Stochastic Simulation Method for CAE Vehicle Dynamics Evaluation and Design Robustness Analysis 2010-36-0251
This work presents a statistical approach for simulation based
on Monte Carlo method. As an exercise of the method a CAE vehicle
dynamics model was specifically created to evaluate the likelihood
to meet a given target driving a maneuver for given inputs
variations. In the exercise, three different inputs were chosen as
stochastic inputs (also called noise factors) and all relevant
information about their statistics has been raised, based in
components information. The chosen inputs are: front/rear dampers
curves, front/rear ride heights and tire surface temperature.
A brief description of the Monte Carlo technique is presented.
The choice of this method is due to the reduced number of
simulations required to have a given accuracy in comparison with
other approaches, especially for multivariable system.
As output variable for the exercise, the tire patch height was
chosen and the resulting probability density function of it is
presented. Two different setups were tested in order to evaluate
the robustness level of each. A third test was also done having as
a parameter the height of the center of gravity of the vehicle.
The method has proven to be a powerful design tool. One verified
advantage of the method is its outcome, not normally obtained from
conventional deterministic CAE simulations: the likelihood of
achieving a specific target and the design robustness to inputs