Find out how multidisciplinary simulation and design exploration can help to solve the toughest challenges that the automotive industry has to offer.
Not all engineering simulation tools are created equal. In order to provide a constant stream of relevant engineering data, simulation software must be able to meet certain key criteria.
Click on an icon to learn more.
Multidisciplinary Design Exploration leads to improved product performance
Engineering is a process of continual improvement. Engineers are required to improve products step by step, by making incremental changes that are intended to make the product somehow “better”: Faster; Stronger; Lighter; More efficient; Less expensive.
Every successful product is the result of many design iterations, each of which improves the performance of the product in some manner. While the amount of improvement delivered by each “design upgrade” is often modest, when stacked together over tens, hundreds, or even thousands of design iterations this process delivers products whose performance in the real world is significantly improved over the original design.
However, for every successfully implemented design improvement, there are many more “failed iterations” that either delivered no improvement in product performance or somehow made it worse. For every hard won improvement in a product, there are an almost infinite number of ways to break it. This is the challenge of engineering: to navigate that infinite tree of potential design improvements, making those choices that improve the product (towards some final goal), and rejecting the far more numerous “wrong choices” that would make it worse.
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.