An intravascular shunt (arteriovenous anastomosis) created during hemodialysis is a common site of stenosis. Early detection of stenosis is crucial for treatment; hence, shunt sounds are gaining attention as an indicator for simple, noninvasive screening of stenosis. However, previous studies on screening methods using shunt sounds have not achieved sufficient detection accuracy for practical application. This study aimed to elucidate the mechanism of generation of shunt sounds by conducting computational fluid dynamics analysis of the fluctuations of pressure and magnitude of vorticity as potential sound sources. This insight may contribute to improving stenosis detection methods using shunt sounds. In this analysis, a side-to-end model was used as the typical anatomical geometry for vascular access. The power spectral densities of the pressure and vorticity magnitude fluctuations were estimated using an autoregressive model. Using shunt models with and without stenosis, we calculated the time fluctuations of the pressure and vorticity magnitude in the shunt blood vessel and investigated the differences in the power spectral densities of these fluctuations due to stenosis. The source of the shunt sounds was investigated by comparing the power spectral densities of the pressure and vorticity fluctuations to those of the shunt acoustic data, and a similar trend was observed. The power spectral densities of both the pressure and vorticity magnitude fluctuations were higher in the shunt model with stenosis than in the model without stenosis as the frequency increased. This analysis suggests that pressure and vorticity magnitude fluctuations in the shunt blood flow contribute to the generation of shunt sounds. Moreover, stenosis shifted these sounds to higher frequencies due to flow separation. Additionally, when the shunt acoustic spectrum at a specific point of the blood vessel exceeded those at adjacent points, especially within the 500-1200 Hz range, stenosis was likely to be present at that location. This finding demonstrates that stenosis can be detected with high accuracy by analyzing the spectral shifts in acoustic data at multiple points along a shunt. Improving software and hardware based on these insights may further enhance the accuracy of intradialytic shunt stenosis screening devices using shunt sounds.