Historically, those predictions came from hand calculations or from the experimental testing of physical prototypes. Although these approaches delivered some of the greatest technological advances of the modern world, they have some fundamental shortcomings.

The first is that both approaches rely on a large degree of simplification as the engineer is forced to simplify the real-world problem into a form that can easily be formulated or tested.

The second is that the cost of physical testing limits its deployment to a small number of specially chosen “design points” that are not necessarily representative of the full range of operating conditions that the product will experience during its lifetime. Thirdly, experimental testing is usually time-consuming and therefore deployed late in the design process - often only as a confirmation of the final design.

Today engineering simulation provides the most reliable flow of information into the design process. It provides comprehensive predictions that are usually more accurate and always less expensive than experimental testing. Deployed effectively, engineering simulation can be used to improve a product by providing a stream of engineering data to drive the design through multiple iterations.

Engineering simulation allows engineers to see into the future, predicting the consequence of any design changes on the real-world performance of their products. More than that, simulation gives engineers the opportunity to glimpse “all possible futures,” exploring the performance of a product over the full range of operating conditions that it is likely to face in its working life, rather than just at a handful of carefully chosen “design conditions.” 

With minimal effort from the engineer, design exploration algorithms can be used to drive the design through a wide range of parametric design variables, configurations, and operating scenarios. Specifically, by employing numerical optimization technology, the design exploration algorithm can intelligently choose the parameters that are most likely to lead to an improvement. Ultimately, this process results in higher quality and more robust products that exceed customer expectations.