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At least 649 records · Page 36

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NASA NTRS2016

Air Traffic Controller Acceptability of Unmanned Aircraft System Detect-and-Avoid Thresholds

A human-in-the-loop experiment was conducted with 15 retired air traffic controllers to investigate two research questions: (a) what procedures are appropriate for the use of unmanned aircraft system (UAS) detect-and-avoid systems, and (b) how long in advance of a predicted close encounter should pilots request or execute a separation maneuver. The controller participants managed a busy Oakland air route traffic control sector with mixed commercial/general aviation and manned/UAS traffic, providing separation services, miles-in-trail restrictions and issuing traffic advisories. Controllers filled out post-scenario and post-simulation questionnaires, and metrics were collected on the acceptability of procedural options and temporal thresholds. The states of aircraft were also recorded when controllers issued traffic advisories. Subjective feedback indicated a strong preference for pilots to request maneuvers to remain well clear from intruder aircraft rather than deviate from their IFR clearance. Controllers also reported that maneuvering at 120 seconds until closest point of approach (CPA) was too early; maneuvers executed with less than 90 seconds until CPA were more acceptable. The magnitudes of the requested maneuvers were frequently judged to be too large, indicating a possible discrepancy between the quantitative UAS well clear standard and the one employed subjectively by manned pilots. The ranges between pairs of aircraft and the times to CPA at which traffic advisories were issued were used to construct empirical probability distributions of those metrics. Given these distributions, we propose that UAS pilots wait until an intruder aircraft is approximately 80 seconds to CPA or 6 nmi away before requesting a maneuver, and maneuver immediately if the intruder is within 60 seconds and 4 nmi. These thresholds should make the use of UAS detect and avoid systems compatible with current airspace procedures and controller expectations.

unmanned aircraft systems↗

NASA NTRS2016

Plasma Oscillation Characterization of NASA's HERMeS Hall Thruster via High Speed Imaging

The performance and facility effect characterization tests of NASA's 12.5-kW Hall Effect Rocket with Magnetic Shielding had been completed. As a part of these tests, three plasma oscillation characterization studies were performed to help determine operation settings and quantify margins. The studies included the magnetic field strength variation study, background pressure effect study, and cathode flow fraction study. Separate high-speed videos of the thruster including the cathode and of only the cathode were recorded. Breathing mode at 10-15 kHz and cathode gradient-driven mode at 60-75 kHz were observed. An additional high frequency (40-70 kHz) global oscillation mode with sinusoidal probability distribution function was identified.

Ground Tests↗

NASA NTRS2017

Near Earth Asteroid Characterization for Threat Assessment

Physical characteristics of NEAs are an essential input to modeling behavior during atmospheric entry and to assess the risk of impact but determining these properties requires a non-trivial investment of time and resources. The characteristics relevant to these models include size, density, strength and ablation coefficient. Some of these characteristics cannot be directly measured, but rather must be inferred from related measurements of asteroids and/or meteorites. Furthermore, for the majority of NEAs, only the basic measurements exist so often properties must be inferred from statistics of the population of more completely characterized objects. The Asteroid Threat Assessment Project at NASA Ames Research Center has developed a probabilistic asteroid impact risk (PAIR) model in order to assess the risk of asteroid impact. Our PAIR model and its use to develop probability distributions of impact risk are discussed in other contributions to PDC 2017 (e.g., Mathias et al.). Here we utilize PAIR to investigate which NEA characteristics are important for assessing the impact threat by investigating how changes in these characteristics alter the damage predicted by PAIR. We will also provide an assessment of the current state of knowledge of the NEA characteristics of importance for asteroid threat assessment. The relative importance of different properties as identified using PAIR will be combined with our assessment of the current state of knowledge to identify potential high impact investigations. In addition, we will discuss an ongoing effort to collate the existing measurements of NEA properties of interest to the planetary defense community into a readily accessible database.

Asteroid characterization↗

NASA NTRS2017

Preliminary Analysis of Two Years of the Massive Collision Monitoring Activity

It is hypothesized that the interactions between many of the most massive derelicts in low Earth orbit are more frequent than modeled by the traditional combination of kinetic theory of gases and Poisson probability distribution function. This is suggested by the fact that there are clusters of derelicts where members' inclinations are nearly identical and their apogees/perigees overlap significantly resulting in periodic synchronization of the objects' orbits. In order to address this proposition, an experiment was designed and conducted over the last two years. Results from this monitoring and characterization experiment are presented with implications for proposed debris remediation strategies. Four separate clusters of massive derelicts were examined that are centered around 775km, 850km, 975km, and 1500km, respectively. In aggregate, the constituents of these clusters contain around 500 objects and about 800,000kg of mass; this equates to a third of all derelict mass in LEO. Preliminary analysis indicates that encounter rates over this time period for these objects are greater than is estimated by traditional techniques. Hypothesized dependencies between latitude of encounter, relative velocity, frequency of encounters, inclination, and differential semi-major axis were established and verified. This experiment also identified specific repeatable cluster dynamics that may reduce the cost/risk and enhance the effectiveness of debris remediation activities and also enable new operational debris remediation options.

McKnight, Darren↗

NASA NTRS2010

Analyzing Non Stationary Processes in Radiometers

The lack of well-developed techniques for modeling changing statistical moments in our observations has stymied the application of stochastic process theory for many scientific and engineering applications. Non linear effects of the observation methodology is one of the most perplexing aspects to modeling non stationary processes. This perplexing problem was encountered when modeling the effect of non stationary receiver fluctuations on the performance of radiometer calibration architectures. Existing modeling approaches were found not applicable; particularly problematic is modeling processes across scales over which they begin to exhibit non stationary behavior within the time interval of the calibration algorithm. Alternatively, the radiometer output is modeled as samples from a sequence random variables; the random variables are treated using a conditional probability distribution function conditioned on the use of the variable in the calibration algorithm. This approach of treating a process as a sequence of random variables with non stationary stochastic moments produce sensible predictions of temporal effects of calibration algorithms. To test these model predictions, an experiment using the Millimeter wave Imaging Radiometer (MIR) was conducted. The MIR with its two black body calibration references was configured in a laboratory setting to observe a third ultra-stable reference (CryoTarget). The MIR was programmed to sequentially sample each of the three references in approximately a 1 second cycle. Data were collected over a six-hour interval. The sequence of reference measurements form an ensemble sample set comprised of a series of three reference measurements. Two references are required to estimate the receiver response. A third reference is used to estimate the uncertainty in the estimate. Typically, calibration algorithms are designed to suppress the non stationary effects of receiver fluctuations. By treating the data sequence as an ensemble collection, it is possible to apply temporal algorithms which exacerbate the non stationary effects. By varying the algorithm, information about the properties of the non stationary receiver fluctuations is obtained. Comparisons of analytical calculations and statistical analysis of data demonstrate impressive agreement.

Racette, Paul↗

NASA NTRS2017

Optimization of a Deep Convective Cloud Technique in Evaluating the Long-Term Radiometric Stability of MODIS Reflective Solar Bands

MODIS reflective solar bands are calibrated on-orbit using a solar diffuser and near-monthly lunar observations. To monitor the performance and effectiveness of the on-orbit calibrations, pseudo-invariant targets such as deep convective clouds (DCCs), Libya-4, and Dome-C are used to track the long-term stability of MODIS Level 1B product. However, the current MODIS operational DCC technique (DCCT) simply uses the criteria set for the 0.65- m band. We optimize several critical DCCT parameters including the 11- micrometer IR-band Brightness Temperature (BT11) threshold for DCC identification, DCC core size and uniformity to help locate DCCs at convection centers, data collection time interval, and probability distribution function (PDF) bin increment for each channel. The mode reflectances corresponding to the PDF peaks are utilized as the DCC reflectances. Results show that the BT11 threshold and time interval are most critical for the Short Wave Infrared (SWIR) bands. The Bidirectional Reflectance Distribution Function model is most effective in reducing the DCC anisotropy for the visible channels. The uniformity filters and PDF bin size have minimal impacts on the visible channels and a larger impact on the SWIR bands. The newly optimized DCCT will be used for future evaluation of MODIS on-orbit calibration by MODIS Characterization Support Team.

Mu, Qiaozhen↗

NASA NTRS2018

Stochastic Reduced Order Models with Python (SROMPy)

Stochastic Reduced Order Models with Python (SROMPy) is a software package developed to enable user-friendly utilization of the stochastic reduced order model (SROM) approach for uncertainty quantification. A SROM is a low dimensional, discrete approximation to a random quantity that enables efficient and non-intrusive stochastic computations. With SROMPy, a user can easily generate a SROM to approximate a random variable or vector described by several different types of probability distributions using the Python programming language. Once a SROM is constructed, the software can be used to propagate uncertainty through a user-defined computational model to estimate statistics of a given quantity of interest. This report is meant to introduce the SROMPy module and brie y demonstrate its capabilities. A simple example of a spring-mass system with a random input is included to illustrate the practicality of the SROM approach to uncertainty quantification and relative ease of applying it with SROMPy. The example includes a comparison with a solution obtained using classical Monte Carlo simulation, demonstrating the similarities and advantages of using the SROM approach.

Warner, James E.↗

NASA NTRS2018

Investigating Extreme Events in the NASA GEOS Multiscale Modeling System

Advances in computing capabilities and scientific development have come together to evolve general circulation models into multi-scale Earth system modeling tools. The Goddard Earth Observing System (GEOS) model is one such example of this evolution. The GEOS model is driven by the finite-volume cubed-sphere (FV3) non-hydrostatic dynamical core. Surrounding FV3 is a scale-aware physics package and data assimilation capability permitting multi-scale application of GEOS for sub-seasonal to seasonal climate prediction, medium range weather prediction, and global mesoscale modeling at convection allowing resolutions. GEOS also includes a comprehensive chemistry package representing a range of capabilities from basic chemistry and interactve aerosols and gaseous species, to carbon emissions and uptake, and complex ozone photochemistry. In this study, we apply the GEOS model to study the fidelity of these processes with increasing horizontal resolution and scale-aware processes in GEOS on extreme events. The GEOS model is run for 40-days beginning in August 2016 at three uniform global resolutions of 13-km (c768), 6-km (c1536) and 3-km (c3072) with 72 vertical levels up to 0.01mb. The model physics use the Grell-Freitas scale-aware convection scheme to dynamically reduce the role of parameterized deep convection as resolved scale processes in the model take over at higher resolutions. We will include high-resolution global emissions and fluxes of aerosols and carbon downscaled from recent satellite observations. We will compare these simulations with reanalyses and observations, focusing on rainfall, clouds and radiative forcing at hourly to monthly timescales. We will closely examine the probability distribution of precipitation intensities and radiative properties of clouds on a daily time scale. In addition, we will focus on extreme events, in particular the diurnal cycle of convection over the US and the frequency and physical nature of organized convection and heavy rain events across the globe.

Putman, William↗

NASA NTRS2018

Dynamic Adjustment of Simulation Parameters for Efficient Vehicle Prognostics

Model-based diagnostics and prognostics rely on state estimation and uncertainty management algorithms to produce useful information for system operators and maintainers. This information enables more informed operational decisions, condition-based maintenance, and overall mission safety assurance. Typically, uncertainty is associated with vehicle state-of-health estimation and prediction results because of modeling errors, internal or external sources of noise, and sensor inaccuracy. Probabilistic uncertainty management methods including Sequential Monte Carlo simulation are commonly used to reason about state-of-health estimates and predictions in the presence of these sources of uncertainty. However, such algorithms can be computationally expensive as they require a very large number of samples to obtain a sufficiently accurate quantification of the end of life probability distribution. As a result, highly mobile autonomous systems that leverage the prognostic results for mission-level replanning are often constrained in their processing capability because of these computationally expensive simulation approaches. Therefore, in this paper, we investigate algorithmic methods for dynamically adjusting simulation time step as well as number of samples to achieve highly efficient prognostic results while maintaining results accuracy. Results obtained from simulated flight experiments of an electric unmanned aerial vehicle are presented to verify the efficacy of such algorithms.

Prognostics↗

NASA NTRS2018

Multidecadal Changes in Lower Stratospheric Ozone: Variability Vs. Trends

As upper stratospheric ozone appears to be recovering as a result of decreasing chlorine loading following the implementation of the Montreal Protocol and its amendments and in agreement with model projections, several recent studies report an apparent decline of ozone concentrations in the lower stratosphere in the last two decades, particularly in the extratropics. Our previous work as well as at least two other studies provide evidence that this decline results from transport changes rather than an intensification of chemical depletion. It remains unclear whether these changes represent long-term internal variability or are a consequence of a climate forcing. Here we perform free-running ensembles of the recent past (1980-2016) using the Goddard Earth Observing System Model (GEOS) at the cubed sphere C180 (approximately half degree) resolution. Two suites of 10-member ensembles are performed, one in which observed sea surface temperature (SSTs) are fully prescribed, and the other in which the linear SST trend over the recent past is removed so as to only retain internal variability. We evaluate the trends in both ozone as well as two idealized tracers with prescribed uniform loss that are used to isolate the role of transport from chemistry and emissions. Probability-distribution-functions of the trends in both ozone and idealized tracers are compared among ensemble members and with observed trends in order to evaluate the likelihood of recent observed declines in lower stratospheric ozone, relative to large internal variability. Moreover, comparisons among simulations with and without imposed SST trends indicate the extent to which dynamically-driven ozone trends reflect forced trends or internal variability in lower stratospheric dynamics.

Wargan, Krzysztof↗

NASA NTRS2019

On the Maximum Expected Electric Field in Electrically Small, Undermoded Enclosures

This paper reports the experimental validation of an improved statistical model for the prediction of maximum expected electric field in electrically small and under-moded enclosures. The aerospace community is interested in application of Hill's statistical models to design of avionics boxes for shielding effectiveness and for tailoring EMC test requirements for critical applications. However, it is observed that the probability distribution for mean-squared electric field in an electrically small enclosure differs from the exponential distribution which is widely used in reverberation chamber testing. It is postulated here that the difference is attributable to the under-moded character of the small enclosure.

Bremner, Paul↗

NASA NTRS2019

On the Maximum Expected Electric Field in Electrically Small, Undermoded Enclosures

This paper reports the experimental validation of an improved statistical model for the prediction of maximum expected electric field in electrically small and under-moded enclosures. The aerospace community is interested in application of Hill’s statistical models to design of avionics boxes for shielding effectiveness and for tailoring EMC test requirements for critical applications. However, it is observed that the probability distribution for mean-squared electric field (|E(sub x)|(exp 2)) in an electrically small enclosure differs from the exponential distribution which is widely used in reverberation chamber testing. It is postulated here that the difference is attributable to the under-moded character of the small enclosure. We will define “under-moded” as the condition where a single excitation frequency does not excite enough closely spaced resonant modes to achieve Hill’s assumption of an isotropic (or fully diffuse) plane wave field in the enclosure.

Bremner, P. G.↗

NASA NTRS2017

A Model-Based, Bayesian Solution for Characterization of Complex Damage Scenarios in Aerospace Composite Structures

Ultrasonic damage detection and characterization is commonly used in nondestructive evaluation (NDE) of aerospace composite components. In recent years there has been an increased development of guided wave based methods. In real materials and structures, these dispersive waves result in complicated behavior in the presence of complex damage scenarios. Model-based characterization methods utilize accurate three dimensional finite element models (FEMs) of guided wave interaction with realistic damage scenarios to aid in defect identification and classification. This work describes an inverse solution for realistic composite damage characterization by comparing the wavenumber-frequency spectra of experimental and simulated ultrasonic inspections. The composite laminate material properties are first verified through a Bayesian solution (Markov chain Monte Carlo), enabling uncertainty quantification surrounding the characterization. A study is undertaken to assess the efficacy of the proposed damage model and comparative metrics between the experimental and simulated output. The FEM is then parameterized with a damage model capable of describing the typical complex damage created by impact events in composites. The damage is characterized through a transdimensional Markov chain Monte Carlo solution, enabling a flexible damage model capable of adapting to the complex damage geometry investigated here. The posterior probability distributions of the individual delamination petals as well as the overall envelope of the damage site are determined.

H. Reed↗

NASA NTRS2017

Optimal Transport Based Control of Granular Imaging System in Space

"Orbiting Rainbows” is a paradigm for creating a space-based observatory (telescope) from confined and aligned granular media. The overall objective is to construct a very large and lightweight aperture in space using a cloud of micron-sized particles held in position and aligned by the application of external electromagnetic fields, thus alleviating the extreme expense of deploying and stabilizing large monolithic telescopes by reducing the mass of the primary aperture. Adaptive optics techniques for wavefront stabilization and computational imaging techniques are then used to remove the optical noise from the scattering reflections of the granular medium, and finally producing clean images within the diffraction limit. In this paper, we address one of the key challenges of this new paradigm, namely controlling the position and orientation of the granules or particles so that the cloud of particles achieves the desired shape of an ultra-lightweight telescope and points into the right orientation. A further complication arises due to the fact that the particles do not have any onboard actuators. A number of external electric-field-based actuators are used to control all the particles simultaneously. Moreover, the number of actuators are orders-of-magnitude smaller than the number of particles, making this system largely under-actuated. Since the cloud of particles in any plausible configuration can be modeled as a probability distribution over the workspace, we formulate this control challenge as an optimal transport problem and then show that our optimal transport based control law drives the cloud of particles from any initial distribution to the desired position distribution (optical surface) and angular distribution (polarization state). Simulation results demonstrate the effectiveness of our approach.

Bandyopadhyay, Saptarshi↗

NASA NTRS2019

On Expected Value Strong Controllability

The Probabilistic Simple Temporal Network (PSTN) generalizes Simple Temporal Networks with Uncertainty (STNUs) by introducing probability distributions over the timing of uncontrollable timepoints. PSTNs are controllable if there is a strategy to execute the controllable timepoints while bounding the risk of violating any constraint to a small value. If this risk bound can't be satisfied, PSTNs are not considered controllable. We introduce the Expected Value Probabilistic SimpleTemporal Network (EPSTN), which extends PSTNs by including a benefit to the satisfaction of temporal constraints. We study the problem of Expected Value Strong Controllability (EvSC) of EPSTNs, which seeks a schedule maximizing the expected value of satisfied constraints. We solve the EvSC problem by extending a previously developed linear program, combined with search over constraints to violate at execution time. We describe conditions under which the solution to this linear program is the maximum expected value schedule. We then show how to search for constraints to discard, using the linear program at the core of the search. While the general problem is shown to be exponential, we conclude by providing several methods to bound the complexity of search.

Planning↗

NASA NTRS2016

A Multi-Sensor Water Vapor, Temperature and Cloud Climate Data Record: Status of Our MEaSUREs 2012 Project

- Our primary objectives: Incorporate all A-Train water vapor, including small-scale, horizontal and vertical water vapor structure from MODIS (Moderate Resolution Imaging Spectroradiometer) and GPS (Global Positioning System); Use MODIS cloud classification that is collocated at the pixel-scale over the full AIRS/AMSU (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit) swath; Extend summaries to PDFs (Probability Distribution Functions)/higher order moments of water vapor sorted by cloud classes; Establish robust, scale-dependent statistical relationships between cloud, temperature, and water vapor PDFs for the climate modeling community. - We will also: Update our current data record to new CloudSat/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification; Provide merged AIRS and MLS (Microwave Limb Sounder) water vapor profiles using averaging kernels; Include temperature and water vapor from models (ECMWF - European Centre for Medium-Range Weather Forecasts) and re-analyses (MERRA - Modern Era-Retrospective Analysis for Research and Applications); Classify METOP-A (Meteorological Operational Satellite-A) and NOAA (National Oceanic and Atmospheric Administration) satellite water vapor with AVHRR (Advanced Very High Resolution Radiometer) during SNOs (Solar Neutrino Oscillations); Compare with the NVAP-M (re-analysis and extension of the NASA Water Vapor Project (NVAP)) water vapor climatologies; Use existing data sources and mature algorithms, and document processing algorithms.

Fetzer, Eric↗

NASA NTRS2019

Modeling Deicing Operations in Departure Scheduling Using Fast Time Simulation

In winter snow conditions, aircraft need inspection for deicing service before takeoff. Deicing service is a procedure to remove frost, ice, slush, or snow from aircraft for safe operation. Deicing operations vary by airport in many ways. Some airports have designated deicing zones, whereas some use a closed runway or terminal area to perform the procedure. Nonetheless, deicing operations add extra workloads to controllers, and cause increased taxi traffic on the ground. NASA and Korea Aerospace Research Institute (KARI) have been collaborating to model deicing operations at Incheon International Airport (ICN). This paper describes the deicing model and the study of deicing operations in departure scheduling using fast time simulations. The deicing model uses a heuristic algorithm for deicing zone assignment. In the fast time simulations, the model uses probability distributions derived from actual operation data to model deicing request and deicing zone time. It is envisioned that such a deicing model can be useful in airport surface scheduling to provide decision support and improve traffic management performance in winter snow operations.

modeling and simulation↗

NASA NTRS2019

Modeling Deicing Operations in Departure Scheduling using Fast Time Simulation

surface operation↗