Aeroacoustics is a critical research field that studies sound generated by turbulent flows interacting with solid structures like aircraft and cars. As the demand for quieter vehicles increases and noise pollution's impact on human well-being becomes clearer, there's a need for precise methods to predict and reduce aerodynamic noise. Computational Aeroacoustics (CAA) combines theory and computation to address these challenges. CAA relies on Computational Fluid Dynamics (CFD) to analyze fluid flow and its acoustic effects. To capture flow dynamics and acoustics accurately, especially in turbulent scenarios, it's crucial to use reliable CFD approaches. Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) are commonly used for simulating turbulence, but each has limitations in accuracy and efficiency. To overcome these limitations, researchers have developed hybrid LES/RANS methods like Partially-Averaged Navier-Stokes (PANS), aiming for a balance between computational cost and accuracy. This research focuses onassessing the SSV-PANS method, a specific PANS variant, by comparing it to Large Eddy Simulation in terms of accuracy and computational efficiency. By dissecting fluid variables into hydrodynamic and compressible perturbation equations, the study analyzes airflow around an Ahmed body, a well-known benchmark case in Computational Fluid Dynamics (CFD). Through these analyses, the research advances our understanding of CAA methods, especially the SSV-PANS method, contributing to quieter vehicles, reduced noise emissions, and optimized vehicle designs. This promotes effective noise pollution reduction and healthier, more sustainable communities.