Prior to the availability of advanced EE simulation tools, we depended on spreadsheet analysis to do simple calculations to help us study the interaction between components—for example, a control module and a switch. This approach was limited to studying specific parameters for components—such as ensuring the switch received sufficient current to make a good contact. While this form of analysis was limited, we learned a lot about the importance of data accuracy and of fully capturing and understanding the data parameters, so that we could enable accurate results, and create models that mirrored the real world.
Mirroring the Real World
Creating an analysis that is thorough, accurate and mirrors the real world is a top priority. Since the tools we use now are far more sophisticated than our early spreadsheet models, we spend a lot of resources ensuring that the models we use are accurate, whether they are sourced in-house or from suppliers. The alternative to CAE analysis—the use of physical prototypes—is becoming prohibitively expensive.
In addition to the cost of building the vehicle itself, program teams must pay to have access to the vehicle for each day of physical testing. The extensive tests that we run using CAE analysis tools would take many days of testing using physical prototypes and can't even begin to approach the number of “what-if” scenarios that CAE analysis can cover.
As well as helping to reduce development costs, CAE analysis makes a significant contribution to the robustness of our designs. We run hundreds or thousands of analyses with variations of component tolerances and look at the effects of temperature changes and aging. We can then use data from the analysis tools to isolate the component tolerances that matter, and tighten ones of interest, or make other changes that improve the robustness of the overall subsystem.
Sometimes, the quality issues we uncover have implications for our suppliers. A circuit design may pass the