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At least 1,279 records · Page 71
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NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2010

Multi-agent autonomous system

A multi-agent autonomous system for exploration of hazardous or inaccessible locations. The multi-agent autonomous system includes simple surface-based agents or craft controlled by an airborne tracking and command system. The airborne tracking and command system includes an instrument suite used to image an operational area and any craft deployed within the operational area. The image data is used to identify the craft, targets for exploration, and obstacles in the operational area. The tracking and command system determines paths for the surface-based craft using the identified targets and obstacles and commands the craft using simple movement commands to move through the operational area to the targets while avoiding the obstacles. Each craft includes its own instrument suite to collect information about the operational area that is transmitted back to the tracking and command system. The tracking and command system may be further coupled to a satellite system to provide additional image information about the operational area and provide operational and location commands to the tracking and command system.

Fink, Wolfgang↗
NASA NTRS2011

Consequences of the Ion Cyclotron Instability in the Inner Magnetospheric Plasma

The inner magnetospheric plasma is a very unique composition of different plasma particles and waves. Among these plasma particles and waves are Ring Current (RC) particles and Electromagnetic Ion Cyclotron (EMIC) waves. The RC is the source of free energy for the EMIC wave excitation provided by a temperature anisotropy of RC ions, which develops naturally during inward E x B convection from the plasma sheet. The cold plasmasphere, which is under the strong influence of the magnetospheric electric field, strongly mediates the RC-EMIC waves-coupling process, and ultimately becomes part of the particle and energy interplay, generated by the ion cyclotron instability of the inner magnetosphere. On the other hand, there is a strong influence of the RC on the inner magnetospheric electric and magnetic field configurations and these configurations, in turn, are important to RC dynamics. Therefore, one of the biggest needs for inner magnetospheric plasma physics research is the continued progression toward a coupled, interconnected system, with the inclusion of nonlinear feedback mechanisms between the plasma populations, the electric and magnetic fields, and plasma waves.

Khazanov, George V.↗
NASA NTRS2006

Expectation-Based Control of Noise and Chaos

A proposed approach to control of noise and chaos in dynamic systems would supplement conventional methods. The approach is based on fictitious forces composed of expectations governed by Fokker-Planck or Liouville equations that describe the evolution of the probability densities of the controlled parameters. These forces would be utilized as feedback control forces that would suppress the undesired diffusion of the controlled parameters. Examples of dynamic systems in which the approach is expected to prove beneficial include spacecraft, electronic systems, and coupled lasers.

Zak, Michael↗
NASA NTRS2004

NASA Tech Briefs, October 2004

Topics include: Relative-Motion Sensors and Actuators for Two Optical Tables; Improved Position Sensor for Feedback Control of Levitation; Compact Tactile Sensors for Robot Fingers; Improved Ion-Channel Biosensors; Suspended-Patch Antenna With Inverted, EM-Coupled Feed; System Would Predictively Preempt Traffic Lights for Emergency Vehicles; Optical Position Encoders for High or Low Temperatures; Inter-Valence-Subband/Conduction-Band-Transport IR Detectors; Additional Drive Circuitry for Piezoelectric Screw Motors; Software for Use with Optoelectronic Measuring Tool; Coordinating Shared Activities; Software Reduces Radio-Interference Effects in Radar Data; Using Iron to Treat Chlorohydrocarbon-Contaminated Soil; Thermally Insulating, Kinematic Tensioned-Fiber Suspension; Back Actuators for Segmented Mirrors and Other Applications; Mechanism for Self-Reacted Friction Stir Welding; Lightweight Exoskeletons with Controllable Actuators; Miniature Robotic Submarine for Exploring Harsh Environments; Electron-Spin Filters Based on the Rashba Effect; Diffusion-Cooled Tantalum Hot-Electron Bolometer Mixers; Tunable Optical True-Time Delay Devices Would Exploit EIT; Fast Query-Optimized Kernel-Machine Classification; Indentured Parts List Maintenance and Part Assembly Capture Tool - IMPACT; An Architecture for Controlling Multiple Robots; Progress in Fabrication of Rocket Combustion Chambers by VPS; CHEM-Based Self-Deploying Spacecraft Radar Antennas; Scalable Multiprocessor for High-Speed Computing in Space; and Simple Systems for Detecting Spacecraft Meteoroid Punctures.

Source record↗
NASA NTRS2014

Multi-Objective Hybrid Optimal Control for Interplanetary Mission Planning

Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. Because low-thrust trajectory design is tightly coupled with systems design, power and propulsion characteristics must be chosen as well. In addition, a time-history of control variables must be chosen which defines the trajectory. There are often may thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The method is demonstrated on hypothetical mission to the main asteroid belt and to Deimos.

Low Thrust↗
NASA NTRS2014

Forcing of the Coupled Ionosphere-Thermosphere (IT) System During Magnetic Storms

Poynting flux shows peaks around auroral zone AND inside polar cap. Energy enters IT system at all local times in polar cap. Track-integrated flux at DMSP often peaks at polar latitudes- probably due to increased area of polar cap during storm main phases. center dot lon temperatures at DMSP show large increases in polar region at all local times; cusp and auroral zones do not show distinctively high Ti. center dot I on temperatures in the polar cap are higher than in the auroral zones during quiet times. center dot Neutral densities at GRACE and GOCE show maxima at polar latitudes without clear auroral signatures. Response is fast, minutes from onset to density peaks. center dot GUVI observations of O/N2 ratio during storms show similar response as direct measurements of ion and neutral densities, i.e. high temperatures in polar cap during prestorm quiet period, heating proceeding from polar cap to lower latitudes during storm main phase. center dot Discrepancy between maps of Poynting flux and of ion temperatures/neutral densities suggests that connection between Poynting flux and Joule heating is not simple.

Magnetic Storms↗
NASA NTRS2015

Multi-Objective Hybrid Optimal Control for Interplanetary Mission Planning

Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. Because low-thrust trajectory design is tightly coupled with systems design, power and propulsion characteristics must be chosen as well. In addition, a time-history of control variables must be chosen which defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The methods is demonstrated on hypothetical mission to the main asteroid belt and to Deimos.

Trajectory Design↗
NASA NTRS2015

Modeling Landslide Susceptibility Over Large Regions with Fuzzy Overlay

Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas.

Landslide↗
NASA NTRS2015

Structured Overlapping Grid Simulations of Contra-rotating Open Rotor Noise

Computational simulations using structured overlapping grids with the Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework are presented for predicting tonal noise generated by a contra-rotating open rotor (CROR) propulsion system. A coupled Computational Fluid Dynamics (CFD) and Computational AeroAcoustics (CAA) numerical approach is applied. Three-dimensional time-accurate hybrid Reynolds Averaged Navier-Stokes/Large Eddy Simulation (RANS/LES) CFD simulations are performed in the inertial frame, including dynamic moving grids, using a higher-order accurate finite difference discretization on structured overlapping grids. A higher-order accurate free-stream preserving metric discretization with discrete enforcement of the Geometric Conservation Law (GCL) on moving curvilinear grids is used to create an accurate, efficient, and stable numerical scheme. The aeroacoustic analysis is based on a permeable surface Ffowcs Williams-Hawkings (FW-H) approach, evaluated in the frequency domain. A time-step sensitivity study was performed using only the forward row of blades to determine an adequate time-step. The numerical approach is validated against existing wind tunnel measurements.

Grid↗
NASA NTRS2017

WCRP Task Team for the Intercomparison of Reanalyses (TIRA): Motivation and Progress

Reanalyses have proven to be an important resource for weather and climate related research, as well as societal applications at large. Several centers have emerged to produce new atmospheric reanalyses in various forms every few years. In addition, land and ocean communities are producing disciplinary uncoupled reanalyses. Current research and development in reanalysis is directed at (1) extending the length of reanalyzed period and (2) use of coupled Earth system models for climate reanalysis. While WCRPs involvement in the reanalyses communities through its Data Advisory Council (WDAC) has been substantial, for example in organizing international conferences on reanalyses, a central team of reanalyses expertise is not in place in the WCRP structure. The differences among reanalyses and their inherent uncertainties are some of the most important questions for both users and developers of reanalyses. Therefore, a collaborative effort to systematically assess and intercompare reanalyses would be a logical progression that fills the needs of the community and contributes to the WCRP mission. The primary charge to the TIRA is to develop a reanalysis intercomparison project plan that will attain the following objectives.1)To foster understanding and estimation of uncertainties in reanalysis data by intercomparison and other means 2)To communicate new developments and best practices among the reanalyses producing centers 3)To enhance the understanding of data and assimilation issues and their impact on uncertainties, leading to improved reanalyses for climate assessment 4)To communicate the strengths and weaknesses of reanalyses, their fitness for purpose, and best practices in the use of reanalysis datasets by the scientific community. This presentation outlines the need for a task team on reanalyses, their intercomparison, the objectives of the team and progress thus far.

WDA↗
NASA NTRS2017

Overview of NASA GRC Electrified Aircraft Propulsion Systems Analysis Methods

The accurate modeling and analysis of electrified aircraft propulsion concepts require intricate subsystem system component coupling. The major challenge in electrified aircraft propulsion concept modeling lies in understanding how the subsystems "talk" to each other and the dependencies they have on one another.

Schnulo, Sydney↗
NASA NTRS2018

New Neural Network Cloud Mask Algorithm Based on Radiative Transfer Simulations

Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Comparedto threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.

cloud mask algorithms↗
NASA NTRS2018

SCExAO/CHARIS Near-IR High-Contrast Imaging and Integral Field Spectroscopy of the HIP 79977 Debris Disk

We present new, near-infrared (1.1–2.4 μm) high-contrast imaging of the bright debris disk surrounding HIP 79977 with the Subaru Coronagraphic Extreme Adaptive Optics system (SCExAO) coupled with the CHARIS integral field spectrograph. SCExAO/CHARIS resolves the disk down to smaller angular separations of (0".11; r ∼ 14 au) and at a higher significance than previously achieved at the same wavelengths. The disk exhibits a marginally significant east–west brightness asymmetry in H band that requires confirmation. Geometrical modeling suggests a nearly edge-on disk viewed at a position angle of ∼114°.6 east of north. The disk is best-fit by scattered-light models assuming strongly forward-scattering grains (g ∼ 0.5–0.65) confined to a torus with a peak density at r0 ∼ 53–75 au. We find that a shallow outer density power law of α(sub out) = −1 to −3 and flare index of β = 1 are preferred. Other disk parameters (e.g., inner density power law and vertical scale height) are more poorly constrained. The disk has a slightly blue intrinsic color and its profile is broadly consistent with predictions from birth ring models applied to other debris disks. While HIP 79977ʼs disk appears to be more strongly forward- scattering than most resolved disks surrounding 5–30 Myr old stars, this difference may be due to observational biases favoring forward-scattering models for inclined disks versus lower inclination, ostensibly neutral-scattering disks like HR 4796A’s. Deeper, higher signal-to-noise SCExAO/CHARIS data can better constrain the disk’s dust composition.

Goebel, Sean↗
NASA NTRS2019

Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses

Satellite soil moisture observations are assimilated into NASA's Global Earth Observing System using a weakly coupled land/atmosphere data assimilation system. Specifically, Advanced Scatterometer and Soil Moisture Ocean Salinity soil moisture retrievals are assimilated with an Ensemble Kalman Filter into a system that uses the same model, atmospheric assimilation system (3DVar), and atmospheric observations as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The first set of experiments investigate the impact of the soil moisture assimilation on the model states and fluxes within the cycling DA system. Comparison to independent ground-based observations from global networks of soil moisture, latent and sensible heating, and 2m temperature and specific humidity showed small, but positive, improvements in the global mean statistics. Additionally, in some regions, the soil moisture assimilation induced more substantial improvements. For all variables evaluated, the soil moisture assimilation improved the model at monthly to seasonal, rather than daily, time scales. Based on the above experiments, it is recommended that satellite soil moisture be assimilated into future reanalyses, including the follow-on to MERRA-2.

Draper, Clara↗
NASA NTRS2019

El Niño Related Tropical Land Surface Water and Energy Response in MERRA-2

The El Niño Southern Oscillation (ENSO) is a coupled Earth system circulation phenomena that reaches all around the globe. The heat added to the atmosphere by increased precipitation produces circulation changes that have global reach and, over time, warms the entire tropical band, and much of the Earth. Many studies have noted that El Niño causes warm and dry (and sometimes drought) conditions over tropical land masses. We develop a composite analysis of El Niño to identify the predominate features of tropical land response. This analysis shows that the land’s lagged response is related to a reduction clouds that leads to increase surface shortwave radiation that increases the surface temperature. The precipitation lag is somewhat longer, and then leads to a reduction in soil water and, in concert with increased SW induced surface warming, leads to increased sensible heating of the atmosphere above. The M2AMIP simulation generally captures these features, but the response is strongest with increased temporal and spatial proximity to the El Niño peak warming. The regionality of these features is also discussed, and it is noted that even the strongest individual El Niño events can vary from this composite mechanistic paradigm.

Bosilovich, Michael G.↗
NASA NTRS2022

Earth System Model Parameter Adjustment Using a Green's Functions Approach

We demonstrate the practicality and effectiveness of using a Green's functions estimation approach for adjusting uncertain parameters in an Earth system model (ESM). This estimation approach has previously been applied to an intermediate-complexity climate model and to individual ESM components, e.g., ocean, sea ice, or carbon cycle components. Here, the Green's functions approach is applied to a state-of-the-art ESM that comprises a global atmosphere/land configuration of the Goddard Earth Observing System (GEOS) coupled to an ocean and sea ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). Horizontal grid spacing is approximately 110 km for GEOS and 37–110 km for MITgcm. In addition to the reference GEOS-MITgcm simulation, we carried out a series of model sensitivity experiments, in which 20 uncertain parameters are perturbed. These “control” parameters can be used to adjust sea ice, microphysics, turbulence, radiation, and surface schemes in the coupled simulation. We defined eight observational targets: sea ice fraction, net surface shortwave radiation, downward longwave radiation, near-surface temperature, sea surface temperature, sea surface salinity, and ocean temperature and salinity at 300 m. We applied the Green's functions approach to optimize the values of the 20 control parameters so as to minimize a weighted least-squares distance between the model and the eight observational targets. The new experiment with the optimized parameters resulted in a total cost reduction of 9 % relative to a simulation that had already been adjusted using other methods. The optimized experiment attained a balanced cost reduction over most of the observational targets. We also report on results from a set of sensitivity experiments that are not used in the final optimized simulation but helped explore options and guided the optimization process. These experiments include an assessment of sensitivity to the number of control parameters and to the selection of observational targets and weights in the cost function. Based on these sensitivity experiments, we selected a specific definition for the cost function. The sensitivity experiments also revealed a decreasing overall cost as the number of control variables was increased. In summary, we recommend using the Green's functions estimation approach as an additional fine-tuning step in the model development process. The method is not a replacement for modelers' experience in choosing and adjusting sensitive model parameters. Instead, it is an additional practical and effective tool for carrying out final adjustments of uncertain ESM parameters.

Green's Function↗
NASA NTRSDate not supplied

Inner Magnetospheric Physics

A brief overview of inner magnetospheric physics will be given. As a discipline, magnetospheric physics is a young science. Its earliest experimental beginnings were in the 1950s with the study of low frequency radio waves originating from lightning and later with the first orbiting satellite, Explorer 1. The solar wind drives the coupled magnetospheric system from the ionosphere-thermosphere, plasmasphere, ring current, radiation belts, out through the region of interface to interplanetary space, the magnetosheath and bow shock. The basic plasma systems, their motions, and responses to the driving solar wind will be mentioned. These highlights of the magnetospheric system are intended only to provide points of reference for further more in-depth study.

inner magnetosphere, physical processes↗