APPROVED This thesis presents an enhanced method for predicting aerodynamically generated broadband noise, produced by rotating machinery. The method improves on existing work for Vertical Axis Wind Turbine (VAWT) noise prediction, but is formulated to be applicable to a host of fan and rotor configurations. Recently developed analytical and semi-empirical airfoil noise models for inflow-turbulence and self-noise mechanisms are considered. Models were applied to a spatially and temporally discretised computational domain in a stripwise manner. Airfoil noise predictions are dependent on aerodynamic input data, and analytic methods were derived to calculate the required input parameters. Comparisons to experimental noise measurements for an operational wind turbine are encouraging. Overall Sound Pressure Level (SPL) trends for changes in atmospheric inflow velocities, and rotor speeds, are captured. Time dependent Computational Fluid Dynamics (CFD) calculations were carried out to solve the aerodynamic solution for a newly designed six-bladed VAWT. The CFD flow solution was used as input data to inform the noise models and predictions were compared to benchmark results, produced using the analytical approach, to quantify errors between the two methods. A parameter study was performed, which shows the sensitivity of overall VAWT noise levels to changes in atmospheric inflow velocity and turbulence. Noise sources were characterised, and the location and mechanism of the primary sources was determined. The results show that inflow-turbulence noise is the primary source of aeroacoustic noise generated by a VAWT, and that up to 10 % turbulence intensity, atmospheric turbulence dominates blade generated turbulence. The approach shows that CFD can be leveraged to provide additional insight for noise predictions when using analytical noise models. The CFD based prediction approach was also applied to a small scale Contra Rotating Open Rotor (CROR), where overall broadband noise levels, as well as directivity patterns, are comparable to measurements.