It is common to give assurance in terms of the probability of success in satisfying some performance criteria and the probability of success is estimated from the mean value and variance of the performance function. The mean value and variance of the performance function is further estimated from the propagation of the input uncertainties. Therefore, it becomes a fundamental challenge to accurately estimate the uncertainty propagations from given input randomness in the probabilistic design process. Better approximation of the performance function is a key factor in enhancing the approximation quality of the mean value and the standard deviation. However, higher order approximations for the performance increase the computational cost associated. This paper presents an improved approximation method for the prediction of the mean and variance without increasing the number of function evaluations.


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    Titel :

    Prediction of Probabilistic Design Models for Uncertainty Propagation


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Gea, Hae Chang (Autor:in) / Oza, Kunjal (Autor:in)

    Kongress:

    SAE 2006 World Congress & Exhibition ; 2006



    Erscheinungsdatum :

    2006-04-03




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch


    Schlagwörter :


    Prediction of probabilistic design models for uncertainty propagation

    Gea,H.C. / Oza,K. / State Univ.of New Jersey,US | Kraftfahrwesen | 2006


    2006-01-0111 Prediction of Probabilistic Design Models for Uncertainty Propagation

    Gea, H. C. / Oza, K. / Society of Automotive Engineers | British Library Conference Proceedings | 2006


    Probabilistic Methods for Uncertainty Propagation Applied to Aircraft Design

    Green, Lawrence / Lin, Hong-Zong / Khalessi, M. | AIAA | 2002


    Uncertainty Propagation Techniques in Probabilistic Design of Multilevel Systems

    Youn, Byeng Dong / Choi, Kyung K. / Kokkolaras, Michael et al. | AIAA | 2004


    Probabilistic Methods for Uncertainty Propagation Applied to Aircraft Design

    Green, Lawrence L. / Lin, Hong-Zong / Khalessi, Mohammad R. | NTRS | 2002