Noise-Sensitivity to Vehicle-Level Design Variables

Abstract

Space-filling design of experiments are performed on the Environmental Design Space (EDS) architecture to enable an understanding of the sensitivity of various noise-metrics to vehicle-level design variables. These include aerodynamic, propulsion, and airframe design variables. Half-normal probability plots are used to show that airframe design variables dominate approach noise, while design variables related to bypass ratio have the greatest influence on departure noise. These results are consistent for both NPD-curves and soundexposure-level (SEL) contour areas, but not for the certification effective-perceived-noiselevel (EPNL) metrics or Maximum A-weighted Sound Level (L Amax ) based spectra, which demonstrate some unique sensitivities. Interdependencies of noise, fuel burn, and NO x emissions metrics are also explored. Nomenclature AEDT = Aviation Environmental Design ToolANOPP = Aircraft Noise Prediction Program ASDL = Aerospace Systems Design Laboratory CAEP = Committee on Aviation Environmental Protection CDA = Constant Descent Angle CMPGEN = Fan and Compressor Component Map Generating Tool dB = Decibels DNL = Day-Night-Average Level DOE = Design of Experiments EDS = Environmental Design Space EPA = Environmental Protection Agency EPNL = Effective Perceived Noise Level FAA = Federal Aviation Administration FLOPS = Flight Optimization System ICAO = International Civil Aviation Organization INM = Integrated Noise Model L Amax = Maximum A-Weighted Sound Level LHC = Latin Hypercube

The Environmental Design Space: Modeling and Performance Updates

“…Last but not least, LeVine [15] provided an overview of the sensitivity of noise metrics with respect to high-level design parameters used in EDS. As shown in Fig.…”

Development of Generic Vehicles for Fleet-Level Analysis of Noise and Emissions Tradeoffs

“…5 Furthermore, LeVine et al performed sensitivity analyses with respect to noise-metrics, specifically SEL contours, additionally exploring some of the interdependencies with fuel burn and NO x emissions. 19 Cross-referencing these two studies allowed for the reduction of the design space by defaulting variables to baseline values if they were not significantly driving the performance with respect to the environmental metrics. Latin hypercube (LHC) designs of experiments (DOEs) were employed on the remaining design space, and surrogate models were fit to rapidly hone in on the locations in the design space of the best generic vehicle designs for Test I.A.…”