Uncertainty quantification in computational structural dynamics : A new paradigm for model validation

Abstract

We present an overview of new research efforts underway at Sandia National Laboratories to understand the sources of uncertainty and error in computational structural dynamics and other physics simulations, and to quantify their effects on predictive accuracy. In order to establish confidence in computational simulations as these simulations move further from the established experimental database, a new approach to modeling and simulation validation is needed. In particular, when simulations are used to qualify the safety and reliability of systems, we believe that validation should be based upon a comprehensive quantification of uncertainties and errors from all phases of the modeling and simulation process. Uncertainty and error quantification is a two-step process, the first step being the identification of all uncertainty and error sources in each phase of modeling and simulation. The second step is the assessment and propagation of the most significant uncertainties and errors through the phases of the modeling and simulation process to the predicted response quantities. This paper outlines the phases of modeling and simulation, the distinction between uncertainty and error, and a categorization of uncertainty and error sources in each phase of modeling and simulation. We also address the question of how uncertainties in the form or structure of the model might be assessed using multiple models. Examples from linear structural dynamics are given to illustrate these concepts.

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