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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33

Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics

The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.

Statistics↗

Local starburst galaxies and their descendants. Statistics from the Sloan Digital Sky Survey

Despite strong interest in the starburst phenomenon in extragalactic astronomy, the concept remains ill-defined. Here we use a strict definition of starburst to examine the statistical properties of starburst galaxies in the local universe. We also seek to establish links between starburst galaxies, post-starburst (hereafter postburst) galaxies, and active galaxies. Data were selected from the Sloan Digital Sky Survey DR7. We applied a novel method of treating dust attenuation and derive star formation rates, ages, and stellar masses assuming a two-component stellar population model. Dynamical masses are calculated from the width of the H-alpha line. These masses agree excellently with the photometric masses. The mass (gas+stars) range is approximately 10( exp 9) - 10(exp 11.5) solar mass. As a selection criterion for starburst galaxies, we use, the birthrate parameter, b = SFR/SFR, requiring that b is greater than 3. For postburst galaxies, we use, the equivalent width of Hdelta in absorption with the criterion EW (sub Hdelta_abs) is greater than 6 A. Results. We find that only 1% of star-forming galaxies are starburst galaxies. They contribute 3-6% to the stellar production and are therefore unimportant for the local star formation activity. The median starburst age is 70 Myr roughly independent of mass, indicating that star formation is mainly regulated by local feedback processes. The b-parameter strongly depends on burst age. Values close to b = 60 are found at ages approximately 10 Myr, while almost no starbursts are found at ages greater than 1 Gyr. The median baryonic burst mass fraction of sub-L galaxies is 5% and decreases slowly towards high masses. The median mass fraction of the recent burst in the postburst sample is 5-10%. A smaller fraction of the postburst galaxies, however, originates in non-bursting galaxies. The age-mass distribution of the postburst progenitors (with mass fractions is greater than 3%) is bimodal with a break at logM(solar mass ) 10.6, above which the ages are doubled. The starburst and postburst luminosity functions (LFs) follow each other closely until M(sub r ) (is) approximately -21, when active galactic nuclei (AGNs) begin to dominate. The postburst LF continues to follow the AGN LF, while starbursts become less significant. This suggests that the number of luminous starbursts is underestimated by about one dex at high luminosities, because of having large amounts of dust and/or being outshone by an AGN. It also indicates that the starburst phase preceded the AGN phase. Finally, we look at the conditions for global gas outflow caused by stellar feedback and find that massive starburst galaxies are susceptible to such outflows.

Statistical distributions↗

A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics

Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.

Deep Neural networks↗

A Framework for Software Health Management Using Bayesian Statistics

Software Health Management (SWHM) must make sure that the software always remains in safe and healthy regions of the state space. Boundaries between healthy and unhealthy regions are important for the detection of violations and health management.In this position paper, we present a framework, which employs techniques from Bayesian statistical modeling and active learning to efficiently characterize health boundaries in high-dimensional spaces. We will discuss, how this framework supports SWHM during design time and during operation of learning/adapting software systems

software health management↗

Development of TID Hardness Assurance Methodologies to Capitalize on Statistical Radiation Environment Models

We develop methods for bounding part-to-part variation in TID data. When used in conjunction with statistical radiation environment models, these methods allow development of RHA that deliver estimated piece part reliability for any desired confidence level, moving beyond risk avoidance methodologies based on radiation design margin. Two methods are developed, and their results compared for realistic data.

Total Ionizing Dose↗

Development of TID Hardness Assurance Methodologies to Capitalize on Statistical Radiation Environment Models

We develop methods for bounding part-to-part variation in TID data. When used in conjunction with statistical radiation environment models, these methods allow development of RHA that deliver estimated piece part reliability for any desired confidence level, moving beyond risk avoidance methodologies based on radiation design margin. Two methods are developed, and their results compared for realistic data.

Total ionizing dose↗

Earth-Space Links and Fade-Duration Statistics

The available data on fade-duration statistics are reviewed, with emphasis on mobile and fixed satellite links. Fades in mobile links are due to roadside-tree shadowing, and fades in fixed links are due to rain attenuation.

fade-duration↗

Physical and statistical modeling of Saturn's troposphere

We analyze the 5.2-pm spectra of Saturn by utilizing two independent methods: (a) physical models based on the relevant atmospheric parameters and (b) statistical analysis, based on principal components analysis (PCA), to determine the influence of the variation of phosphine and the opacity of clouds deep within Saturn's atmosphere to understand the dynamics in its atmosphere.

Saturn↗

Statistical Process Control and Analysis on the Water Content Measurements in NASA Glenn’s Icing Research Tunnel

The Icing Research Tunnel at NASA Glenn follows the recommended practice for calibration outlined in SAE’s ARP5905. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within the stated specifications of variation within +/- 10% of the curve fit equation generated from calibration data. Using past measurements and data trends, IRT characterization engineers wanted to develop methods for the ability to know when data were not within variation. Trends can be observed in the liquid water content measurement process by constructing statistical process control charts. This paper describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, process for rejection of data, and construction of control charts. Data are presented to display the control capability to meet defined liquid water content specifications of the IRT with the Multi-Element Sensor mounted in the center of the test section.

Statistical Process Control↗

Statistical Process Control and Analysis on the Water Content Measurements in NASA Glenn’s Icing Research Tunnel

The Icing Research Tunnel at NASA Glenn follows the recommended practice for calibration outlined in SAE’s ARP5905. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within the stated specifications of variation within +/- 10% of the curve fit equation generated from calibration data. Using past measurements and data trends, IRT characterization engineers wanted to develop methods for the ability to know when data were not within variation. Trends can be observed in the liquid water content measurement process by constructing statistical process control charts. This paper describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, process for rejection of data, and construction of control charts. Data are presented to display the control capability to meet defined liquid water content specifications of the IRT with the Multi-Element Sensor mounted in the center of the test section.

Statistical Process Control↗

Launch Vehicle Design and Requirements Verification Using Statistical Methods

- This presentation applies statistics to launch vehicle design, but the methods may be used for other engineering applications - Vehicle Models: What do we know when? How to separate parameters for making Monte Carlo runs - Known when assembling the vehicle but not during design - Known prior to committing to flight - Unknown at lift off - How do we correctly model the vehicle during the design phases? - Number of Monte Carlo samples - Requirements success - Design parameter values

Monte Carlo↗