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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

Considering value of information when using CFD in design