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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
NASA NTRS1980

The effect of viewing time, time to encounter, and practice on perception of aircraft separation on a cockpit display of traffic information

The concept of a cockpit display of traffic information (CDTI) includes the integration of air traffic, navigation, and other pertinent information in a single electronic display in the cockpit. Two studies were conducted to develop a clear and concise display format for use in later full-mission simulator evaluations of the CDTI concept. Subjects were required to monitor a CDTI for specified periods of time and to make perceptual judgments concerning the future position of a single intruder aircraft in relationship to their own aircraft. Experimental variables included: type of predictor information displayed on the two aircraft symbols; time to encounter point; length of time subjects viewed the display; amount of practice; and type of encounter (straight or turning). Results show that length of viewing time had little or no effect on performance; time to encounter influenced performance with the straight predictor but did not with the curved predictor; and that learning occurred under all conditions.

Oconnor, S.↗
NASA NTRS1987

Advances in flow visualization using liquid-crystal coatings

This paper discusses a new four-part mixing method for visualizing boundary layer flows, including transitions, separation, and shock locations, by the use of liquid-crystal coatings. The method controls the event temperature and color-play bandwidth best suited to specific experimental conditions, and is easily learned. The method is applicable almost throughout the altitude and speed ranges for subsonic aircraft flight envelopes, and is also applicable to supersonic flow visualization and for general use in high- and low-speed wind tunnel and water tunnel testing.

Holmes, Bruce J.↗
NASA NTRS2007

Ejecta- and Size-Scaling Considerations from Impacts of Glass Projectiles into Sand

One of the most promising means of learning how initial impact conditions are related to the processes leading to the formation of a planetary-scale crater is through scaling relationships.1,2,3 The first phase of deriving such relationships has led to great insight into the cratering process and has yielded predictive capabilities that are mathematically rigorous and internally consistent. Such derivations typically have treated targets as continuous media; in many, cases, however, planetary materials represent irregular and discontinuous targets, the effects of which on the scaling relationships are still poorly understood.4,5 We continue to examine the effects of varying impact conditions on the excavation and final dimensions of craters formed in sand. Along with the more commonly treated variables such as impact speed, projectile size and material, and impact angle,6 such experiments also permit the study of changing granularity and friction angle of the target materials. This contribution presents some of the data collected during and after the impact of glass spheres into a medium-grained sand.

Anderson J. L. B.↗
NASA NTRS2020

Mars 2020 Center Differential Pivot Restraint: Flexurized Spring System Providing Compliance for Rover Mobility Deployment Prior to Landing

The Center Differential Pivot Restraint (CDPR) for the Mars 2020 mission is designed to constrain motion of the rover differential and to dampen out the load response of during rover’s mobility deployment event in Entry, Descent, and Landing (EDL) phase of the mission. For Mars 2020, it was required to redesign the mechanism to save minimum 33% of the mass from the previous Mars Science Laboratory (MSL) design. The new mechanism architecture used a monolithic spring-like flexure to act as the system compliance, resulting in 50% mass savings from the MSL design. Along the design and prototype testing process, several mechanism design principles were re-affirmed as well as lessons learned about potential race conditions.

Cameron, Matthew↗
NASA NTRS2006

Development of a Countermeasure to Mitigate Postflight Locomotor Dysfunction

Astronauts returning from space flight experience locomotor dysfunction following their return to Earth. Our laboratory is currently developing a gait adaptability training program that is designed to facilitate recovery of locomotor function following a return to a gravitational environment. The training program exploits the ability of the sensorimotor system to generalize from exposure to multiple adaptive challenges during training so that the gait control system essentially learns to learn and therefore can reorganize more rapidly when faced with a novel adaptive challenge. Evidence for the potential efficacy of an adaptive generalization gait training program can be obtained from numerous studies in the motor learning literature which have demonstrated that systematically varying the conditions of training enhances the ability of the performer to learn and retain a novel motor task. These variable practice training approaches have been used in applied contexts to improve motor skills required in a number of different sports. The central nervous system (CNS) can produce voluntary movement in an almost infinite number of ways. For example, locomotion can be achieved with many different combinations of joint angles, muscle activation patterns and forces. The CNS can exploit these degrees of freedom to enhance motor response adaptability during periods of adaptive flux like that encountered during a change in gravitational environment. Ultimately, the functional goal of an adaptive generalization countermeasure is not necessarily to immediately return movement patterns back to normal. Rather the training regimen should facilitate the reorganization of available sensory and motor subsystems to achieve safe and effective locomotion as soon as possible after long duration space flight. Indeed, this approach has been proposed as a basic feature underlying effective neurological rehabilitation. We have previously confirmed that subjects participating in an adaptive generalization training program using a variety of visuomotor distortions and throwing as the dependent measure can learn to enhance their ability to adapt to a novel sensorimotor environment (Roller et al., 2001). Importantly, this increased adaptability was retained even one month after completion of the training period. Adaptive generalization has been observed in a variety of other tasks requiring sensorimotor transformations including manual control tasks and reaching (Bock et al., 2001, Seidler, 2003) and obstacle avoidance during walking (Lam and Dietz, 2004). Taken together, the evidence suggests that a training regimen exposing crewmembers to variation in locomotor conditions, with repeated transitions among states, may enhance their ability to learn how to reassemble appropriate locomotor patterns upon return from microgravity. We believe exposure to this type of training will extend crewmembers locomotor behavioral repertoires, facilitating the return of functional mobility after long duration space flight. In other words, our proposed training protocol will compel subjects to develop new behavioral solutions under varying sensorimotor demands. Over time subjects will learn to create appropriate locomotor solution more rapidly enabling acquisition of mobility sooner after long-duration space flight. A gait adaptability training program can be superimposed on nominal treadmill exercise activities thus ensuring that no additional crew time is required to perform this type of training regimen and that it can be implemented with current in-flight exercise systems available on the International Space Station.

Bloomberg, J. J.↗
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts.

Schram, Malachi [Thomas Jefferson National Acceler↗
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2025

Dependence of Convective Cloud Microphysical Properties on Environmental Conditions during the TRACER and ESCAPE Field Campaigns: A Synergistic Approach of Observations, Machine Learning and Parcel Models

The sensitivity of convective clouds to aerosols and their interactions with environment, combined with limited observational constraints in parameterizations, introduces significant uncertainties in atmospheric models. Here, this study investigates the dependence of convective cloud microphysical properties on environmental conditions using a synergistic approach that combines unique observations from the TRACER and ESCAPE field campaigns, machine learning techniques, and parcel model simulations with a super-droplet microphysics scheme. A random forest algorithm identifies in-situ vertical velocity (w), temperature (T), and surface fine-mode aerosol mass concentration as the three most important environmental conditions influencing cloud properties including liquid water content (LWC), number concentration for particles with D max < 50 μm (N c ,<50), 50 μm ≤ D max ≤ 3000 μm (N c,50–3000 ), and droplet effective diameter (D e ). Results show that LWC, N c,<50 , and N c,50–3000 significantly increase with w in updrafts. Across w bins, as T decreases, LWC, D e , and N c,50–3000 increase, while N c,<50 decreases, which are closely linked to the distance above cloud bases. Warmer cloud bases yield higher LWC, greater N c,50–3000 , and smaller N c,<50 , while polluted environments produce greater N c,<50 . Parcel model simulations successfully replicate these observed dependencies. The simulation results indicate that warmer cloud bases enhance condensation generating larger droplets, and differences in droplet sizes are then amplified through collision-coalescence, resulting in a greater N c,50–3000 . Polluted conditions result in a greater N c,<50 primarily due to enhanced cloud condensation nuclei activation despite increased collision-coalescence rates compared to pristine conditions. This study provides observed quantitative patterns characterizing cloud microphysical properties as a function of key environmental parameters, offering valuable constraints for improving physics parameterizations and numerical models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Detecting And Characterizing Archetypes of Unintended Consequences in Engineered Systems

When designing engineered systems, the potential for unintended consequences of design policies or design decisions exists despite best intentions. Conditions that might cause the formation of unintended consequences are often known only in hindsight. However, since these conditions are associated with a single event, it is difficult to uncover the general patterns of conditions leading to unintended consequences. In this research, patterns of conditions associated with unintended consequences are learned from historical data and represented in the form of archetypes. While previous work using systems theoretic modeling has identified high-level archetypes, this work leverages a self-organizing map to learn archetypes of unintended consequences from human-tagged risk factors in a large data set of lessons learned from adverse events at NASA. The sixty-six identified archetypes contain patterns of conditions such as complexity and human-machine interaction associated with the formation of unintended consequences. To validate the archetypes, a sample of the archetypes is represented using system dynamics in order to illustrate that the identified archetypes are specialized versions of known high-level archetypes of unintended consequences. While the research is based upon a specific dataset, the archetypes apply to any engineered system and the pattern of leading indicators open a new path to manage unintended consequences and mitigate the magnitude of potentially adverse outcomes.

Hannah S Walsh↗
NASA NTRS2012

Command Center Training Tool (C2T2)

This abstract presents the training approach taken to create a management-centered, experiential learning solution for the Virginia Port Authority's Port Command Center. The resultant tool, called the Command Center Training Tool (C2T2), follows a holistic approach integrated across the training management cycle and within a single environment. The approach allows a single training manager to progress from training design through execution and AAR. The approach starts with modeling the training organization, identifying the organizational elements and their individual and collective performance requirements, including organizational-specific performance scoring ontologies. Next, the developer specifies conditions, the problems, and constructs that compose exercises and drive experiential learning. These conditions are defined by incidents, which denote a single, multi-media datum, and scenarios, which are stories told by incidents. To these layered, modular components, previously developed meta-data is attached, including associated performance requirements. The components are then stored in a searchable library An event developer can create a training event by searching the library based on metadata and then selecting and loading the resultant modular pieces. This loading process brings into the training event all the previously associated task and teamwork material as well as AAR preparation materials. The approach includes tools within an integrated management environment that places these materials at the fingertips of the event facilitator such that, in real time, the facilitator can track training audience performance and resultantly modify the training event. The approach also supports the concentrated knowledge management requirements for rapid preparation of an extensive AAR. This approach supports the integrated training cycle and allows a management-based perspective and advanced tools, through which a complex, thorough training event can be developed.

Jones, Phillip↗
NASA NTRS2009

What was Learned from Numerical Simulations of F-16XL (CAWAPI) at Flight Conditions

Nine groups participating in the CAWAPI project have contributed steady and unsteady viscous simulations of a full-scale, semi-span model of the F-16XL aircraft. Three different categories of flight Reynolds/Mach number combinations were computed and compared with flight-test measurements for the purpose of code validation and improved understanding of the flight physics. Steady-state simulations are done with several turbulence models of different complexity with no topology information required and which overcome Boussinesq-assumption problems in vortical flows. Detached-eddy simulation (DES) and its successor delayed detached-eddy simulation (DDES) have been used to compute the time accurate flow development. Common structured and unstructured grids as well as individually-adapted unstructured grids were used. Although discrepancies are observed in the comparisons, overall reasonable agreement is demonstrated for surface pressure distribution, local skin friction and boundary velocity profiles at subsonic speeds. The physical modeling, be it steady or unsteady flow, and the grid resolution both contribute to the discrepancies observed in the comparisons with flight data, but at this time it cannot be determined how much each part contributes to the whole. Overall it can be said that the technology readiness of CFD-simulation technology for the study of vehicle performance has matured since 2001 such that it can be used today with a reasonable level of confidence for complex configurations.

Rizzi, Arthur↗
DOE OSTI2026

HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction

Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differences, referred to as the domain shift problem. Domain generalization (DG) solutions aim to address this issue by extracting domain-invariant representations that mitigate errors in unseen domains. However, in hydrological settings, each reservoir exhibits unique inflow patterns, while some metadata beyond observations like spatial information exerts indirect but significant influence. This mismatch limits the applicability of conventional DG techniques to many-domain hydrological systems. To overcome these challenges, we propose HydroDCM, a scalable DG framework for cross-reservoir inflow forecasting. Spatial metadata of reservoirs is used to construct pseudo-domain labels that guide adversarial learning of invariant temporal features. During inference, HydroDCM adapts these features through light-weight conditioning layers informed by the target reservoir’s metadata, reconciling DG’s invariance with location-specific adaptation. Experiment results on 30 real-world reservoirs in the Upper Colorado River Basin demonstrate that our method substantially outperforms state-of-the-art DG baselines under many-domain conditions and remains computationally efficient.

Hu, Pengfei [ORNL] (ORCID:0009000367130950)↗
DOE OSTI2024

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. Here, in this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model’s relative sensitivity to newly introduced leaf-, stem and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

08 HYDROGEN↗
NASA NTRSDate not supplied

Dragonfly Rotor Optimization using Machine Learning Applied to an OVERFLOW Generated Airfoil Database

NASA’s 4th New Frontiers Mission is the Titan Dragonfly relocatable lander. This coaxial quadrotor vehicle will be launched on a rocket to Titan in 2028. Following a gravity assisted Earth flyby and an approximate 6-year transit, Dragonfly will enter the Titan atmosphere around 2034 with the goal of exploring Titan’s pre-biotic chemistry and habitability. The multirotor design for this unique application has continually evolved since 2016 with constraints such as Titan’s cryogenic atmosphere at 95 Kelvin (-288 F), gravity 14% that of Earth’s, atmospheric density 440% of standard sea-level air, and the inability to test the entire system together under all these conditions until the first flight on Titan. This paper focuses on rotor design aspects of the Dragonfly lander and introduces a novel framework for multirotor design optimization considering multiple flight conditions. The methodology leverages machine learning methods and is demonstrated in the context of Dragonfly. A new OVERFLOW Machine Learning Airfoil Performance (PALMO) database is first presented. PALMO is then wrapped inside a Bayesian optimization framework and applied to a 4-rotor system (one side of the Dragonfly lander). Training data is generated on each iteration of the optimization using the CAMRAD-II comprehensive analysis software to evaluate successive rotor designs in multiple relevant flight conditions. An optimal design for the 4-rotor system was found with approximately 900 rotor designs analyzed in CAMRAD-II, which required 9 million queries of the PALMO surrogate models. This demonstration case evaluated 10,000,000 potential candidate rotor designs in 5.5 hours on 114 CPU cores using uniform inflow, and in 27.8 hours using the prescribed wake model. This work thus enables mid-fidelity rotor design optimization without requiring access to high-performance computing.

Dragonfly↗
NASA NTRS1992

Automatic learning rate adjustment for self-supervising autonomous robot control

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

Arras, Michael K.↗