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Comprehensive Acoustic Analysis for Urban Air Mobility Vehicles

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC20C0381
Agency Tracking Number: 205961
Amount: $123,343.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A1
Solicitation Number: SBIR_20_P1
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-08-12
Award End Date (Contract End Date): 2021-03-01
Small Business Information
34 Lexington Avenue
Ewing, NJ 08618-2302
United States
DUNS: 096857313
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Daniel Wachspress
 (609) 538-0444
 dan@continuum-dynamics.com
Business Contact
 Barbara Agans
Phone: (609) 538-0444
Email: melissa@continuum-dynamics.com
Research Institution
N/A
Abstract

A major innovative thrust in urban air mobility (UAM) is underway that could potentially transform how we travel by providing on-demand, affordable, quiet, and fast passenger-carrying operations in metropolitan areas using novel air vehicles, most employing some form of Distributed Electric Propulsion (DEP).nbsp; The need to support the rapid maturation of technology for UAM is a key motivation for the current NASA UAM Grand Challenge.nbsp; As noted by NASA, ldquo;the Grand Challenge aims to improve UAM safety and accelerate scalability through integrated demonstrations by hosting a series of UAM ecosystem-wide challenges beginning in 2020rdquo; addressing a wide range of technical impediments to the growth of UAM, including, notably, the need to characterize vehicle noise levels.nbsp; The proposed effort will both build on recent major advances in noise modeling at CDI and, in the long term, support of the acoustics analysis goals of the Grand Challenge by enhancing state-of-the-art rotary-wing aeromechanics and acoustics analysis with key additional modeling capabilities needed for comprehensive prediction of DEP aircraft noise, focusing initially on the special problems associated with the prediction of noise from multiple, time-varying RPM systems.

* Information listed above is at the time of submission. *

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