This thesis presents an approach to find lower resolution CFD models that can accurately lead a designer to a correct decision at a lower computational cost. High-fidelity CFD models often contain too much information and come at a higher computational cost, limiting the designs a designer can test and how much optimization can be performed on the design. Lower model resolution is commonly used to reduce computational time. However there are no clear guidelines on how much model accuracy is required. Instead experience and intuition are used to select an appropriate lower resolution model. This thesis presents an alternative to this ad hoc method by considering the added value of the addition information provided by increasing accurate and more computationally expensive models. In the algorithm presented here for selecting the correct model resolution, the designer, who should be most familiar with the model, creates some quantifiable metrics for model components that he/she identifies as key characteristics. The designer uses the individual component metrics to create a selection utility function. The selection utility function is used to validate the accuracy of low-resolution model by comparing the magnitude of utility that leads to the correct decision. The low-resolution model can then be used to test multiple designs and/or for optimization at substantially lower computational time, giving the designer more flexibility in the design process for the model and other similar models.