Skip to content

  • Research
    • Projects
  • Universities
    • Main Universities
    • Affiliate Universities
  • Partners
    • Advisory Committee
    • Request Information
  • Resources
    • General Public Resources
    • Researcher Resources
    • Student Resources
    • Relevant External Links
  • Contact Us
    • Leadership
    • Administrative Staff

Search

white clouds above silhouette of clouds at day

Improved Engine Fan Broadband Noise Prediction Capabilities

Project Number: 075
Category: Noise, Aircraft Technology Innovation

Annual Reports

  • 2020 Annual Report
  • 2021 Annual Report
  • 2022 Annual Report
  • 2023 Annual Report
  • 2024 Annual Report

Participants

  • RTX Technology Research Center
  • Boston University

Lead Investigators

  • Sheryl Grace
  • Jeffrey Mendoza

    Program Managers

    • Chris Dorbian

    Publications

    • Effect of Straight and Swept FEGV Placement on Fan Broadband Interaction Noise
    • Machine Learning Aided Low-Order Predictions of Fan Stage Broadband Interaction Noise
    • Machine Learning Aided Fan Broadband Interaction Noise Prediction for Leaned and Swept Fans
    • Fan-stage Broadband Interaction Noise Trends
    • Fan Wake Prediction Via Machine Learning

    FAA Center of Excellence for Alternative Jet Fuels & Environment

    • Washington State University
    • Massachusetts Institute of Technology
    • Federal Aviation Administration
    © Washington State University