"Prediction is very difficult, especially if it's about the future"
- Niels Bohr, Nobel laureate in Physics
Whether you like it or not, as a simulation engineer you are in the prediction game. Put simply, your job is to predict how an abstract design would perform in the real world, hopefully accounting for the most challenging operating conditions that it would likely experience during its working life.Compared with other professional forecasters such as economists, television meteorologists or political commentators, the audience for engineering predictions is more critical and less likely to forgive. While incorrect weather forecasts are quickly forgotten (at least those that don't involve hurricanes), and one rarely takes economists seriously, the cost of getting an engineering prediction wrong can be enormous. The failure of a product in service can have serious consequences, particularly in the case of safety critical applications where unforeseen failure can result in injury or loss-of-life. Even in less serious circumstances, the unexpected failure of a product can act to de-motivate consumers, damaging brand reputation, potentially incurring large warranty expenses.
The problem is that uncertainty is a fundamental part of all prediction; no engineering prediction is perfect and no simulation model is a complete representation of the real world scenario. Every model is based upon a set of underlying assumptions that allows it to be solved numerically, but ultimately influences the accuracy of the prediction. As engineers, we are responsible for acknowledging and understanding the uncertainty in our predictions and, wherever possible, to try and minimize that uncertainty through the application of judicious modeling assumptions.