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NASA NTRS2012

Offset Compound Gear Inline Two-Speed Drive

A two-speed transmission having an input shaft and an output shaft, the transmission being capable of transitioning between fixed ratios, the high-range ratio being direct 1:1 and the low-range ratio being about 2:1. The transmission is a simple lightweight, yet robust, configuration utilizing only two gear meshes, being comprised of an input gear, a cluster gear, and an output gear. The transmission is controlled with a clutch and a sprag and with the input and output shafts turning in the same direction.

Stevens, Mark A.↗

NASA NTRS2014

Offset Compound Gear Inline Two-Speed Drive

Stevens, Mark A.↗

NASA NTRS2004

Functional Groups Based on Leaf Physiology: Are they Spatially and Temporally Robust?

The functional grouping hypothesis, which suggests that complexity in ecosystem function can be simplified by grouping species with similar responses, was tested in the Florida scrub habitat. Functional groups were identified based on how species in fire maintained Florida scrub regulate exchange of carbon and water with the atmosphere as indicated by both instantaneous gas exchange measurements and integrated measures of function (%N, delta C-13, delta N-15, C-N ratio). Using cluster analysis, five distinct physiologically-based functional groups were identified in the fire maintained scrub. These functional groups were tested to determine if they were robust spatially, temporally, and with management regime. Analysis of Similarities (ANOSIM), a non-parametric multivariate analysis, indicated that these five physiologically-based groupings were not altered by plot differences (R = -0.115, p = 0.893) or by the three different management regimes; prescribed burn, mechanically treated and burn, and fire-suppressed (R = 0.018, p = 0.349). The physiological groupings also remained robust between the two climatically different years 1999 and 2000 (R = -0.027, p = 0.725). Easy-to-measure morphological characteristics indicating functional groups would be more practical for scaling and modeling ecosystem processes than detailed gas-exchange measurements, therefore we tested a variety of morphological characteristics as functional indicators. A combination of non-parametric multivariate techniques (Hierarchical cluster analysis, non-metric Multi-Dimensional Scaling, and ANOSIM) were used to compare the ability of life form, leaf thickness, and specific leaf area classifications to identify the physiologically-based functional groups. Life form classifications (ANOSIM; R = 0.629, p 0.001) were able to depict the physiological groupings more adequately than either specific leaf area (ANOSIM; R = 0.426, p = 0.001) or leaf thickness (ANOSIM; R 0.344, p 0.001). The ability of life forms to depict the physiological groupings was improved by separating the parasitic Ximenia americana from the shrub category (ANOSIM; R = 0.794, p = 0.001). Therefore, a life form classification including parasites was determined to be a good indicator of the physiological processes of scrub species, and would be a useful method of grouping for scaling physiological processes to the ecosystem level.

Foster, Tammy E.↗

NASA NTRS1995

The coupling of fluids, dynamics, and controls on advanced architecture computers

This grant provided for the demonstration of coupled controls, body dynamics, and fluids computations in a workstation cluster environment; and an investigation of the impact of peer-peer communication on flow solver performance and robustness. The findings of these investigations were documented in the conference articles.The attached publication, 'Towards Distributed Fluids/Controls Simulations', documents the solution and scaling of the coupled Navier-Stokes, Euler rigid-body dynamics, and state feedback control equations for a two-dimensional canard-wing. The poor scaling shown was due to serialized grid connectivity computation and Ethernet bandwidth limits. The scaling of a peer-to-peer communication flow code on an IBM SP-2 was also shown. The scaling of the code on the switched fabric-linked nodes was good, with a 2.4 percent loss due to communication of intergrid boundary point information. The code performance on 30 worker nodes was 1.7 (mu)s/point/iteration, or a factor of three over a Cray C-90 head. The attached paper, 'Nonlinear Fluid Computations in a Distributed Environment', documents the effect of several computational rate enhancing methods on convergence. For the cases shown, the highest throughput was achieved using boundary updates at each step, with the manager process performing communication tasks only. Constrained domain decomposition of the implicit fluid equations did not degrade the convergence rate or final solution. The scaling of a coupled body/fluid dynamics problem on an Ethernet-linked cluster was also shown.

Atwood, Christopher↗

NASA NTRS2014

The Evolution of the Galaxy Rest-Frame Ultraviolet Luminosity Function Over the First Two Billion Years

We present a robust measurement and analysis of the rest-frame ultraviolet (UV) luminosity function at z = 4 to 8. We use deep Hubble Space Telescope imaging over the CANDELS/GOODS fields, the Hubble Ultra Deep Field and the Hubble Frontier Field deep parallel observations near the Abell 2744 and MACS J0416.1- 2403 clusters. The combination of these surveys provides an effective volume of 0.6-1.2 ×10(exp 6) Mpc(exp 3) over this epoch, allowing us to perform a robust search for bright (M(sub UV) less than −21) and faint (M(sub UV) = −18) galaxies. We select galaxies using a well-tested photometric redshift technique with careful screening of contaminants, finding a sample of 7446 galaxies at 3.5 less than z less than 8.5, with more than 1000 galaxies at z of approximately 6 - 8. We measure both a stepwise luminosity function for galaxies in our redshift samples, as well as a Schechter function, using a Markov Chain Monte Carlo analysis to measure robust uncertainties. At the faint end our UV luminosity functions agree with previous studies, yet we find a higher abundance of UV-bright galaxies at z of greater than or equal to 6. Our bestfit value of the characteristic magnitude M* is consistent with −21 at z of greater than or equal to 5, different than that inferred based on previous trends at lower redshift. At z = 8, a single power-law provides an equally good fit to the UV luminosity function, while at z = 6 and 7, an exponential cutoff at the bright-end is moderately preferred. We compare our luminosity functions to semi-analytical models, and find that the lack of evolution in M* is consistent with models where the impact of dust attenuation on the bright-end of the luminosity function decreases at higher redshift, though a decreasing impact of feedback may also be possible. We measure the evolution of the cosmic star-formation rate (SFR) density by integrating our observed luminosity functions to M(sub UV) = −17, correcting for dust attenuation, and find that the SFR density declines proportionally to (1 + z)((exp −4.3)(+/-)(0.5)) at z greater than 4, consistent with observations at z greater than or equal to 9. Our observed luminosity functions are consistent with a reionization history that starts at redshift of approximately greater than 10, completes at z greater than 6, and reaches a midpoint (x(sub HII) = 0.5) at 6.7 less than z less than 9.4. Finally, using a constant cumulative number density selection and an empirically derived rising star-formation history, our observations predict that the abundance of bright z = 9 galaxies is likely higher than previous constraints, though consistent with recent estimates of bright z similar to 10 galaxies.

Galaxy↗

NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors Mounted on Trash-Trucks: Methods Development and Lessons Learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Drive-by sensing↗

NASA NTRS2020

Air Quality Monitoring Case Study Using Mobile Low-cost Sensors mounted on Trash-Trucks: Methods Development and Lessons Learned

Drive-by sensing↗

NASA NTRS2023

Design of a Minimal di-Nickel Hydrogenase Peptide

The most ancient processes for energy production in the evolution of life involve the reversible oxidation of molecular hydrogen by hydrogenase. Extant hydrogenase enzymes are complex, comprising hundreds of amino acids and multiple cofactors. We designed a 13 amino acid nickel-binding peptide capable of robustly producing molecular hydrogen from protons under a wide variety of conditions. The peptide forms a di-nickel cluster structurally analogous to a Ni-Fe cluster in [NiFe]-hydrogenase and the Ni-Ni cluster in acetyl-CoA synthase (ACS), two ancient, extant proteins central to metabolism. These experimental results clearly demonstrate that modern enzymes, despite their enormous complexity, likely evolved from simple peptide precursors on early Earth. One Sentence Summary: Small metal-binding peptides were the likely precursors of modern enzymes.

Joshua A. Mancini↗

NASA NTRS1991

A quantitative measure of phase correlations in density fields

A quantitative measure of the phase correlations in a density field is presented based on the location of the maxima of the Fourier components of that field. It is found that this measue can easily detect non-Gaussian behavior either in artificially constructed density fields or those that become non-Gaussian from gravitational clustering of Gaussian initial conditions. It is found that different initial power spectra produce somewhat distinguishable signals, and the signals are robust against sparse sampling.

Scherrer, Robert J.↗

NASA NTRS2023

Drought and Deluge: the Recurrence of Hydroclimate Extremes During the Past 600 Years in Eastern Australia’s Natural Resource Management (NRM) Clusters

Recent extremes of flood and drought across Australia have raised questions about the recurrence of such rare events and highlighted the importance of understanding multi-decadal climate variability. However, instrumental records over the past century are too short to adequately characterise climate variability on multi-decadal and longer timescales or robustly determine extreme event frequencies and their duration. Palaeoclimate reconstructions can provide much-needed information to help address this problem. Here, we use the 600-year hydroclimate record captured in the eastern Australian and New Zealand Drought Atlas (ANZDA) to analyse drought and pluvial frequency trends for East Australian Natural Resource Management (NRM) clusters. This partitioning of the drought atlas grid points into recognised biophysical areas (i.e. NRM clusters) revealed their differences and similarities in drought intensity and pluvial events over time. We find sustained multi-decadal periods of a wet–dry geographic 'seesaw' between eastern to central and southern NRMs (e.g. 1550–1600 CE and 1700–1750 CE). In contrast, other periods reveal spatially consistent wetting (e.g. 1500–1550 CE) or drying (e.g. 1750–1800 CE). Emerging hot spot analysis further shows that some areas that appear naturally buffered from severe drought during the instrumental period have a greater exposure risk when the longer 600-year record is considered. These findings are particularly relevant to management plans when dealing with the impacts of climate extremes developed at regional scales. Our results demonstrate that integrating and extending instrumental records with palaeoclimate datasets will become increasingly important for developing robust and locally specific extreme event frequency information for managing water resources.

Australian hydroclimate↗

NASA NTRS2005

Decentralized Formation Flying Control in a Multiple-Team Hierarchy

This paper presents the prototype of a system that addresses these objectives-a decentralized guidance and control system that is distributed across spacecraft using a multiple-team framework. The objective is to divide large clusters into teams of manageable size, so that the communication and computational demands driven by N decentralized units are related to the number of satellites in a team rather than the entire cluster. The system is designed to provide a high-level of autonomy, to support clusters with large numbers of satellites, to enable the number of spacecraft in the cluster to change post-launch, and to provide for on-orbit software modification. The distributed guidance and control system will be implemented in an object-oriented style using MANTA (Messaging Architecture for Networking and Threaded Applications). In this architecture, tasks may be remotely added, removed or replaced post-launch to increase mission flexibility and robustness. This built-in adaptability will allow software modifications to be made on-orbit in a robust manner. The prototype system, which is implemented in MATLAB, emulates the object-oriented and message-passing features of the MANTA software. In this paper, the multiple-team organization of the cluster is described, and the modular software architecture is presented. The relative dynamics in eccentric reference orbits is reviewed, and families of periodic, relative trajectories are identified, expressed as sets of static geometric parameters. The guidance law design is presented, and an example reconfiguration scenario is used to illustrate the distributed process of assigning geometric goals to the cluster. Next, a decentralized maneuver planning approach is presented that utilizes linear-programming methods to enact reconfiguration and coarse formation keeping maneuvers. Finally, a method for performing online collision avoidance is discussed, and an example is provided to gauge its performance.

Mueller, Joseph .↗

NASA NTRS2000

Using Model-Based Reasoning for Autonomous Instrument Operation

Multiprobe missions are an important part of NASA's future: Cluster, Magnetospheric Multi Scale, Global Electrodynamics and Magnetospheric Constellation are representatives from the Sun-Earth Connections Theme. To make such missions robust, reliable, and affordable, ideally the many spacecraft of a constellation must be at least as easy to operate as one spacecraft is today. To support this need for scalability, science instrumentation must become increasingly easy to operate, even as this same instrumentation becomes more capable and advanced. Communication and control resources will be at a premium for future instruments. Many missions will be out of contact with ground operators for extended periods either to reduce operations cost or because of orbits that limit communication to weekly perigee transits. Autonomous capability is necessary if such missions are to effectively achieve their operational objectives. An autonomous system is one that acts given its situation in a mission appropriate manner without external direction to achieve mission goals. To achieve this capability autonomy must be built into the system through judicious design or through a built-in intelligence that recognizes system state and manages system response. To recognize desired or undesired system states, the system must have an implicit or explicit understanding of its expected states given its history and self observations. The systems we are concerned with, science instruments, can have stringent requirements for system state knowledge in addition to requirements driven by health and safety concerns. Without accurate knowledge of the system state, the usefulness of the science instrument may be severely limited. At the same time, health and safety concerns often lead to overly conservative instrument operations further reducing the effectiveness of the instrument. These requirements, coupled with overall mission requirements including lack of communication opportunities and tolerance of environmental hazards, frame the problem of constructing autonomous science instruments. we are developing a model of the Low Energy Neutral Atom instrument (LENA) that is currently flying on board the Imager for Magnetosphere-to-Aurora Global Exploration (IMAGE) spacecraft. LENA is a particle detector that uses high voltage electrostatic optics and time-of-flight mass spectrometry to image neutral atom emissions from the denser regions of the Earth's magnetosphere. As with most spacecraft borne science instruments, phenomena in addition to neutral atoms are detected by LENA. Solar radiation and energetic particles from Earth's radiation belts are of particular concern because they may help generate currents that may compromise LENA's long term performance. An explicit model of the instrument response has been constructed and is currently in use on board IMAGE to dynamically adapt LENA to the presence or absence of energetic background radiations. The components of LENA are common in space science instrumentation, and lessons learned by modelling this system may be applied to other instruments. This work demonstrates that a model-based approach can be used to enhance science instrument effectiveness. Our future work involves the extension of these methods to cover more aspects of LENA operation and the generalization to other space science instrumentation.

Johnson, Mike↗

NASA NTRS2013

Automatic Data Filter Customization Using a Genetic Algorithm

This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.

Mandrake, Lukas↗

NASA NTRS2002

Functional Groups Based on Leaf Physiology: Are they Spatially and Temporally Robust?

The functional grouping hypothesis, which suggests that complexity in function can be simplified by grouping species with similar responses, was tested in the Florida scrub habitat. Functional groups were identified based on how species in fire maintained FL scrub function in terms of carbon, water and nitrogen dynamics. The suite of physiologic parameters measured to determine function included both instantaneous gas exchange measurements obtained from photosynthetic light response curves and integrated measures of function. Using cluster analysis, five distinct physiologically-based functional groups were identified. Using non-parametric multivariate analyses, it was determined that these five groupings were not altered by plot differences or by the three different management regimes; prescribed burn, mechanically treated and burn, and fire-suppressed. The physiological groupings also remained robust between the two years 1999 and 2000. In order for these groupings to be of use for scaling ecosystem processes, there needs to be an easy-to-measure morphological indicator of function. Life form classifications were able to depict the physiological groupings more adequately than either specific leaf area or leaf thickness. THe ability of life forms to depict the groupings was improved by separating the parasitic Ximenia americana from the shrub category.

Foster, Tammy E.↗

NASA NTRS2016

The Efficiency of Magnetic Field Amplification at Shocks by Turbulence

Turbulent dynamo field amplification has often been invoked to explain the strong field strengths in thin rims in supernova shocks (approx.100 micrograms) and in radio relics in galaxy clusters (approx. micrograms). We present high-resolution magnetohydrodynamic simulations of the interaction between pre-shock turbulence, clumping and shocks, to quantify the conditions under which turbulent dynamo amplification can be significant. We demonstrate numerically converged field amplification which scales with Alfven Mach number, B/B0 varies as MA, up to MA approx.150.This implies that the post-shock field strength is relatively independent of the seed field. Amplification is dominated by compression at low MA, and stretching (turbulent amplification) at high MA. For high MA, the B-field grows exponentially and saturates at equipartition with turbulence, while the vorticity jumps sharply at the shock and subsequently decays; the resulting field is orientated predominately along the shock normal (an effect only apparent in 3D and not 2D). This agrees with the radial field bias seen in supernova remnants. By contrast, for low MA, field amplification is mostly compressional, relatively modest, and results in a predominantly perpendicular field. The latter is consistent with the polarization seen in radio relics. Our results are relatively robust to the assumed level of gas clumping. Our results imply that the turbulent dynamo may be important for supernovae, but is only consistent with the field strength, and not geometry, for cluster radio relics. For the latter, this implies strong pre-existing B-fields in the ambient cluster outskirts.

lementary particles â€“ magnetic fields â€“ radiat↗

NASA NTRS2016

Landslides in West Coast Metropolitan Areas: The Role of Extreme Weather Events

Rainfall-induced landslides represent a pervasive issue in areas where extreme rainfall intersects complex terrain. A farsighted management of landslide risk requires assessing how landslide hazard will change in coming decades and thus requires, inter alia, that we understand what rainfall events are most likely to trigger landslides and how global warming will affect the frequency of such weather events. We take advantage of 9 years of landslide occurrence data compiled by collating Google news reports and of a high-resolution satellite-based daily rainfall data to investigate what weather triggers landslide along the West Coast US. We show that, while this landslide compilation cannot provide consistent and widespread monitoring everywhere, it captures enough of the events in the major urban areas that it can be used to identify the relevant relationships between landslides and rainfall events in Puget Sound, the Bay Area, and greater Los Angeles. In all these regions, days that recorded landslides have rainfall distributions that are skewed away from dry and low-rainfall accumulations and towards heavy intensities. However, large daily accumulation is the main driver of enhanced hazard of landslides only in Puget Sound. There, landslide are often clustered in space and time and major events are primarily driven by synoptic scale variability, namely "atmospheric rivers" of high humidity air hitting anywhere along the West Coast, and the interaction of frontal system with the coastal orography. The relationship between landslide occurrences and daily rainfall is less robust in California, where antecedent precipitation (in the case of the Bay area) and the peak intensity of localized downpours at sub-daily time scales (in the case of Los Angeles) are key factors not captured by the same-day accumulations. Accordingly, we suggest that the assessment of future changes in landslide hazard for the entire the West Coast requires consideration of future changes in the occurrence and intensity of atmospheric rivers, in their duration and clustering, and in the occurrence of short-duration (sub-daily) extreme rainfall as well. Major regional landslide events, in which multiple occurrences are recorded in the catalog for the same day, are too rare to allow a statistical characterization of their triggering events, but a case study analysis indicates that a variety of synoptic-scale events can be involved, including not only atmospheric rivers but also broader cold- and warm-front precipitation. That a news-based catalog of landslides is accurate enough to allow the identification of different landslide/ rainfall relationships in the major urban areas along the US West Coast suggests that this technology can potentially be used for other English-language cities and could become an even more powerful tool if expanded to other languages and non-traditional news sources, such as social media.

landslides↗

NASA NTRS1994

Fuzzy logic, neural networks, and soft computing

The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

Zadeh, Lofti A.↗

NASA NTRS1991

Fusion of monocular cues to detect man-made structures in aerial imagery

The extraction of buildings from aerial imagery is a complex problem for automated computer vision. It requires locating regions in a scene that possess properties distinguishing them as man-made objects as opposed to naturally occurring terrain features. It is reasonable to assume that no single detection method can correctly delineate or verify buildings in every scene. A cooperative-methods paradigm is useful in approaching the building extraction problem. Using this paradigm, each extraction technique provides information which can be added or assimilated into an overall interpretation of the scene. Thus, the main objective is to explore the development of computer vision system that integrates the results of various scene analysis techniques into an accurate and robust interpretation of the underlying three dimensional scene. The problem of building hypothesis fusion in aerial imagery is discussed. Building extraction techniques are briefly surveyed, including four building extraction, verification, and clustering systems. A method for fusing the symbolic data generated by these systems is described, and applied to monocular image and stereo image data sets. Evaluation methods for the fusion results are described, and the fusion results are analyzed using these methods.

Shufelt, Jefferey↗