Reliability-based design optimization accounts for variation. However, it assumes that statistical information is available in the form of fully defined probabilistic distributions. This is not true for a variety of engineering problems where uncertainty is usually given in terms of interval ranges. In this case, interval analysis or possibility theory can be used instead of probability theory. This paper shows how possibility theory can be used in design and presents a computationally efficient sequential optimization algorithm. The algorithm handles problems with only uncertain or a combination of random and uncertain design variables and parameters. It consists of a sequence of cycles composed of a deterministic design optimization followed by a set of worst-case reliability evaluation loops. A crank-slider mechanism example demonstrates the accuracy and efficiency of the proposed sequential algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An Efficient Possibility-Based Design Optimization Method for a Combination of Interval and Random Variables


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    SAE World Congress & Exhibition ; 2007



    Erscheinungsdatum :

    2007-04-16




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch





    2007-01-0553 An Efficient Possibility-Based Design Optimization Method for a Combination of Interval and Random Variables

    Zhou, J. / Mourelatos, Z. P. / Society of Automotive Engineers | British Library Conference Proceedings | 2007



    Design Optimization with Imprecise Random Variables

    Herrmann, Jeffrey W. | SAE Technical Papers | 2009