Getting the best performance out of your products can be very time consuming and challenging as with
aerodynamics very often the various interactions between different parts of your device can be very difficult to
understand. Multi-objective optimization methods on parametric geometries eliminate the guessing and use
algorithms to drive your developments to be the very best. For example on a race car, very often drag,
down-force, lift balance and cooling performance will be conflicting objectives. We will help you to find
the trade-offs in any system and get the optimal configuration according to your requirements and constraints.
The big challenge of these kind of optimizations is the parametrization of the geometry. Below is an example
of a race car with front and rear parts of the body work parametrized, ready for the Mantium treatment.
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Systems with conflicting objectives will not reach a single- value optimum. Instead, as showcased here, a pareto-front will emerge out of the optimization. This front helps to understand the trade-off between conflicting objectives. Staying with the race car example, two conflicting objectives could be lift and drag. Improving downforce will often create additional drag. Using the pareto-front, it is for example possible to find the configuration with the least amount of drag for a desired level of downforce.
If you are interested in a project using a parametric optimization to drive your products to new horizons or have questions please contact us.