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All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Aeroacoustics: Acoustic wave propagation; Aircraft noise prediction; Aeroacoustic instrumentation

The papers in this volume deal with recent research into acoustic-wave propagation through the atmosphere and progress in aeroacoustic instrumentation, facilities, and test techniques. Topics include the propagation of aircraft noise over long distances in the lower atmosphere, measured effects of turbulence on the rise time of a weak shock, sound scattering from atmospheric turbulence, saturation effects associated with sound propagation in a turbulent medium, and a computer model of the lightning-thunder process. Other papers discuss the development of a computer system for aircraft noise prediction; aircraft flyover noise measurements; and theories and methods for the prediction of ground effects on aircraft noise propagation, for the prediction of airframe aerodynamic noise, for turbine noise prediction, and for combustion noise prediction. Attention is also given to the use of Hartmann generators as sources of high-intensity sound in a large absorption flow-duct facility, an outdoor jet noise facility, factors in the design and performance of free-jet acoustic wind tunnels, and the use of a laser shadowgraph for jet noise diagnosis.

Schwartz, I. R.↗
NASA NTRS2007

Toxicity of Carbon Nanotubes and Its Implications for Occupational and Environmental Health

Carbon nanotubes (CNTs), which possess desirable electrical and mechanical properties, potentially have wide industrial applications. CNTs exist in two forms, single-wall (SW) and multi-wall (MW). There has been great concern that if CNTs enter the work environment as suspended respirable particulate matter (PM), they could pose an inhalation hazard. The results of recent rodent studies have collectively shown that CNTs can produce inflammation, epithelioid granulomas, fibrosis, and biochemical changes in the lungs. Studies in mice given equal amounts of test dusts showed that CNTs were more toxic than quartz and produced lesions that became progressively more pronounced. These results have led us to recommend that respirable CNT dust be considered a serious occupational health hazard, and that exposure limits be established in the expectation of expanded industrial applications. CNTs, which are totally insoluble and fibrous, would be expected to be more biopersistent than mineral fibers. Biopersistence is the key factor determining the long-term toxicity of mineral fibers and certainly of CNTs too. We have postulated that the electrical and fibrous properties of CNTs also play important roles in the toxicity of CNTs in the lungs. Recently, MWCNTs have been found in ultrafine PM aggregates in combustion streams of methane, propane, and natural-gas flames of typical stoves; indoor and outdoor fine (< 2.5 micron) PM samples were reported to contain significant fractions of MWCNTs. Environmental fine PM is mainly formed from combustion of fuels, and fine PM has been reported to be a major contributor to the induction of cardiopulmonary diseases by pollutants. Given that manufactured SWCNTs and/or MWCNTs have elicited pathological changes in the lungs and heart, we have postulated that exposure to combustion-generated MWCNTs in fine PM in the air may play a significant role in air pollution-related cardiopulmonary diseases. Therefore, CNTs from manufacturing and combustion sources in the environment could have adverse effects on human health.

Lam, Chiu-wing↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRS1994

Subjective response to sonic booms having different shapes, rise times, and durations

Two laboratory experiments were conducted to quantify the subjective response of people to simulated outdoor sonic booms having different pressure signatures. The specific objectives of the experiments were to compare subjective response to sonic booms when described in terms of 'loudness' and 'annoyance'; to determine the ability of various noise metrics to predict subjective response to sonic booms; to determine the effects on subjective response of rise time, duration, and level; and to compare the subjective response to 'N-wave' sonic boom signatures with the subjective response to 'minimized' sonic boom signatures. The experiments were conducted in a computer-controlled, man-rated sonic boom simulator capable of reproducing user-specified pressure signatures for a wide range of sonic boom parameters. One hundred and fifty sonic booms representing different combinations of two wave shapes, four rise times, seven durations, and three peak overpressures were presented to 36 test subjects in each experiment. The test subjects in the first experiment made judgments of 'loudness' while the test subjects in the second experiment judged 'annoyance.' Subjective response to sonic booms was the same whether expressed in terms of loudness or in terms of annoyance. Analyses of several different noise metrics indicated that A-weighted sound exposure level and Perceived Level were the best predictors of subjective response. Further analyses indicated that, of these two noise metrics, only Perceived Level completely accounted for the effects of wave shape, rise time, and peak overpressure. Neither metric fully accounted for the effect of duration. However, the magnitude of the duration effect was small over the very wide range of durations considered.

Mccurdy, David A.↗
NASA NTRSDate not supplied

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗
NASA NTRS2005

Water and Air Measures That Make 'PureSense'

Each day, we read about mounting global concerns regarding the ability to sustain supplies of clean water and to reduce air contamination. With water and air serving as life s most vital elements, it is important to know when these environmental necessities may be contaminated, in order to eliminate exposure immediately. The ability to respond requires an understanding of the conditions impacting safety and quality, from source to tap for water, and from outdoor to indoor environments for air. Unfortunately, the "time-to-know" is not immediate with many current technologies, which is a major problem, given the greater likelihood of risky situations in today s world. Accelerating alert and response times requires new tools, methods, and technologies. New solutions are needed to engage in more rapid detection, analysis, and response. This is the focus of a company called PureSense Environmental, Inc., which evolved out of a unique relationship with NASA. The need for real-time management and operations over the quality of water and air, and the urgency to provide new solutions, were reinforced by the events of September 11, 2001. This, and subsequent events, exposed many of the vulnerabilities facing the multiple agencies tasked with working in tandem to protect communities from harmful disaster. Much has been done since September 11 to accelerate responses to environmental contamination. Partnerships were forged across the public and private sectors to explore, test, and use new tools. Methods and technologies were adopted to move more astutely from proof-of-concept to working solutions.

Source record↗
NASA NTRSDate not supplied

Operational Evaluation of NASA SPoRT Lightning Safety Applications for Impact Based Decision Support Services

The NWS Office in Huntsville is tasked with monitoring and predicting the threat for lightning within its County Warning Area. These impact-based decision support services (IDSS) are provided routinely for aviation operations, irregularly for large-scale, outdoor events, and can have varying safety requirements, such as proximity to location and duration. Lightning monitoring and prediction can require the rapid synthesis of a plethora of data, so products that make this process more efficient and effective are sought by the operational community. The Huntsville NWS Office benefits from close collaboration with the NASA SPoRT Center, which is developing several products to address these operational challenges in concentrated R2O/O2R efforts. One example of this is the StopLight product, which uses Geostationary Lightning Mapper (GLM) flash extent density (FED) to provide an easy-to-interpret visual aid of lightning occurring within the last 30 minutes. The StopLight product shows both the age and location of the last lightning flash within each GLM pixel, which can be particularly useful for IDSS with determining when to resume activities or operations that have been shut down due to lightning. This presentation highlights results of collaboration between NASA SPoRT and NWS Huntsville, including testing of the Stoplight product, and additional experimental products in development. A brief summary of the lightning products are provided while the main focus will discuss product evaluation and forecaster perspective during real-time weather watch activities to support aviation operations and events where IDSS was necessary.

Kristopher White↗
NASA NTRS1975

Study of noise sources in a subsonic fan using measured blade pressures and acoustic theory

Sources of noise in a 1.4 m (4.6 ft) diameter subsonic tip speed propulsive fan running statically outdoors are studied using a combination of techniques. Signals measured with pressure transducers on a rotor blade are plotted in a format showing the space-time history of inlet distortion. Study of these plots visually and with statistical correlation analysis confirms that the inlet flow contains long, thin eddies of turbulence. Turbulence generated in the boundary layer of the shroud upstream of the rotor tips was not found to be an important noise source. Fan noise is diagnosed by computing narrowband spectra of rotor and stator sound power and comparing these with measured sound power spectra. Rotor noise is computed from spectra of the measured blade pressures and stator noise is computed using the author's stator noise theory. It is concluded that the rotor and stator sources contribute about equally at frequencies in the vicinity of the first three harmonics of blade passing frequency. At higher frequencies, the stator contribution diminishes rapidly and the rotor/inlet turbulence mechanism dominates. Two parametric studies are performed by using the rotor noise calculation procedure which was correlated with test. In the first study, the effects on noise spectrum and directivity are calculated for changes in turbulence properties, rotational Mach number, number of blades, and stagger angle. In the second study the influences of design tip speed and blade number on noise are evaluated.

Hanson, D. B.↗
NASA NTRS2008

Sub-Surface Windscreen for the Measurement of Outdoor Infrasound

A windscreen has been developed that features two advantages favorable for the measurement of outdoor infrasound. First, the sub-surface location, with the top of the windscreen flush with the ground surface, minimizes the mean velocity of the impinging wind. Secondly, the windscreen material (closed cell polyurethane foam) has a sufficiently low acoustic impedance (222 times that of air) and wall thickness (0.0127 m) to provide a transmission coefficient of nearly unity over the infrasonic frequency range (0-20 Hz). The windscreen, a tightly-sealed box having internal dimensions of 0.3048 x 0.3048 x 0.3556 m, contains a microphone, preamplifier, and a cable feed thru to an external power supply. Provisions are made for rain drainage and seismic isolation. A three-element array, configured as an equilateral triangle with 30.48 m spacing and operating continuously in the field, periodically receives highly coherent signals attributed to emissions from atmospheric turbulence. The time delays between infrasonic signals received at the microphones permit determination of the bearing and elevation of the sources, which correlate well with locations of pilot reports (PIREPS) within a 320 km radius about the array. The test results are interpreted to yield spectral information on infrasonic emissions from clear air turbulence.

Shams, Qamar A.↗