshield and provide unique data for the validation of design tools and numerical flight simulation techniques. Collaborative opportunities and technology investments in support of planned observations of NASA's next Orion flight test in 2018 will be explored in the full manuscript.

Horvath, Thomas J.↗
DOE OSTI2025

LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout

Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. Deep neural networks (DNNs) have recently emerged as a promising solution to improve readout accuracy . Prior hardware implementations of DNN-based readout are resource-intensive and suffer from high inference latency, limiting their practical use in low-latency decoding and quantum error correction (QEC) loops. This paper proposes LUNA, a fast and efficient superconducting qubit readout accelerator that combines low-cost integrator-based preprocessing with Look-Up Table (LUT) based neural networks for classification. The architecture uses simple integrators for dimensionality reduction with minimal hardware overhead, and employs LogicNets (DNNs synthesized into LUT logic) to drastically reduce resource usage while enabling ultra-low-latency inference. We integrate this with a differential evolution based exploration and optimization framework to identify high-quality design points. Our results show up to a 10.95x reduction in area and 30% lower latency with little to no loss in fidelity compared to the state-of-the-art. LUNA enables scalable, low-footprint, and high-speed qubit readout, supporting the development of larger and more reliable quantum computing systems.

Farooq, M. A. [Arizona State U., Tempe]↗
NASA NTRS2010

Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems

Mathematically sound techniques are used to view a knowledge-based system (KBS) as a set of processes executing in parallel and being enabled in response to specific rules being fired. The set of processes can be manipulated, examined, analyzed, and used in a simulation. The tool that embodies this technology may warn developers of errors in their rules, but may also highlight rules (or sets of rules) in the system that are underspecified (or overspecified) and need to be corrected for the KBS to operate as intended. The rules embodied in a KBS specify the allowed situations, events, and/or results of the system they describe. In that sense, they provide a very abstract specification of a system. The system is implemented through the combination of the system specification together with an appropriate inference engine, independent of the algorithm used in that inference engine. Viewing the rule base as a major component of the specification, and choosing an appropriate specification notation to represent it, reveals how additional power can be derived from an approach to the knowledge-base system that involves analysis, simulation, and verification. This innovative approach requires no special knowledge of the rules, and allows a general approach where standardized analysis, verification, simulation, and model checking techniques can be applied to the KBS.

Hinchey, Mike↗
DOE OSTI2025

MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models

Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining computational efficiency. However, MoEs introduce several inference-time challenges, including load imbalance across experts and the additional routing computational overhead. To address these challenges and fully harness the benefits of MoE, a systematic evaluation of hardware acceleration techniques is essential. We present MoE-Inference-Bench, a comprehensive study to evaluate MoE performance across diverse scenarios. We analyze the impact of batch size, sequence length, and critical MoE hyperparameters such as FFN dimensions and number of experts on throughput. We evaluate several optimization techniques on Nvidia H100 GPUs, including pruning, Fused MoE operations, speculative decoding, quantization, and various parallelization strategies. Our evaluation includes MoEs from the Mixtral, DeepSeek, OLMoE and Qwen families. The results reveal performance differences across configurations and provide insights for the efficient deployment of MoEs.

Chitty-Venkata, Krishna Teja↗
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS1971

Remote Sensing of Sea Surface Temperature from Earth Satellites

Techniques for measuring sea surface temperature using remote sensors in earth orbiting satellites are discussed. Specific reference is made to the infrared sensors carried by the TIROS Operational Satellite. The development of a method to examine a large number of measurements in order to differentiate the temperature effects of clouds is described. It is stated that the system used is completely objective and minimizes the influence of atmospheric absorption, cloud contamination, and instrumental noise on the inferred sea surface temperatures.

Rao, P. K.↗
DOE OSTI2025

Power balance and divertor asymmetries in the Super-X divertors of MAST-U using SOLPS-ITER

Spherical tokamaks (STs) present unique challenges and opportunities in the area of particle and power exhaust, intensified due to their more compact sizes. Substantial efforts are underway in STs to determine the limits in dissipative operational regimes and advanced divertor solutions, including at MAST-U which provides access to the Super-X divertor configuration. Power balance, and upper/lower divertor asymmetries have been studied using SOLPS-ITER simulations of the MAST-U Super-X divertor. A set of simulations with experimentally inferred transport coefficients with E x B and diamagnetic drifts activated, consisting of density and power scans, and high field side vs low field side gas puff locations, have been used for code experimentation to uncover trends beyond the current experimental parameter space. The upper biased asymmetry (U:L > 1) of the ratio of the peaks of the plasma energy flux densities at the outer targets increases with heating power and decreases with gas puff strength, going from symmetric to up to a factor of 15. The upper target electron temperature has been found to be a good ordering quantity for the magnitude of this asymmetry for all heating powers, gas puff strength, and gas puff locations. The lower divertor biased asymmetry (U:L < 1) of the radiation patterns processed through SOLPS-based bolometry synthetic diagnostics is in qualitative agreement with resistive bolometry experimental results, and it is in quantitative agreement with the trend of the total volume radiation within the divertors of SOLPS. However, radiation measurements alone are not sufficient to infer the magnitude of the asymmetry of the peaks of the power loads at the targets.

MAST-U↗
DOE OSTI2025

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker–Planck Equations

The Fokker-Planck (FP) equation is a foundational partial differential equation (PDE) in stochastic processes involving Brownian motions. However, the curse of dimensionality (CoD) poses a formidable challenge when dealing with high-dimensional FP equations. Although Monte Carlo simulation and (vanilla) Physics-Informed Neural Networks (PINNs) have shown the potential to tackle CoD, both methods exhibit significant numerical errors in high dimensions when dealing with the probability density function (PDF) associated with Brownian motion. The point-wise PDF values tend to decrease exponentially as dimensionality increases, surpassing the precision of numerical simulations and resulting in substantial errors. In addition, due to its massive sampling, Monte Carlo fails to offer fast sampling. Modeling the logarithm likelihood (LL) via vanilla PINNs transforms the FP equation into a notoriously difficult Hamilton-Jacobi-Bellman (HJB) equation, which is impractical for PINN learning, whose error grows rapidly with dimension. To this end, we propose a novel approach utilizing a score-based solver to fit the score function in stochastic differential equations (SDEs). The score function, defined as the gradient of the LL, plays a fundamental role in inferring LL and PDF and enables fast SDE sampling, offering an effective means to overcome the CoD. Three fitting methods, Score Matching (SM), Sliced Score Matching (SSM), and Score-PINN, are introduced, each contributing unique advantages in computational complexity, accuracy, and generality. The proposed score-based SDE solver operates in two stages: first, employing score matching or Score-PINN to acquire the score function; and second, solving the LL via an ordinary differential equation (ODE) using the obtained score function. Comparative evaluations across these methods showcase varying trade-offs. The proposed methodology is evaluated across diverse SDEs, including anisotropic Ornstein-Uhlenbeck processes, geometric Brownian motion, and Brownian motion with varying eigenspace. We also test various distributions, including Gaussian, Log-normal, Laplace, and Cauchy distributions. The numerical results demonstrate the score-based SDE solver’s stability, speed, and performance across different experimental settings, solidifying its potential as a solution to CoD for high-dimensional FP equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Acceptance criteria for in situ surveillance of MSR materials based on thermally-loaded mechanical test articles

This report describes practices and acceptance test procedures for designing, running, and maintaining a material surveillance program in a future operating molten salt reactor. The programs described here rely on test data from passively actuated mechanical test articles inserted into critical regions of the reactor and periodically removed for out-of-reactor testing. The report defines definite acceptance procedures, based on the results of these tests, to determine whether a component can continue to operate accounting for the accumulation of environmentally-assisted mechanical damage in the component materials to date, and extrapolated out through the next inspection period. Additionally, the report describes work on a software tool implementing many of the surveillance methods and procedures described here and progress on simplified methods for inferring damage accumulation in the test articles, based on out-of-reactor thermal cycling, that do not rely on sophisticated numerical analysis.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Situation assessment in the Paladin tactical decision generation system

Paladin is a real-time tactical decision generator for air combat engagements. Paladin uses specialized knowledge-based systems and other Artificial Intelligence (AI) programming techniques to address the modern air combat environment and agile aircraft in a clear and concise manner. Paladin is designed to provide insight into both the tactical benefits and the costs of enhanced agility. The system was developed using the Lisp programming language on a specialized AI workstation. Paladin utilizes a set of air combat rules, an active throttle controller, and a situation assessment module that have been implemented as a set of highly specialized knowledge-based systems. The situation assessment module was developed to determine the tactical mode of operation (aggressive, defensive, neutral, evasive, or disengagement) used by Paladin at each decision point in the air combat engagement. Paladin uses the situation assessment module; the situationally dependent modes of operation to more accurately represent the complex decision-making process of human pilots. This allows Paladin to adapt its tactics to the current situation and improves system performance. Discussed here are the details of Paladin's situation assessment and modes of operation. The results of simulation testing showing the error introduced into the situation assessment module due to estimation errors in positional and geometric data for the opponent aircraft are presented. Implementation issues for real-time performance are discussed and several solutions are presented, including Paladin's use of an inference engine designed for real-time execution.

Mcmanus, John W.↗
NASA NTRS1988

High level intelligent control of telerobotics systems

A high level robot command language is proposed for the autonomous mode of an advanced telerobotics system and a predictive display mechanism for the teleoperational model. It is believed that any such system will involve some mixture of these two modes, since, although artificial intelligence can facilitate significant autonomy, a system that can resort to teleoperation will always have the advantage. The high level command language will allow humans to give the robot instructions in a very natural manner. The robot will then analyze these instructions to infer meaning so that is can translate the task into lower level executable primitives. If, however, the robot is unable to perform the task autonomously, it will switch to the teleoperational mode. The time delay between control movement and actual robot movement has always been a problem in teleoperations. The remote operator may not actually see (via a monitor) the results of high actions for several seconds. A computer generated predictive display system is proposed whereby the operator can see a real-time model of the robot's environment and the delayed video picture on the monitor at the same time.

Mckee, James↗
NASA NTRS2000

Remotely Sensed Precipitation from the January 2000 East Coast Blizzard

Initial precipitation forecasts of the January 2000 blizzard were unable to adequately alert emergency response along the eastern seaboard. Cyclogenesis, over the Gulf of Mexico was a contributing cause of that problem. We present distributions of precipitation derived from spaceborne microwave measurements from the Special Sensor Microwave/ Imager (SSM/I) and the TRMM Microwave Imager (TMI) supplemented by NWS operational radar and the National lightning Detection Network (NLDN) Long-Range (LR) product. The combined NWS radar and NLDN-LR data revealed the location of precipitation between 1/23 and 1/27/2000 over the US, the Gulf and the Atlantic Ocean. The remotely sensed precipitation distributions are compared to the precipitation inferred from coincident TMI observations.

Chang, D.-E.↗
NASA NTRS2001

Challenges and Lessons Learned in the Application of Autonomy to Space Operations

NASA's Space Operations Management Office (SOMO) is working toward a goal of providing an integrated infrastructure of mission and data services for space missions undertaken by NASA enterprises. A significant portion of this effort is focused on reducing the cost of these services. We are interested in the potential of autonomy to reduce operations costs. SOMO services support space missions, but are not part of the mission objectives; therefore the level of acceptable risk is very low. In fact, SOMO could be effective ly prevented from applying autonomy if customers merely perceive it as adding risk to their mission(s). We are interested in this workshop from the standpoint of understanding what can be done to realize the potential cost savings due to autonomy while maintaining acceptable risk and serving the needs of our customers. We would like to present our lessons learned so far in adopting autonomy and automation, which we think will contribute to clarifying the challenges facing the use of such technology. SOMO provides services to a diverse and ambitious set of mission customers. Many of these missions are groundbreaking missions for which communications, data, and other operations requirements sometimes cannot be clearly articulated early in the program. This motivates a need for systems that are robust in the face of unanticipated situations so that customer missions are not unreasonably constrained or impacted by "shortcomings" in SOMO services. One of SOMO's primary goals is to realize a paradigm in which SOMO acts as a service provider to organizations that fly space missions for NASA, other government agencies, and even the commercial sector. These organizations purchase SOMO services "by the pound" as customers. We have to provide systems that are not experiments themselves, but rather stable bases from which to do bold experiments. To this end, SOMO also seeks to work closely with industry to see that robust autonomy technology gets infused into products and services for the space industry and beyond. The potential for application of these technologies spans space-based communications networks (e.g. TDRSS) and ground-based assets including communication and tracking antenna systems, data networks, and control centers. There are several problems that are candidates for the application of autonomy, if it can be made reliable enough, including: antenna control, antenna scheduling, communication link scheduling and operation, navigation, attitude determination, fault detection, isolation, and reconfiguration (for spacecraft or ground assets), and mission-level planning and scheduling. Some attempts have been made to apply autonomy and automation in these areas in the past with varying degrees of success. We will present relevant case histories and the lessons inferred from them. Combining this past experience with anticipated future needs, we can clarify the challenges that must be met in order to realize the benefits of autonomy.

Forrest, David J.↗
NASA NTRS2011

Process Algebra Approach for Action Recognition in the Maritime Domain

The maritime environment poses a number of challenges for autonomous operation of surface boats. Among these challenges are the highly dynamic nature of the environment, the onboard sensing and reasoning requirements for obeying the navigational rules of the road, and the need for robust day/night hazard detection and avoidance. Development of full mission level autonomy entails addressing these challenges, coupled with inference of the tactical and strategic intent of possibly adversarial vehicles in the surrounding environment. This paper introduces PACIFIC (Process Algebra Capture of Intent From Information Content), an onboard system based on formal process algebras that is capable of extracting actions/activities from sensory inputs and reasoning within a mission context to ensure proper responses. PACIFIC is part of the Behavior Engine in CARACaS (Cognitive Architecture for Robotic Agent Command and Sensing), a system that is currently running on a number of U.S. Navy unmanned surface and underwater vehicles. Results from a series of experimental studies that demonstrate the effectiveness of the system are also presented.

process algebras↗
DOE OSTI2026

Improving the Confidence in Retrievals of Vertical Distributions of Cloud Condensation Nuclei Number Concentration from ARM Supported by Aircraft In Situ Observations

Accurate quantification of the vertical distribution of cloud condensation nuclei (CCN) number concentrations is critical for improving our understanding of aerosol–cloud interactions. Ground-based Raman lidars operated by the Atmospheric Radiation Measurement (ARM) program, together with surface CCN measurements, are used to retrieve vertically resolved CCN number concentrations (Retrieved Number concentration of CCN, RNCCN). These retrievals rely on several assumptions, including that aerosol composition is vertically homogeneous. To assess this assumption, we developed and tested a framework to infer the dominant aerosol classes/types at different altitudes. This was done by applying a k-Nearest-Neighbors (kNN) algorithm to lidar ratio and linear depolarization ratio measurements from Raman lidar. We evaluated the framework using aircraft aerosol and CCN measurements from the ARM Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) field campaign. The results show that RNCCN performance degrades as vertical aerosol complexity increases, i.e., RNCCN agrees with the aircraft CCN in vertically homogeneous conditions, but closure decreases in layered aerosol structures. To generalize beyond individual examples, we introduce a metric (heterogeneity index) that quantifies the vertical complexity by assessing the variation of inferred aerosol classes/types. Case-level statistics show a tendency for RNCCN and aircraft differences to increase with this metric. By detecting retrievals that are likely compromised by aerosol vertical heterogeneity, the proposed framework improves the interpretability and effective use of RNCCN used for long-term evaluation of models and aerosol–cloud interactions.

Tian, Jingjing↗
NASA NTRS1958

Wind-tunnel investigation of the heat transfer to the X-15 airplane

Paper describes the wind tunnel test program which has provided a picture of the general distribution of heat transfer coefficients over the X-15 airplane. The tunnel tests have emphasized the importance of the interactions of the flow fields of components and have shown that in some cases, where the local flow direction and total pressure can be reasonably closely inferred, use of experimental pressures to compute flow conditions yields very good predictions of heat transfer rate. There are still some regions of the airplane for which the heat transfer could not be adequately studied because of the size or construction of the model. Such local regions will not necessarily limit the operation of the airplane but will require evaluation in the full-scale tests. (author)

Feller, William V↗
NASA NTRS1976

An estimate of the geodetic accuracy attainable with a transportable lunar laser station

A transportable lunar laser ranging station can be expected to measure the range to the lunar corner retroreflectors from a site on the earth's surface with an uncertainty of about 3 cm. The accuracy with which that station can infer its geocentric position is influenced by many factors including the uncertainties in the pole position and rotation rate of the earth and the data loss due to weather. The results of a simplified modeling study intended to include these factors are presented in cartographic form. The modeling indicates that, assuming the successful operation of three or more widely separated fixed laser stations, the transportable station will be able to determine its relative geocentric position in all three coordinates with an accuracy comparable to the original range uncertainty.

Silverberg, E. C.↗
NASA NTRS2006

Calculating Statistical Orbit Distributions Using GEO Optical Observations with the Michigan Orbital Debris Survey Telescope (MODEST)

NASA's Orbital Debris measurements program has a goal to characterize the small debris environment in the geosynchronous Earth-orbit (GEO) region using optical telescopes ("small" refers to objects too small to catalog and track with current systems). Traditionally, observations of GEO and near-GEO objects involve following the object with the telescope long enough to obtain an orbit suitable for tracking purposes. Telescopes operating in survey mode, however, randomly observe objects that pass through their field of view. Typically, these short-arc observation are inadequate to obtain detailed orbits, but can be used to estimate approximate circular orbit elements (semimajor axis, inclination, and ascending node). From this information, it should be possible to make statistical inferences about the orbital distributions of the GEO population bright enough to be observed by the system. The Michigan Orbital Debris Survey Telescope (MODEST) has been making such statistical surveys of the GEO region for four years. During that time, the telescope has made enough observations in enough areas of the GEO belt to have had nearly complete coverage. That means that almost all objects in all possible orbits in the GEO and near- GEO region had a non-zero chance of being observed. Some regions (such as those near zero inclination) have had good coverage, while others are poorly covered. Nevertheless, it is possible to remove these statistical biases and reconstruct the orbit populations within the limits of sampling error. In this paper, these statistical techniques and assumptions are described, and the techniques are applied to the current MODEST data set to arrive at our best estimate of the GEO orbit population distribution.

Matney, M.↗