Validation of Aircraft Noise Predictions via AEDT using Large-Scale Data Collection

Juan Alonso

Professor
Department of Aeronautics and Astronautics
Stanford University

Seminar Information

Seminar Series

Penner Distinguished Lectures in the Aerospace & Mechanical Engineering Sciences

Seminar Date - Time

May 14, 2026, 3:00 pm

-

4:00

Seminar Location

CMRR JKW Auditorium + Zoom

Important Links

Zoom Link

Abstract

Assessing aircraft noise exposure accurately is critical given that noise assessments are the backbone of (a) our existing national aircraft noise policy and (b) of any actions taken for noise abatement and mitigation purposes. In the U.S., the Federal Aviation Administration's Aviation Environmental Design Tool (AEDT) is approved to predict the impacts of aircraft noise and emissions. AEDT's critical role in regulatory compliance and evaluating the environmental impacts of aviation requires asking how accurate are its noise predictions. Previous studies suggest that AEDT's predictive accuracy could be improved in some settings. This presentation describes the results of a large-scale study we have conducted over the past 3.5 years, using over 200,000 flight trajectories paired with measured sound levels for arrivals to Runways 28L/28R at San Francisco International Airport, over 12 months. For each flight, two AEDT studies were run, one using the approved mode for regulatory filing and the other using an advanced non-regulatory mode with exact aircraft trajectories. AEDT's per aircraft noise predictions for arrival operations into SFO were compared with curated measured sound levels at two locations. On average, AEDT underestimated LAmax by −3.09 dB and SEL by −2.04 dB. Discrepancies appear to result from limitations in the physical modeling of flight trajectories and noise generation, combined with input data uncertainties (aircraft weight, airspeed, thrust, and lift configuration) and atmospheric conditions. Given the abundance of data, we have experimented with the use of data-driven methods to construct more accurate noise models of certain classes of aircraft that we observe very frequently. Some results for this enhanced modeling strategy are also discussed.

Speaker Bio

Juan J. Alonso is the Vance D. and Arlene C. Coffman Professor of Aeronautics and Astronautics at Stanford University and the James and Anna Marie Spilker Chair of the Department. Prof. Alonso is the founder and director of the Aerospace Design Laboratory (ADL) where he specializes in the development of high-fidelity computational analysis and design methodologies to enable the creation of realizable and efficient aerospace systems. He is keenly interested in the sustainability of the commercial aviation enterprise. He is the author of over 300 technical publications on the topics of computational aircraft and spacecraft design, multi-disciplinary optimization, fundamental numerical methods, and high-performance parallel computing. During the period spanning 2006-09, Prof. Alonso was the Director of the NASA Fundamental Aeronautics Program in Washington, DC. In that position he was responsible for the entire portfolio of aerospace vehicle and vehicle technology research for the agency in the subsonic rotary wing, subsonic fixed wing, supersonic, and hypersonic regimes, with particular emphasis on the energy and fuel efficiency of the aviation enterprise and its environmental impact. He is the recipient of several AIAA Best Paper Awards, the NASA Exceptional Public Service Medal, the NASA ARMD Associate Administrator Award, and the AIAA Stanford Chapter Professor of the Year award (8 times). Prof. Alonso has served in the NASA Advisory Council, the Secretary of Transportation’s Future of Aviation Advisory Committee, the FAA Administrator’s Management Advisory Council, and as an Independent Expert in the ICAO/CAEP fuel burn, noise, and emissions technology goals evaluation. He is also the CTO and co-founder of a startup company, Luminary, that is attempting to revolutionize multi-physics simulations through GPU computing. Prof. Alonso earned his Ph.D. in Mechanical and Aerospace Engineering at Princeton University and his B.S. degree at the Massachusetts Institute of Technology.