The numerical evaluation of interior acoustic responses is currently a well-established tool in vehicle body development. However, as recently as five years ago, an acoustic evaluation of a simplified simulation model required on the order of a couple of weeks of computation time on a supercomputer. Today, complex FE-simulations which have been successfully validated with test data can run overnight on a Linux cluster. This dramatic acceleration in computational speed has only been possible through the use of the Automated Multi-Level Substructuring (AMLS) algorithm, running on a parallel-processing machine. This capability now available for acoustic design suggests an automatic design improvement process in acoustic body design, analogous to the numerical gradient-based optimization algorithms, which have been widely used in the past in structural design. However, such classical optimization strategies implemented for acoustic level reduction have generally failed because of the highly nonlinear boundaries of the acoustic design space, as discussed by the author in an earlier paper (cf. [11]). Alternatively, the use of self-adaptive multi-criteria evolutionary algorithms has proven very successfully. The resulting Pareto front optimization algorithm, discussed in this paper, has been successful because it takes into account the relation between acoustic level reduction and additional mass. Two factors however complicate the acoustic optimization process considerably: Firstly, Evolutionary Algorithms require many hundreds of evaluations with a total turn-around time of several weeks. Secondly, an optimized acoustic structure does not generally constitute a robust design. This paper discusses one approach, currently being used by BMW to overcome these obstacles. The ineffi- cient computational performance is being circumvented using new numerical approaches such as the modal correction method (MCM). And a robust acoustic optimization is being realized through a multi-criteria optimization, which uses the additional objective function requirement of minimizing the acoustic level variance. Implementation of both these approaches together can be used to achieve a robust optimized acoustic vehicle design virtually overnight.


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    Title :

    Robust acoustic vehicle body design using evolutionary algorithms


    Additional title:

    Robuster akustischer Karosserieentwurf mit evolutionären Algorithmen


    Contributors:
    Kropp, A. (author)


    Publication date :

    2006


    Size :

    15 Seiten, 12 Bilder, 24 Quellen



    Type of media :

    Conference paper


    Type of material :

    Storage medium


    Language :

    English