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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Probabilistic Causal Analysis for System Safety Risk Assessments in Commercial Air Transport

Aviation is one of the critical modes of our national transportation system. As such, it is essential that new technologies be continually developed to ensure that a safe mode of transportation becomes even safer in the future. The NASA Aviation Safety Program (AvSP) is managing the development of new technologies and interventions aimed at reducing the fatal aviation accident rate by a factor of 5 by year 2007 and by a factor of 10 by year 2022. A portfolio assessment is currently being conducted to determine the projected impact that the new technologies and/or interventions may have on reducing aviation safety system risk. This paper reports on advanced risk analytics that combine the use of a human error taxonomy, probabilistic Bayesian Belief Networks, and case-based scenarios to assess a relative risk intensity metric. A sample case is used for illustrative purposes.

Luxhoj, James T.↗
NASA NTRS2012

Radar Waveform Pulse Analysis Measurement System for High-Power GaN Amplifiers

This work presents a measurement system to characterize the pulsed response of high-power GaN amplifiers for use in space-based SAR platforms that require very strict amplitude and phase stability. The measurement system is able to record and analyze data on three different time scales: fast, slow, and long, which allows for greater detail of the mechanisms that impact amplitude and phase stability. The system is fully automated through MATLAB, which offers both instrument control capability and in-situ data processing. To validate this system, a high-power GaN HEMT amplifier operated in saturation was characterized. The fast time results show that variations to the amplitude and phase are correlated to DC supply transients, while long time characteristics are correlated to temperature changes.

high-power amplifier (HPA)↗
NASA NTRS2016

Development and Calibration of a Field-Deployable Microphone Phased Array for Propulsion and Airframe Noise Flyover Measurements

A new aeroacoustic measurement capability has been developed consisting of a large channelcount, field-deployable microphone phased array suitable for airframe noise flyover measurements for a range of aircraft types and scales. The array incorporates up to 185 hardened, weather-resistant sensors suitable for outdoor use. A custom 4-mA current loop receiver circuit with temperature compensation was developed to power the sensors over extended cable lengths with minimal degradation of the signal to noise ratio and frequency response. Extensive laboratory calibrations and environmental testing of the sensors were conducted to verify the design's performance specifications. A compact data system combining sensor power, signal conditioning, and digitization was assembled for use with the array. Complementing the data system is a robust analysis system capable of near real-time presentation of beamformed and deconvolved contour plots and integrated spectra obtained from array data acquired during flyover passes. Additional instrumentation systems needed to process the array data were also assembled. These include a commercial weather station and a video monitoring / recording system. A detailed mock-up of the instrumentation suite (phased array, weather station, and data processor) was performed in the NASA Langley Acoustic Development Laboratory to vet the system performance. The first deployment of the system occurred at Finnegan Airfield at Fort A.P. Hill where the array was utilized to measure the vehicle noise from a number of sUAS (small Unmanned Aerial System) aircraft. A unique in-situ calibration method for the array microphones using a hovering aerial sound source was attempted for the first time during the deployment.

Humphreys, William M., Jr.↗
NASA NTRS2016

Isokinetic TWC Evaporator Probe: Calculations and Systemic Uncertainty Analysis

A new Isokinetic Total Water Content Evaporator (IKP2) was downsized from a prototype instrument, specifically to make airborne measurements of hydrometeor total water content (TWC) in deep tropical convective clouds to assess the new ice crystal Appendix D icing envelope. The probe underwent numerous laboratory and wind tunnel investigations to ensure reliable operation under the difficult high altitude/speed/TWC conditions under which other TWC instruments have been known to either fail, or have unknown performance characteristics and the results are presented in a companion paper (Ref. 1). This paper presents the equations used to determine the total water content (TWC) of the sampled atmosphere from the values measured by the IKP2 or necessary ancillary data from other instruments. The uncertainty in the final TWC is determined by propagating the uncertainty in the measured values through the calculations to the final result. Two techniques were used and the results compared. The first is a typical analytical method of propagating uncertainty and the second performs a Monte Carlo simulation. The results are very similar with differences that are insignificant for practical purposes. The uncertainty is between 2 and 3 percent at most practical operating conditions. The capture efficiency of the IKP2 was also examined based on a computational fluid dynamic simulation of the original IKP and scaled down to the IKP2. Particles above 24 micrometers were found to have a capture efficiency greater than 99 percent at all operating conditions.

Meteorological Instrument↗
NASA NTRS2016

Isokinetic TWC Evaporator Probe: Calculations and Systemic Uncertainty Analysis

A new Isokinetic Total Water Content Evaporator (IKP2) was downsized from a prototype instrument, specifically to make airborne measurements of hydrometeor total water content (TWC) in deep tropical convective clouds to assess the new ice crystal Appendix D icing envelope. The probe underwent numerous laboratory and wind tunnel investigations to ensure reliable operation under the difficult high altitude/speed/TWC conditions under which other TWC instruments have been known to either fail, or have unknown performance characteristics and the results are presented in a companion paper. This paper presents the equations used to determine the total water content (TWC) of the sampled atmosphere from the values measured by the IKP2 or necessary ancillary data from other instruments. The uncertainty in the final TWC is determined by propagating the uncertainty in the measured values through the calculations to the final result. Two techniques were used and the results compared. The first is a typical analytical method of propagating uncertainty and the second performs a Monte Carlo simulation. The results are very similar with differences that are insignificant for practical purposes. The uncertainty is between 2 percent and 3 percent at most practical operating conditions. The capture efficiency of the IKP2 was also examined based on a computational fluid dynamic simulation of the original IKP and scaled down to the IKP2. Particles above 24 microns were found to have a capture efficiency greater than 99 percent at all operating conditions.

Aircraft Icing↗
NASA NTRS2017

Material Analysis and System Design for Exploration Life Support Systems 2017

Advanced Environmental Control and Life Support System (ECLSS) design is critical for manned space flight beyond Earth. Current systems enable extended missions in low-Earth orbit, but for deep-space missions, not only will astronauts be outside the reach of resupply operations from Earth but they will also need to handle malfunctions and compensate for the degradation of materials. These two daunting challenges must be overcome for long-term independent space flight. In order to solve the first, separation and recycling of onboard atmosphere is required. Current systems utilize space vacuum to fully regenerate CO2 sorbent beds, but this is not sustainable. The second challenge stems from material and performance degradation due to operational cycling and on-board contaminants. This report will review the recent work by the ECLSS team at Marshall Space Flight Center towards overcoming these challenges by characterizing materials via novel methods and by assessing new air revitalization systems.

Knox, Jim↗
NASA NTRS2015

SARDA HITL #6 Simulation: System Performance Analysis (Initial Results)

This presentation discusses the results of the 6th human-in-the-loop simulation of the Spot and Runway Departure Advisor (SARDA). SARDA is a research prototype decision support tool for both Air Traffic Control Tower controllers and airlines ramp controllers to assit in controlling surface traffic of a busy airport. Data was collected during the human-in-the-loop simulations of SARDA for ramp operatoins at Charlotte International Airport (Oct-Nov 2014). The performance results include taxi time, taxi delay, total delay, and runway usage.

surface management↗
NASA NTRS2017

Gaining the most utility from our geospace observational system: Network analysis of total electron content as a means to understand space weather to the point of prediction

We present the first network analysis of interplanetary magnetic field (IMF) clock angle dependent, high-latitude, hemispheric-specific total electron content (TEC) data. We examine network parameters to describe spatio-temporal correlations in the TEC data for January 2016. We find that significant network structure exists distinguishing the dayside and nightside ionosphere, and specific features in the high-latitudes (cusp/ionospheric footpoints of magnetospheric boundary layers, polar cap, and auroral zone), and that these features vary with IMF clock angle. In this brief summary paper, we provide proof of concept results and identify important areas of future research, providing a basis for the discussion of network analysis and machine learning approaches for space weather applications.

Malik, Nishant↗