This thesis presents the development, implementation, and validation of a low-fidelity Aeroacoustic Prediction Framework designed for airborne wind energy systems (AWES), with the Kitepower system as a case study. As AWES technology moves toward commercial viability, understanding and predicting its acoustic emissions becomes critical for regulatory compliance, public acceptance, and design optimization.
The framework integrates established analytical and semi-empirical aeroacoustic models with aerodynamic data based on derived geometry and detailed flight information. It models all major noise sources from the airborne components, such as the Leading Edge Inflatable (LEI) kite, bridle lines, tether, and onboard ram-air turbine. The most significant contributions to the overall noise signature were found to be turbulent boundary layer trailing edge (TBL-TE) noise from airfoils, modeled using the Brooks–Pope–Marcolini (BPM) approach, vortex-shedding noise from cylindrical structures such as the tether and bridle lines, and tonal harmonics produced by the rotating turbine blades, captured through Hanson’s helicoidal surface theory.
To generate aerodynamic input, spanwise airfoil profiles were automatically extracted from 3D CAD models and analyzed through XFOIL. Real-time flight data was provided by an onboard sensor suite and processed through an Extended Kalman Filter (EKF), allowing dynamic simulation of flight conditions. Audio recordings were collected during test flights using GoPro cameras, enabling experimental validation of the acoustic predictions despite the absence of calibrated SPL measurements.
Validation showed strong agreement between predicted and measured spectra up to 5 kHz, particularly for turbine harmonics and general spectral shape. Deviations in the lower tonal harmonics were primarily attributed to acoustic shielding caused by the turbine’s duct structure. Additionally, the use of GoPro cameras introduced limitations due to their lack of calibration data and the presence of internal low-pass filtering above 5 kHz. Despite these constraints, the model successfully predicted tonal peaks, including the blade passing frequency and higher-order harmonics, aligning well with the experimental observations.
Additionally, the framework investigates the influence of the propagation effects, such as atmospheric absorption and geometric spreading, and integrates them to produce realistic observer-based predictions. Despite using non-professional audio hardware, the predictions captured key features including harmonic roll-off and broadband trends, affirming the framework's validity for early-stage design and evaluation.
This work demonstrates that low-order, physics-based models paired with aerodynamic inputs and synchronized flight data can yield meaningful acoustic predictions for AWES. The framework offers modularity, computational efficiency, and adaptability for future upgrades, such as the use of calibrated microphones or high-fidelity CFD data. It serves as a foundation for future extensions in auralization, psychoacoustic testing, and component-level noise reduction strategies.
Ultimately, the thesis bridges theoretical modeling with field-based validation, supporting the responsible integration of AWES technologies into noise-sensitive environments.