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Kathryn M. Ballard

Publications and source records attributed to Kathryn M. Ballard.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Acoustics Technical Working Group and UAM Noise Working Group Proceedings

The NASA Acoustics Technical Working Group Meeting originally started as a program planning meeting in 1992 and has grown through the years. The purpose of these biannual meetings is to foster communication and collaboration among NASA researchers and their university, industry, and government colleagues on activities and work of current and future mutual interest. The NASA-led Urban Air Mobility (UAM) Noise Working Group (UNWG) Meeting began in 2018 as a one-day meeting immediately following the Acoustics Technical Working Group Meeting. The UNWG utilizes four subgroups that conduct regular meetings throughout the year to focus on challenges facing UAM vehicles for community noise and acceptance. The four subgroups coordinate research for development of noise prediction tools/noise reduction technologies, ground and flight test methods, human response and metrics, and regulations and policy (led by the FAA). This work is a compilation of the presentations given at ATWG/UNWG Fall 2022 held in person and via Teams at NASA Glenn Research Center. All NASA content was previously submitted through STRIVES for public presentation. This is merely a compilation. Non-NASA content has permission to distribute. Slide 208 video is included in the Available Downloads as Medial.MOV.

Advanced Air Mobility↗
NASA NTRS2022

Consistency of Annoyance Perception of Sonic Booms and Survey Satisficing

There is a potential in community sonic boom tests for survey measurement error due to recall errors. This occurs when a reported value differs from a participant’s true experience. The two topics examined in this paper are 1) recall bias: how consistently participants recall their annoyance to sonic thump events and 2) survey satisficing: whether participants opt for early survey termination if the option is available. Data from QSF18 were examined for evidence of recall bias and survey satisficing via inconsistencies between single event and daily summary survey reports. In terms of recall bias, when only one single event survey and daily summary survey were submitted by a participant on a particular day, both the single event and daily summary annoyance ratings match in the majority of instances (167 of 186). In terms of survey satisficing, there were fewer questions in the daily summary survey if the participant reported not hearing any sonic thumps during the day. Nevertheless, the instances of participants inconsistently reporting boom audibility between the single event and daily summary survey are relatively few (66 of 767). Therefore, the results of this study provide evidence to rule out recall bias and satisficing as sources of error in the QSF18 study.

Recall bias↗
NASA NTRSDate not supplied

Infusing Statistical Thinking into the NASA Quesst Community Test Campaign

Statistical thinking permeates many important decisions as NASA plans its Quesst mission, which will culminate in a series of community overflights using the X-59 aircraft to demonstrate low-noise supersonic flight. Month-long longitudinal surveys will be deployed to assess human perception and annoyance to this new acoustic phenomenon. NASA works with a large contractor team to develop systems and methodologies to estimate noise doses, to test and field socio-acoustic surveys, and to study the relationship between the two quantities, dose and response, through appropriate choices of statistical models. This latter dose-response relationship will serve as an important tool as national and international noise regulators debate whether overland supersonic flights could be permitted once again within permissible noise limits. In this presentation we highlight several areas where statistical thinking has come into play, including issues of sampling, classification and data fusion, and analysis of longitudinal survey data that are subject to rare events and the consequences of measurement error. We note several operational constraints that shape the appeal or feasibility of some decisions on statistical approaches, and we identify several important remaining questions to be addressed.

Bayesian model↗
NASA NTRSDate not supplied

Examining the Relationship between Self-Reported Workload and Resilient Performance in Airline Flight Crews

Workload has long been associated with human performance in aviation. High workload is typically viewed as a system design problem. The aim of this work was to find observable behaviors that guard cognitive resource margins; thus, improve handling of perturbations that arise. Twelve commercial airline flight crews participated in an experiment at NASA Langley Research Center. We explored workload data (NASA Task Load Index) self-reported after completing flight simulation scenarios to see how different individuals, using the same technologies, cope with challenging situations. A trained observer from the same airline as the study participants assessed performance. We differentiated a lower workload crew from a higher workload crew and analyzed their workload and performance. Results indicate that the workload may be a component of pilots’ resilient performance. Initial strategies that reduced workload were classified and these types of behaviors, if implemented correctly, might assist pilots in maintaining functional workload resource margins.

Michael Stewart↗
NASA NTRSDate not supplied

Dose Error Impacts on A Collection of Realistic Dose-Response Curves Based on A NASA Sonic Boom Community Noise Survey

The National Aeronautics and Space Administration (NASA) plans to conduct surveys of community response to quiet supersonic flight to collect dose-response data for international regulators. Previous models of noise dose versus annoyance response ignored uncertainty in the noise dose experienced by survey respondents. This dose error causes attenuation bias and results in inaccurate dose-response relationships. The impacts of dose errors can be explored by introducing error into the dose estimates of a simulated community noise survey. The simulated population annoyance response is determined by specific noise response characteristics, including the onset of annoyance intercept, rate of increasing annoyance, participant intercept variability, and response rates. This paper explores the effects of dose errors on notional populations created by perturbing the noise response characteristics observed in participants in a recent NASA flight test, QSF18. The QSF18-based population parameters were shifted up to ±20% to create the study populations. For the anticipated dose range of the X-59, changes to the onset of annoyance intercept have the greatest impact on erroneous model results. Quantifying point estimate errors illustrates the impact of dose error, with errors up to 14 dB observed for a fixed high annoyance percentage.

dose-response modeling↗
NASA NTRSDate not supplied

Mitigating the Impacts of Measurement Error in the Quesst Mission Community Noise Study

Beginning in 2025, the NASA Quesst mission will conduct a series of community response tests involving flyovers of the X-59 aircraft at select localities across the United States. Several waves of a longitudinal survey will be administered over approximately one month of testing in order to capture perceptual responses to low-amplitude sonic booms, or “sonic thumps”. Simultaneously, noise exposure levels will be estimated by fusing model-based predictions with measurements taken from a sparse network of monitors in the region. As one of the aims of the study is to produce a dose-response curve, a regression model relating perceptual response to noise exposure levels, it is important to acknowledge the potential attenuation bias that results from measurement error in the estimated noise exposure levels. In this presentation we review and compare several methods for dealing with measurement error in generalized linear mixed models. The methods are demonstrated on simulated data and real data collected during past NASA risk reduction studies.

measurement error↗