Research Opportunities

Acoustically Aware Aircraft

Background

There is a growing public enthusiasm about new applications of aeronautics in our everyday lives, from small package delivery “drones” to routine point-to-point passenger transport using electric rotorcraft; however, noise represents a major barrier to the acceptance of these vertical lift technologies into our communities. Present day helicopter operations are already severely constrained by acoustic considerations.  Enabling the next generation of vertical lift aircraft to operate in and around communities and at unprecedented flight volumes will require a radical new approach to reducing aircraft noise.

Acoustically Aware Aircraft

The aim of this research is to imbue crewed, partially and fully autonomous rotorcraft with an “acoustic awareness” that enables intuitive ultra-low-noise flight operations.  Acoustically aware aircraft will have cockpit, control, and guidance systems that work together to: provide pilots and operators with real-time information about ground noise exposure; control the vehicle flight state to limit acoustic emissions; and, generate low noise flight trajectories “on the fly” in response to changes in the mission plan, airspace, or environment.  Realizing this goal will require experimental studies to better understand the fundamental physics of the aerodynamics and acoustics of the complex rotorcraft of the future, the development of fast and accurate aeroacoustic models suitable for integration into aircraft systems, and the real time application of these models to enable ultra-low-noise flight operations.  The following undergraduate research project opportunities exist in each of these areas:

Undergraduate Research Projects

  1. Experimental Studies of the Aerodynamics and Acoustics of Interacting Rotors

A fundamental experimental study will be conducted to understand the aerodynamic interactions between rotors in multirotor vehicles and the resulting noise.  This project will involve the construction of a model rotor test apparatus and the configuration and use of acoustic data acquisition equipment.

  1. Building Rotorcraft Noise Models with Machine Learning

Machine learning methods will be applied to aerodynamic and acoustic data acquired from flight tests of helicopters in order to build fast noise models.  This project will involve processing experimental flight test data using advanced statistical techniques to isolate the contributions of the major noise sources and relate them to the aerodynamic state of the vehicle.

  1. Dynamic Replanning of Low Noise Aircraft Operations

Combinatoric optimization techniques will be applied to plan and, in response to changing circumstances, dynamically replan practical low noise flight paths for aircraft.  This project will involve the development of models for community noise exposure and aircraft performance, and the application of graph search techniques to identify the optimal trajectory of the vehicle to complete the mission objectives.


For more information, please contact:

Dr. Eric Greenwood
229E Hammond
Spring 2020 Office Hours, Monday 13:30-14:30 & Wednesday 15:30-16:30
eric.greenwood@psu.edu