Multi-Objective Optimization of Vehicle Handling & Comfort performances with modeFRONTIER and MSC.ADAMS Car
EnginSoft
22-23 October 2012 Pacengo del Garda
(VR) - Italy

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EnginSoft Users' Meeting 2007
Le Tecnologie CAE nell'Industria

Multi-Objective Optimization of Vehicle Handling & Comfort performances with modeFRONTIER and MSC.ADAMS Car

Winge Christina - Fiat Group Automobiles

Abstract

In order to optimize two main aspects of a vehicle’s performance the Multibody environment MSC.ADAMS/Car and a detailed full-vehicle model has been linked to modeFRONTIER to allow a Multi-Objective Optimization of both features. The Handling and Comfort performance of a vehicle represents important characteristics such as the stability and the level of ride-comfort perceived while driving the car. From an optimization point of view the two aspects usually are in conflict with each other – while from the ride point of view the requirement typically is high suspension flexibility an optimum handling performance often equals an extremely stiff set-up of the vehicle suspensions.

Therefore a Multi-Objective Optimization concerning the Handling and Comfort performance of a vehicle is particularly challenging. The goal is to reach ever more severe targets without penalizing other aspects of the vehicle performance and the key is to study in detail and determine the influence of vehicle suspensions’ single parameters (suspension attachment stiffnesses, bumpstop characteristics, shock absorbers characteristics, etc. …) and the correlations between them and the targets as well as other parameters of interest to improve both aspects. In the case of the Handling-Comfort optimisation analyzed in this paper regarding a rear suspension elastic characteristics the result was an significant improvement of most performances starting from an already manually optimized set-up. Making use of modeFRONTIER enabled more focus on the results than on modifications of the model and the launch of multiple analysis and helped to gain a deeper understanding of the influence from input variables and the correlation between all input data and performances.


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