A Regulatory Analysis and Process Improvement Decision Support Framework for Unmanned Aerial System (UAS) Noise Certification

Balchanos, Michael; Ravikanti, Balaji; Ali, Hussein; Mali, Hajar; Harrison, Evan; Mavris, Dimitri N; United States. Federal Aviation Administration. Center of Excellence for Alternative Jet Fuels and Environment; Georgia Institute of Technology

doi:10.17866/rd.salford.27913140.v1

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A Regulatory Analysis and Process Improvement Decision Support Framework for Unmanned Aerial System (UAS) Noise Certification

2024-11-01
By Balchanos, Michael ; Ravikanti, Balaji ; Ali, Hussein ; ...

[PDF-3.69 MB]

English

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Creators:

Balchanos, Michael ; Ravikanti, Balaji ; Ali, Hussein ; Mali, Hajar ; Harrison, Evan ; ... More +

Balchanos, Michael ; Ravikanti, Balaji ; Ali, Hussein ; Mali, Hajar ; Harrison, Evan ; Mavris, Dimitri N Less -
Corporate Creators:

United States. Federal Aviation Administration. Center of Excellence for Alternative Jet Fuels and Environment ; Georgia Institute of Technology

United States. Federal Aviation Administration. Center of Excellence for Alternative Jet Fuels and Environment ; Georgia Institute of Technology Less -
Corporate Contributors:

United States. Department of Transportation. Federal Aviation Administration. Office of Environment and Energy
Subject/TRT Terms:

[+]

Aircraft Noise ASCENT Drones Model-based Systems Engineering (MBSE) Regulations Unmanned Aerial Vehicles VTOL Aircraft
Publication/ Report Number:

QD24_submission_128
DOI:

https://doi.org/10.17866/rd.salford.27913140.v1
Resource Type:

Proceedings
Right Statement:

Open Access; https://creativecommons.org/licenses/by/4.0/
Geographical Coverage:

United States
Corporate Publisher:

University of Salford
Abstract:

The rapidly growing Unmanned Aerial Systems (UAS) vehicle category with applications across various configurations and mission profiles introduces challenges in ensuring that applicable noise certification regulations are met. A framework is proposed for assessing regulatory compliance to noise standards and analyzing the process effectiveness and outcomes of noise testing campaigns for UAS. This framework is enabled by a Model-based Systems Engineering (MBSE) workflow for creating verification models that represent requirements derived by applicable regulations under the CFR Part 36 noise certification. As a digital thread-enabled approach, this environment allows for tracking and assessing adherence to noise regulatory practices through mapping noise data flows from test campaigns to metrics of interest as part of the verification of meeting regulatory noise requirements. Moreover, the environment allows for the assessment of the certification process eliminating unnecessary redundancies. The assessment of improvement strategies and selection of testing procedures is demonstrated through an integrated decision support dashboard for a small multi-rotor electric Vertical Takeoff and Landing (eVTOL) vehicle operated for a package delivery mission.
Content Notes:

Balchanos, M., Ravikanti, B., Ali, H., Mali, H., Harrison, E., & Mavris, D. (2024). A Regulatory Analysis and Process Improvement Decision Support Framework for Unmanned Aerial System (UAS) Noise Certification. University of Salford. Quiet Drones 2024, Manchester, UK, September 9, 2024. https://doi.org/10.17866/rd.salford.27913140.v1
Format:

PDF
Funding:

13-C-AJFE-GIT-066
Collection(s):

Federal Aviation Administration
Main Document Checksum:

[+]

urn:sha-512:b69ba8ef7c826bc6cca7f767aefd26f2cd6570abe8f919ff8922c689d17ef645c1d7154756f6dc1ab007c0bb1e064bf1888c62ad4f00049806003cb98b20ff20
Download URL:

https://rosap.ntl.bts.gov/view/dot/79606/dot_79606_DS1.pdf
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A Regulatory Analysis and Process Improvement Decision Support Framework for Unmanned Aerial System (UAS) Noise Certification

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