When attempting to model complicated systems, there are two strategies that can be used. One is to create a model that accounts for as many phenomena in the system as possible in order to yield precise results. The other is to identify the key phenomena that affect the overall output, and then develop a simplified model based on those phenomena. While the first method can give desirable outputs, development is resource intensive. The second strategy, though not capable of the accuracy of the first, is much more cost effective and can give quantitative answers that are just as valuable as detailed outputs. In regards to an automotive supercharger, the first method has already been employed to create a computational fluid dynamics (CFD) model of airflow through the system. The data generated from this model was analyzed and compared with measured data taken at identical conditions to determine if the CFD model was modeling the supercharger correctly, both in terms of airflow and sound generation. Also, a flow model for one portion of the supercharger cycle was developed using the key phenomena model approach. Results from this model were tested for accuracy against measured data. In addition, design applications for the second model were investigated, and a test of the effects of basic design changes conducted.