This report presents a comprehensive study on airfoil self-noise and its prediction. The research was conducted at NASA Langley Research Center and involved aerodynamic and acoustic tests of two- and three-dimensional airfoil blade sections in an anechoic wind tunnel. The study identified and modeled five self-noise mechanisms: turbulent-boundary-layer-trailing-edge (TBL-TE) noise, separation-stall noise, laminar-boundary-layer-vortex-shedding (LBL-VS) noise, tip vortex formation noise, and trailing-edge-bluntness-vortex-shedding noise. The data used in the study were obtained from seven NACA 0012 airfoil blade sections with varying chord lengths, tested at wind tunnel speeds up to Mach 0.21 and angles of attack from 0° to 25.2°. The data were processed and scaled to develop predictive models for each noise mechanism. The predictions were compared with published data from three studies of different airfoil shapes, which were tested up to Mach 0.5 and Reynolds numbers of 4.6 × 10⁶. The results showed that the predictions matched well with the data. The study also included an application of the prediction method to a large-scale-model helicopter rotor, where the predictions were compared with data from a broadband noise test. The study concludes that the developed prediction method is effective for predicting airfoil self-noise and provides a basis for further research in this area. The report includes detailed data processing techniques, noise directivity functions, and a computer code for the prediction method.This report presents a comprehensive study on airfoil self-noise and its prediction. The research was conducted at NASA Langley Research Center and involved aerodynamic and acoustic tests of two- and three-dimensional airfoil blade sections in an anechoic wind tunnel. The study identified and modeled five self-noise mechanisms: turbulent-boundary-layer-trailing-edge (TBL-TE) noise, separation-stall noise, laminar-boundary-layer-vortex-shedding (LBL-VS) noise, tip vortex formation noise, and trailing-edge-bluntness-vortex-shedding noise. The data used in the study were obtained from seven NACA 0012 airfoil blade sections with varying chord lengths, tested at wind tunnel speeds up to Mach 0.21 and angles of attack from 0° to 25.2°. The data were processed and scaled to develop predictive models for each noise mechanism. The predictions were compared with published data from three studies of different airfoil shapes, which were tested up to Mach 0.5 and Reynolds numbers of 4.6 × 10⁶. The results showed that the predictions matched well with the data. The study also included an application of the prediction method to a large-scale-model helicopter rotor, where the predictions were compared with data from a broadband noise test. The study concludes that the developed prediction method is effective for predicting airfoil self-noise and provides a basis for further research in this area. The report includes detailed data processing techniques, noise directivity functions, and a computer code for the prediction method.