SEARCH · Engineering Papers

Results for “Distance learning”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗

NASA NTRS2010

JANNAF Lessons Learned Panel Discussion

The difference between the AS-510 observed and predicted separation distance is attributed to a greater F-1 engine "tail off" impulse than that used in the separation distance prediction. . The F-1 thrust decay was normal and not appreciably different from previous (AS-505 through 509) flights. . Analysis indicates that with an S-IC stage having only four retro motors, failure of one retro motor to ignite would result in marginal separation distances and, in the 3-sigma case, re-contact of the two stages. . S-IC-11 and subsequent flight stages were equipped with eight retro motors rather than the planned four.

Richards, Steve↗

NASA NTRS2015

Advanced Concept Studies for Supersonic Commercial Transports Entering Service in the 2018-2020 Period Phase 2

Lockheed Martin Aeronautics Company (LM), working in conjunction with General Electric Global Research (GE GR) and Stanford University, executed a 19 month program responsive to the NASA sponsored "N+2 Supersonic Validation: Advanced Concept Studies for Supersonic Commercial Transports Entering Service in the 2018-2020 Period" contract. The key technical objective of this effort was to validate integrated airframe and propulsion technologies and design methodologies necessary to realize a supersonic vehicle capable of meeting the N+2 environmental and performance goals. The N+2 program is aligned with NASA's Supersonic Project and is focused on providing system level solutions capable of overcoming the efficiency, environmental, and performance barriers to practical supersonic flight. The N+2 environmental and performance goals are outlined in the technical paper, AIAA-2014-2138 (Ref. 1) along with the validated N+2 Phase 2 results. Our Phase 2 efforts built upon our Phase 1 studies (Ref. 2) and successfully demonstrated the ability to design and test realistic configurations capable of shaped sonic booms over the width of the sonic boom carpet. Developing a shaped boom configuration capable of meeting the N+2 shaped boom targets is a key goal for the N+2 program. During the LM Phase 1 effort, LM successfully designed and tested a shaped boom trijet configuration (1021) capable of achieving 85 PLdB under track (forward and aft shock) and up to 28 deg off-track at Mach 1.6. In Phase 2 we developed a refined configuration (1044-2) that extended the under 85 PLdB sonic boom level over the entire carpet of 52 deg off-track at a cruise Mach number of 1.7. Further, the loudness level of the configuration throughout operational conditions calculates to an average of 79 PLdB. These calculations rely on propagation employing Burger's (sBOOM) rounding methodology, and there are indications that the configuration average loudness would actually be 75 PLdB. We also added significant fidelity to the design of the configuration in this phase by performing a low speed wind tunnel test at our LTWT facility in Palmdale, by more complete modelling of propulsion effects in our sonic boom analysis, and by refining our configuration packaging and performance assessments. Working with General Electric, LM performed an assessment of the impact of inlet and nozzle effects on the sonic boom signature of the LM N+2 configurations. Our results indicate that inlet/exhaust streamtube boundary conditions are adequate for conceptual design studies, but realistic propulsion modeling at similar stream-tube conditions does have a small but measurable impact on the sonic boom signature. Previous supersonic transport studies have identified aeroelastic effects as one of the major challenges associated with the long, slender vehicles particularly common with shaped boom aircraft (Ref. 3). Under the Phase 2 effort, we have developed a detailed structural analysis model to evaluate the impact of flexibility and structural considerations on the feasibility of future quiet supersonic transports. We looked in particular at dynamic structural modes and flutter as a failure that must be avoided. We found that for our N+2 design in particular, adequate flutter margin existed. Our flutter margin is large enough to cover uncertainties like large increases in engine weight and the margin is relatively easy to increase with additional stiffening mass. The lack of major aeroelastic problems probably derives somewhat from an early design bias. While shaped boom aircraft require long length, they are not required to be thin. We intentionally developed our structural depths to avoid major flexibility problems. So at the end of Phase 2, we have validated that aeroelastic problems are not necessarily endemic to shaped boom designs. Experimental validation of sonic boom design and analysis techniques was the primary objective of the N+2 Supersonic Validations contract; and in this Phase, LM participated in four high speed wind tunnel tests. The first so-called Parametric Test in the Ames 9x7 tunnel did an exhaustive look at variation effects of the parameters: humidity, total pressure, sample time, spatial averaging distance and number of measurement locations, and more. From the results we learned to obtain data faster and more accurately, and made test condition tolerances easy to meet (eliminating earlier 60 percent wasted time when condition tolerances could not be held). The next two tests used different tunnels. The Ames 11 ft tunnel was used to test lower Mach numbers of 1.2 and 1.4. There were several difficulties using this tunnel for the first time for sonic boom including having to shift the measurement Mach numbers to 1.15 and 1.3 to avoid flow problems. It is believed that the 11 ft could be used successfully to measure sonic boom but there are likely to be a number of test condition restrictions. The Glenn 8x6 ft tunnel was used next and the tunnel has a number of desirable features for sonic boom measurement. While the Ames 9x7 can only test Mach 1.55 to 2.55 and the 11 ft can only test Mach 1.3 and lower, the Glenn 8x6 can test continuously from Mach 0.3 to 2.0. Unfortunately test measurement accuracy was compromised by a reference pressure drift. Post-test analysis revealed that the drift occurred when Mach number drifted slightly. Test measurements indicated that if Mach number drift is eliminated, results from the 8x6 would be more accurate, especially at longer distances, than results from the 9x7. The fourth test in the 9x7, called LM4, used everything we learned to comprehensively and accurately measure our new 1044-02 configuration with a full-carpet shaped signature design. Productivity was 8 times greater than our Phase 1 LM3 test. Measurement accuracy and repeatability was excellent out to 42 in. However, measurements at greater distances require the rail in the aft position and become substantially less accurate. Further signature processing or measurement improvements are needed for beyond near-field signature validation.

Morgenstern, John↗

NASA NTRS2005

CO/H2 in Translucent Clouds

A thorough examination of techniques to improve the resolution of the FUSE spectrograph was undertaken. These involved co-adding time tagged data very carefully so as to remove any blurs caused by drift. In addition, data was binned by detector pulse height bin (to eliminate any positional vs. gain variations, e.g. "walk"). These techniques only resulted in an extremely modest increase in the spectral resolution, insufficient to allow the CO/H2 studies to be performed. The only remaining potential source of blur was defocus - implying the instrument was never properly focused on orbit. Private discussions with B.G. Anderson of the FUSE team resulted in my learning that this was in fact the case - the slidgrating distance was never optimized on orbit - resulting in a degradation of the peak performance of the instrument. Unfortunately, this was never publicized, even to myself, a member of the instrument tem and the spectrograph designer.

Green, James↗

NASA NTRS2022

A Census of Young Stellar Objects in Two Line-of-Sight Star-Forming Regions Toward IRAS 22147+5948 in the Outer Galaxy

Context. Star formation in the outer Galaxy, namely, outside of the Solar circle, has not been extensively studied in part due to the low CO brightness of the molecular clouds linked with the negative metallicity gradient. Recent infrared surveys provide an overview of dust emission in large sections of the Galaxy, but they suffer from cloud confusion and poor spatial resolution at far-infrared wavelengths. Aims. We aim to develop a methodology to identify and classify young stellar objects (YSOs) in star-forming regions in the outer Galaxy and use it to resolve a long-standing disparity in terms of the distance and evolutionary status of IRAS 22147+5948. Methods. We used a support vector machine learning algorithm to complement standard color–color and color–magnitude diagrams in our search for YSOs in the IRAS 22147 region, based on publicly available data from the Spitzer Mapping of the Outer Galaxy survey. The agglomerative hierarchical clustering algorithm was used to identify clusters. Then the physical properties of individual YSOs were calculated. The distances were determined using CO 1–0 from the Five College Radio Astronomy Observatory survey. Results. We identified 13 Class I and 13 Class II YSO candidates using the color–color diagrams, along with an additional 2 and 21 sources, respectively, using the applied machine learning techniques. The spectral energy distributions of 23 sources were modeled with a star and a passive disk, corresponding to Class II objects. The models of three sources include envelopes that are typical for Class I objects. The objects were grouped into two clusters located at a distance of 2:2 kpc and 5 clusters at 5:6 kpc. The spatial extent of CO, radio continuum, and dust emission confirms the origin of YSOs in two distinct star-forming regions along a similar line of sight. Conclusions. The outer Galaxy may serve as a unique laboratory for exploring star formation across environments, on the condition that complementary methods and ancillary data are used to properly account for cloud confusion and distance uncertainties.

Agata Karska↗

NASA NTRS2007

Learning and Prediction of Slip from Visual Information

This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers.

rover↗

NASA NTRS2006

Look Sharp While Seeing Sharp

The two scientists James B. Stephens and Dr. Charles G. Miller were tasked with studying the harmful properties of light in space, as well as the artificial radiation produced during laser and welding work, for the purpose of creating an enhanced means of eye protection in industrial welding applications. While working to apply their space research to these terrestrial applications, Stephens and Miller became engrossed with previously discovered research showing evidence that the eyes of hawks, eagles, and other birds of prey contain unique oil droplets that actually protect them from intensely radiated light rays (blue, violet, ultraviolet) while allowing vision-enhancing light rays (red, orange, green) to pass through. These oil droplets absorb short wavelength light rays which, in turn, reduce glare and provide heightened color contrast and definition for optimal visual acuity. Accordingly, birds of prey possess the ability to distinguish their targeted prey in natural surroundings and from great distances. Pairing the findings from their initial studies with what they learned from the bird studies, the scientists devised a methodology to incorporate the light-filtering/vision-enhancing dual-action benefits into a filtering system, using light-filtering dyes and tiny particles of zinc oxide. (Zinc oxide, which absorbs ultraviolet light, is also found in sunscreen lotions that protect the skin from sunburn.)

Source record↗

NASA NTRS1996

Gravitational acceleration as a cue for absolute size and distance?

When an object's motion is influenced by gravity, as in the rise and fall of a thrown ball, the vertical component of acceleration is roughly constant at 9.8 m/sec2. In principle, an observer could use this information to estimate the absolute size and distance of the object (Saxberg, 1987a; Watson, Banks, von Hofsten, & Royden, 1992). In five experiments, we examined people's ability to utilize the size and distance information provided by gravitational acceleration. Observers viewed computer simulations of an object rising and falling on a trajectory aligned with the gravitational vector. The simulated objects were balls of different diameters presented across a wide range of simulated distances. Observers were asked to identify the ball that was presented and to estimate its distance. The results showed that observers were much more sensitive to average velocity than to the gravitational acceleration pattern. Likewise, verticality of the motion and visibility of the trajectory's apex had negligible effects on the accuracy of size and distance judgments.

NASA Discipline Space Human Factors↗

NASA NTRS2006

Learning to predict slip for ground robots

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only.

robots↗

NASA NTRS2002

Solving and Learning Soft Temporal Constraints: Experimental Setting and Results

Soft temporal constraints problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. Machine learning techniques can be useful in this respect. In this paper we describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and representational power. Finally, we present a learning module and we show its behavior on randomly-generated examples.

Rossi, F.↗

NASA NTRSDate not supplied

AI-Enhanced Computational Tools for Entry Systems Modeling

To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)

Predictive Modeling↗

NASA NTRS2002

Stirring Up an Elastic Fluid: Critical Viscosity of Xenon-2 (CVX-2)

Whipped cream stays in place even when turned upside down. Yet it readily flows through the nozzle of a spray can to reach the dessert plate. This demonstrates the phenomenon of shear thinning that is important to many industrial and physical processes. Paints, film emulsions, and other complex solutions that are highly viscous under normal conditions but become thin and flow easily under shear forces. A simple fluid, such as water, does not exhibit shear thinning under normal conditions. Very close to the liquid-vapor critical point, where the distinction between liquid and vapor disappears, the fluid becomes more complex and is predicted to display shear thinning. At the critical point, xenon atoms interact over long distances in a classical model of cooperative phenomena. Physicists rely on this system to learn how long-range order arises. The Critical Viscosity of Xenon Experiment (CVX-2) will measure the viscous behavior of xenon, a heavy inert gas used in flash lamps and ion rocket engines, at its critical point. Although it does not easily combine with other chemicals, its viscosity at the critical point can be used as a model for a range of fluids. Viscosity originates from the interactions of individual molecules. It is so complicated that, except for the simplest gas, it cannot be calculated accurately from theory. Tests with critical fluids can provide key data, but are limited on Earth because critical fluids are highly compressed by gravity. CVX-2 employs a tiny metal screen vibrating between two electrodes in a bath of critical xenon. The vibrations and how they dampen are used to measure viscosity. CVX flew on STS-85 (1997), where it revealed that, close to the critical point, the xenon is partly elastic: it can 'stretch' as well as flow. For STS-107, the hardware has been enhanced to determine if critical xenon is a shear-thinning fluid.

Berg, Robert F.↗

NASA NTRS1997

Current Status and Application of Hazard Definition Technology

A research is performed: to define wake non-encounter & hazard, to provide requirements for sensors, and to obtain input from the user community. This research includes: validating wake encounter simulation models, establishing a metric to quantify the upset potential of a wake encounter, applying hazard metric and simulation models to the commercial fleet for development of candidate acceptable encounter limits, and applying technology to near term problems to evaluate current status of technology. The following lessons are learned from this project: technology is not adequate to determine absolute spacing requirements; time, not distance, determines the duration of the wake hazard; Optimum standards depend on the traffic; Wing span is an important parameter for characterizing both generator and follower; and Short span "biz jets" are easily rolled.

Greene, George C.↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

machine learning↗

NASA NTRS2001

Solving and Learning Soft Temporal Constraints: Experimental Scenario and Examples

Soft temporal constraint problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preference use machine learning techniques which learn the local preferences from the global ones. In this paper we describe the existing framework for both solving and learning preferences in temporal constraint problems, the implemented modules, the experimental scenario, and preliminary results on some examples.

Rossi, F.↗

NASA NTRS1992

Adaptive planning for applications with dynamic objectives

We devise a qualitative control layer to be integrated into a real-time multi-agent reactive planner. The reactive planning system consists of distributed planning agents attending to various perspectives of the task environment. Each perspective corresponds to an objective. The set of objectives considered are sometimes in conflict with each other. Each agent receives information about events as they occur, and a set of actions based on heuristics can be taken by the agents. Within the qualitative control scheme, we use a set of qualitative feature vectors to describe the effects of applying actions. A qualitative transition vector is used to denote the qualitative distance between the current state and the target state. We will then apply on-line learning at the qualitative control level to achieve adaptive planning. Our goal is to design a mechanism to refine the heuristics used by the reactive planner every time an action is taken toward achieving the objectives, using feedback from the results of the actions. When the outcome is compared with expectations, our prior objectives may be modified and a new set of objectives (or a new assessment of the relative importance of the different objectives) can be introduced. Because we are able to obtain better estimates of the time-varying objectives, the reactive strategies can be improved and better prediction can be achieved.

Hadavi, Khosrow↗

NASA NTRS2016

The Heliopause Electrostatic Rapid Transit System (HERTS) Design, Trades, and Analyses Performed in the First Year of a Two Year Investigation

The Heliopause Electrostatic Rapid Transit System (HERTS)1 was one of the seven total Phase II NASA Innovative Advanced Concepts (NIAC) that was down-selected in 2015 for continued funding and research. In Phase I we learned that a spacecraft propelled by an Electric Sail (E-Sail) can travel great astronomical distances, such as to the Heliopause region of the solar system (approx.100 to 120 AU) in approximately one quarter of the time (10 years) versus the time it took the Voyager spacecraft launched in 1977 (36 years). The current work within the Phase II NIAC effort builds upon the work that was done in the Phase I NIAC and is focused on: 1) Testing of plasma interaction with a charged wire in a unique MSFC test chamber, 2) Development of a Particle-in-Cell (PIC) models that are validated in the plasma testing and used to extrapolate to the E-Sail propulsion system design. 3) Further down select of a wire deployment and control approach from those narrowed down in the Phase I effort. This paper will document the findings to date (June, 2016) of the above focused areas.

Wiegmann, Bruce M.↗