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At least 37 records · Page 2

Theory and Design Tools For Studies of Reactions to Abrupt Changes in Noise Exposure

Study plans, a pre-tested questionnaire, a sample design evaluation tool, a community publicity monitoring plan, and a theoretical framework have been developed to support combined social/acoustical surveys of residents' reactions to an abrupt change in environmental noise, Secondary analyses of more than 20 previous surveys provide estimates of three parameters of a study simulation model; within individual variability, between study wave variability, and between neighborhood variability in response to community noise. The simulation model predicts the precision of the results from social surveys of reactions to noise, including changes in noise. When the study simulation model analyzed the population distribution, noise exposure environments and feasible noise measurement program at a proposed noise change survey site, it was concluded that the site could not yield sufficient precise estimates of human reaction model to justify conducting a survey. Additional secondary analyses determined that noise reactions are affected by the season of the social survey.

Fields, James M.↗

Attitudinal Responses to Changes in Noise Exposure in Residential Communities

The purpose of this study is (1) to investigate the current body of knowledge encompassing two related topics: (a) to what extent can we reliably predict the change in people's attitudes in response to an abrupt change in noise exposure, and (b) after the change, is there a decay in the abrupt-change effect whereby people's attitudes slowly shift from their initial reaction to a steady-state value? and (2) to provide recommendations for any future work that may be needed. The literature search located 23 studies relating to one or both of the above topics. These prior studies shed considerable light on the current ability to predict initial reaction and decay effects. The literature makes one point very clear: Great care in both experimental design and data analysis is necessary to produce credible, convincing findings, both in the reanalysis of existing data and for planning future data acquisition and analysis studies. New airport studies must be designed to minimize nuisance variables and avoid past design features that may have introduced sufficient unexplained variance to mask sought after effects. Additionally, the study must be designed to tie in with previous investigations by incorporating similar survey questions and techniques.

Horonjeff, Richard D.↗

Historical development of noise exposure metrics

Some of the historical events that led to the introduction of night penalties are reported with emphasis on what happens with different kinds of day/night operations when night pentalties are employed. These effects are considered in terms of the difference between a nighttime weighting cumulative measure of noise exposure verses simply not using any night weighting at all, in decibles.

Galloway, W. J.↗

Effects of Meteorological Conditions on Reactions to Noise Exposure

More than 80,000 residents' responses to transportation noise at different times of year provide the best, but imprecise, statistical estimates of the effects of season and meteorological conditions on community response to noise. Annoyance with noise is found to be slightly statistically significantly higher in the summer than in the winter in a seven-year study in the Netherlands. Analyses of 41 other surveys drawn from diverse countries, climates, and times of year find noise annoyance is increased by temperature, and may be increased by more sunshine, less precipitation, and reduced wind speeds. Meteorological conditions on the day of the interview or the immediately preceding days do not appear to have any more effect on reactions than do the conditions over the immediately preceding weeks or months.

Shepherd, Kevin P.↗

Recommendations for Using Noise Monitors to Estimate Noise Exposure During X-59 Community Tests

A low fidelity simulation approach is used to explore how to place and use noise monitors during X-59 QueSST community tests, where people’s annoyance to the noise produced by the X-59 aircraft will be gathered. Several recommendations are provided including: 1) the desired number of sparsely spaced noise monitor sites within the survey area, 2) whether to group and average measurements across multiple noise monitors located at a site, 3) what spacing should be used if grouped noise monitors are used, 4) an approach to mitigate ambient noise contamination at the measurement sites, 5) a method to combine empirical and predicted dose estimates to provide a single dose estimate for respondents, and 6) assessing how changes in turbulence intensity and array configuration affect dose uncertainty. To make these recommendations, the error that is expected when fitting contrived, smoothly varying sonic boom “reference exposure surfaces” is studied when a spatially sparse and scattered set of samples is used as responses for the fit. The reference exposure surfaces mimic the sonic boom exposure at ground level that might be expected in the X-59 survey area in the absence of atmospheric turbulence, ambient noise, and other localized effects. The spatial extent of these surfaces varies and is representative of the different survey area sizes that might be expected during future X-59 community overflight tests. These contrived reference surfaces are sampled, and those reference samples are then perturbed to mimic atmospheric turbulence, ambient noise and other localized effects that might affect noise monitor measurements within overflown communities. Two different surface fitting methods are investigated when fitting these perturbed samples to approximate the reference surface. The first method uses interpolation between the perturbed data at the scattered sites to compute the fit. The second method fits a polynomial surface model to the perturbed data using ordinary least squares regression analysis. For both fitting methods, the root mean square fit error is computed from the pointwise difference between the fit surface and the reference surface as the count and configuration of the sites is varied while also averaging the error across many different realizations of both the smooth variation of the reference exposure surface and the random, localized perturbations at the sample sites. Different site configurations are compared using this error statistic to make the recommendations noted above. Additionally, the two fitting approaches (interpolation vs linear regression) are compared based on the fit error observed in these simulations. These analyses, comparisons, and recommendations should inform future decisions on the noise monitor placement and the methods used to analyze the noise monitor data that is collected during X-59 community overflights.

sonic boom↗

Noise exposure reduction of advanced high-lift systems

The purpose of NASA Contract NAS1-20090 Task 3 was to investigate the potential for noise reduction that would result from improving the high-lift performance of conventional subsonic transports. The study showed that an increase in lift-to-drag ratio of 15 percent would reduce certification noise levels by about 2 EPNdB on approach, 1.5 EPNdB on cutback, and zero EPNdB on sideline. In most cases, noise contour areas would be reduced by 10 to 20 percent.

Haffner, Stephen W.↗

The development of a method for predicting the noise exposure of payloads in the space shuttle orbiter vehicle

The development of an analytical model for the prediction of sound levels in the payload bay of the space shuttle orbiter vehicle is outlined. Formulation of the analytical model and its validation by means of model scale and full scale tests are included. It is shown that the approach used in the development effort has resulted in a prediction procedure which can be expected to give reliable estimates of payload bay sound levels, even when a payload is present. Furthermore, the analytical model has the capability of being readily modified to include other excitations such as turbulent boundary layers and propeller near-field pressures, and to other aerospace vehicles.

Wilby, J. F.↗

Validation of Aircraft Noise Models at Lower Levels of Exposure

Noise levels around airports and airbases in the United States arc computed via the FAA's Integrated Noise Model (INM) or the Air Force's NOISEMAP (NMAP) program. These models were originally developed for use in the vicinity of airports, at distances which encompass a day night average sound level in decibels (Ldn) of 65 dB or higher. There is increasing interest in aircraft noise at larger distances from the airport. including en-route noise. To evaluate the applicability of INM and NMAP at larger distances, a measurement program was conducted at a major air carrier airport with monitoring sites located in areas exposed to an Ldn of 55 dB and higher. Automated Radar Terminal System (ARTS) radar tracking data were obtained to provide actual flight parameters and positive identification of aircraft. Flight operations were grouped according to aircraft type. stage length, straight versus curved flight tracks, and arrival versus departure. Sound exposure levels (SEL) were computed at monitoring locations, using the INM, and compared with measured values. While individual overflight SEL data was characterized by a high variance, analysis performed on an energy-averaging basis indicates that INM and similar models can be applied to regions exposed to an Ldn of 55 dB with no loss of reliability.

Page, Juliet A.↗

Estimating Sonic Boom Metrics Across a Community Using a Kalman Filter

As part of the Quesst mission, NASA will fly the X‑59 aircraft over selected communities to evaluate community responses to low-intensity sonic booms. The purpose of these community tests is to determine the dose-response relationship between the noise exposure metrics and the community response. The independent variables for the dose-response relationship are the noise exposure metrics experienced by survey respondents within the community. Two sources of noise exposure metrics are available in each community: measurements at sparse locations throughout the community, and calculations from propagation models across the community. Both the measurements and calculations are subject to uncertainty. A Kalman filter is proposed to combine the measured and calculated noise exposure metrics to obtain the best estimate of the true noise exposure metrics across the community. The noise exposure metrics estimated by the Kalman filter have lower uncertainty than either the measured or calculated noise exposure metrics alone. Simulations demonstrate that the Kalman filter produces a more accurate estimate of the true noise exposure metrics than other noise estimation methods.

Sonic boom↗

A Comprehensive Approach to Management of Workplace and Environmental Noise at NASA Lewis Research Center

NASA Lewis Research Center is home to more than 100 experimental research testing facilities and laboratories, including large wind tunnels and engine test cells, which in combination create a varied and complex noise environment. Much of the equipment was manufactured prior to the enactment of legislation limiting product noise emissions or occupational noise exposure. Routine facility maintenance and associated construction also contributes to a noise exposure management responsibility which is equal in magnitude and scope to that of several small industrial companies. The Noise Program, centrally managed within the Office of Environmental Programs at LRC, maintains overall responsibility for hearing conservation, community noise control, and acoustical and noise control engineering. Centralized management of the LRC Noise Program facilitates the timely development and implementation of engineered noise control solutions for problems identified via either the Hearing Conservation of Community Noise Program. The key element of the Lewis Research Center Noise Program, Acoustical and Noise Control Engineering Services, is focused on developing solutions that permanently reduce employee and community noise exposure and maximize research productivity by reducing or eliminating administrative and operational controls and by improving the safety and comfort of the work environment. The Hearing Conservation Program provides noise exposure assessment, medical monitoring, and training for civil servant and contractor employees. The Community Noise Program aims to maintain the support of LRC's neighboring communities while enabling necessary research operations to accomplish their programmatic goals. Noise control engineering capability resides within the Noise Program. The noise control engineering, based on specific exposure limits, is a fundamental consideration throughout the design phase of new test facilities, labs, and office buildings. In summary, the Noise Program addresses hearing conservation, community noise control, and acoustical and noise control engineering.

Cooper, Beth A.↗

Guide to the evaluation of human exposure to noise from large wind turbines

Guidance for evaluating human exposure to wind turbine noise is provided and includes consideration of the source characteristics, the propagation to the receiver location, and the exposure of the receiver to the noise. The criteria for evaluation of human exposure are based on comparisons of the noise at the receiver location with the human perception thresholds for wind turbine noise and noise-induced building vibrations in the presence of background noise.

Stephens, D. G.↗

Simulations of X-59 Sonic Thumps and Traditional Sonic Booms Propagated Around the World for Three Atmospheric Models

Propagation simulations of sonic booms from supersonic aircraft through atmospheric data over time at fixed locations provides the opportunity to assess noise exposure statistics for different climate regions. Knowledge of climate-based differences in sonic boom noise exposure statistics is important to ensure that future civil supersonic aircraft noise certification standards are globally applicable and effective. In this presentation, simulated sonic booms from the NASA X-59 Quesst quiet supersonic aircraft and conventional supersonic aircraft were propagated through atmospheric data at 100 locations across the world using PCBoom. Noise exposure statistics are compared for propagation results from three different atmospheric databases (NOAA Global Forecast System, NOAA Climate Forecast System Version 2, and the ECMWF Reanalysis Version 5). These atmospheric models were chosen due to their global coverage, popularity, and database availability. Preliminary statistical models are fit to assess the impact of several factors including flight direction, season, ground elevation, and climate on noise exposure size and loudness. Areas with prevalence of higher noise due to their climate are identified, which could help inform future supersonic aircraft noise standards.

X-59↗

International Space Station Noise Constraints Flight Rule Process

Crewmembers onboard the International Space Station (ISS) live in a unique workplace environment for as long as 6 ‐12 months. During these long‐duration ISS missions, noise exposures from onboard equipment are posing concerns for human factors and crewmember health risks, such as possible reductions in hearing sensitivity, disruptions of crew sleep, interference with speech intelligibility and voice communications, interference with crew task performance, and reduced alarm audibility. The purpose of this poster is to describe how a recently‐updated noise constraints flight rule is being used to implement a NASA‐created Noise Exposure Estimation Tool and Noise Hazard Inventory to predict crew noise exposures and recommend when hearing protection devices are needed.

Limardo, Jose G.↗

Noise Level Hazards due to Agricultural Equipment in a Laboratory

Noise exposure is a common health hazard in the agricultural field due to the continuous use of large pieces of equipment. In lab settings it is not as common to noise exposure due to industrial scale equipment. However, the energy system laboratories at INL are exploring new and inventive ways to create energy from biomaterials. This process requires the use of large pieces of agricultural equipment such as hammer mills, shakers, conveyor belts, etc. As the researchers continuously refine this process, they not only test the limits of renewable resources for energy but also test the noise exposure threshold limits. The purpose of this project was to determine the noise level hazards which are present due to the use of large agricultural equipment in a laboratory setting.

99 GENERAL AND MISCELLANEOUS↗

Effects of Long Duration Space Flight on Low Frequency Hearing

Pure tone hearing threshold data has been collected on astronauts since the early days of spaceflight. Between 1981 and 2001 (STS-1 thru 108) it was found that of the 608 shuttle astronauts, 17% returned home with a significant hearing threshold shift, and of that 6% had permanent changes in the high frequencies. Believed to be due to prolonged noise exposure on the space crafts. (Hart, 2006) As NASA aimed for longer duration missions the uncertainty about the effects of month-long noise exposure needed to be addressed. To avoid constraining crewmembers to constant earplug usage, and satisfying budget demands, a focused effort on overcoming the issues began. Noise monitoring systems were introduced onto the International Space Station (ISS) and astronauts were given education on the noise environment aboard the ISS, the hazards of noise induced hearing loss, hearing conservation principles, and countermeasure strategies (earplugs, noise cancellation headsets). As well as being trained on a new countermeasure effort that had been in trial testing on prior shuttle missions (STS-6 thru 8), On-Orbit Hearing Assessment (OOHA). (Hart, 2006) OOHA was implemented to routinely monitor the hearing thresholds of crewmembers who are on orbit for longer than 30 days. Flight Surgeons on ground monitor these test for significant hearing changes while aboard. Between 2000 and 2021 the ISS OOHA system (EarQ) recorded changes in the low frequency hearing thresholds. Low frequency hearing changes are not typically caused by noise exposure, and it was initially believed that these changes were due to environmental noise interference. In 2021 an ISS software upgrade (KUDUwave) allowed for a more comprehensive hearing assessment. The new testing system allowed for additional tests that helped describe the type of hearing loss being experienced and determine the influence of ambient noise or other environmental factors. OOHA Data indicates a low frequency hearing shift in 40% of all tests and 60% of all crewmembers. Since 2021, OOHA results (using the KUDUwave) suggest that these shifts are sensorineural and are not the result of ambient noise. While hearing shifts are seen at both 250 and 500 Hz, metrics tracking these shifts have only used 500 Hz data. The lack of 250 Hz has prevented accurate reporting of low frequency hearing shift recovery. The aim of this study is to capture 250 Hz data and accurately present low frequency hearing loss recovery.

hearing↗

Experiment Design and Visualization Techniques for an X-59 Low-boom Variability Study

This presentation outlines the design of experiments approach and data visualization techniques for a simulation study of sonic booms from NASA’s X-59 supersonic aircraft. The X-59 will soon be flown over communities across the contiguous USA as it produces a low-loudness sonic boom, or low-boom. Survey data on human perception of low-booms will be collected to support development of potential future commercial supersonic aircraft noise regulatory standards. The macroscopic atmosphere plays a critical role in the loudness of sonic booms. The extensive sonic boom simulation study presented herein was completed to assess climatological, geographical, and seasonal effects on the variability of the X-59’s low-boom loudness and noise exposure region size in order to inform X-59 community test planning. The loudness and extent of the noise exposure region make up the “sonic boom carpet.” Two spatial and temporal resolutions of atmospheric input data to the simulation were investigated. A Fast Flexible Space-Filling Design was used to select the locations across the USA for the two spatial resolutions. Analysis of simulated X-59 low-boom loudness data within a regional subset of the northeast USA was completed using a bootstrap forest to determine the final spatial and temporal resolution of the countrywide simulation study. Atmospheric profiles from NOAA’s Climate Forecast System Version 2 database were used to generate over one million simulated X-59 carpets at the final selected 138 locations across the USA. Effects of aircraft heading, season, geography, and climate zone on low-boom levels and noise exposure region size were analyzed. Models were developed to estimate loudness metrics throughout the USA for X-59 supersonic cruise overflight, and results were visualized on maps to show geographical and seasonal trends. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

X-59↗

The AIRNOISE-UAM Tool and its Application to ATM-X

The noise disruption caused by Urban Air Mobility (UAM) vehicles will be a major factor in public acceptance of UAM. The noise exposure caused by fixed-wing aircraft and helicopters is predicted using Aviation Environment and Design Tool (AEDT) software at the airport. The base noise is calculated by interpolation or extrapolation of Noise-Power-Distance (NPD) database in the AEDT. In this presentation, a new noise prediction tool "AIRNOISE-UAM" is developed with a new NPD database for eVTOL vehciles in collaboration with a RVLT team. The noise exposure results have been verified with those from AEDT. Lastly, we demonstrate two use cases of AIRNOISE-UAM to the ATM-X project. The first use case is to develop noise exposure maps for FAA UAM compliance planning. The sceond use case is to develop noise-aware flight route planning algorithm.

Urban Air Mobility↗