Story update 6/18/2012: A couple of confusing references to Taguchi methods were removed.

During new product development, computational fluid dynamics (CFD) is often used in the design stage to simulate such things as the effect of air flow on cooling. The problem with CFD is that it can take a long time to find the solution due to the number of iterations that must be performed.

Since the architecture of the CFD software is based on solving the field equations, the time needed to complete a CFD run depends on the degree of convergence, or accuracy, expected. Long run times can tremendously increase the cost and the turnaround time of a project, an important aspect to consider when you are in a war to beat the competition.

An alternative to using CFD alone is to cut down on the number of iterations by using design-of-experiments (DOE) to preselect runs that you know are going to yield results close to what you are looking for. This reduces simulation bottlenecks, thus increasing productivity and efficiency while maintaining the required accuracy levels. The number of runs can be further refined and optimized using the Monte Carlo approach.

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