Sound land use planning requires accurate predictions of the acoustic signatures at noise-sensitive receiver points and methods for interpreting the effect of acoustic signatures on public health, safety, and welfare. Historically, the study of noise impacts from aviation has been focused on fixed-wing aircraft, while the complexity of helicopter and new-technology rotary-wing aircraft has not been given adequate attention.The FAA Integrated Noise Model (INM) is currently the agency’s required tool for NEPA-related studies and FAR Part 150 studies. The Heliport Noise Model Version 2.2 was recently incorporated into INM Version 7.0 with a helicopter noise database collected through both FAA and manufacturer certification measurements. Currently, the FAA is incorporating INM, along with emission and fuel burn calculation methodologies, into the Aviation Environmental Design Tool (AEDT). The fixed-wing aircraft noise prediction techniques employed in INM/AEDT rely on the widely accepted methodologies described in documents such as SAE International’s SAE-AIR-1845 and the European Civil Aviation Conference’s Document 29. However, in contrast to guidance related to fixed-wing aircraft, there is no peer-reviewed guidance document describing an integrated modeling technique for the prediction of helicopter noise.Research is needed to document current practice, improve modeling methods, and provide guidance for using INM/AEDT to predict helicopter noise.
Recommended Community Noise Model Enhancements to Improve Prediction of Helicopter Activity Impacts. Airport Cooperative Research Program (ACRP) Research Results Digest 24
2017
6 pages
Report
Keine Angabe
Englisch
Aircraft , Management Practice , Air Transportation , Transportation , Avionics , Noise Pollution & Control , Airport information , Airport practices , Airport administrators , Computational methodology , Helicopter Activity , Noise analysis , Airport Cooperative Research Program (ACRP) , National Cooperative Highway Research Program (NCHRP) , Integrated Noise Model (INM)