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Accepted Manuscripts

BASIC VIEW  |  EXPANDED VIEW
research-article  
Daniel J. Segalman, Thomas Paez and Lara Bauman
J. Verif. Valid. Uncert   doi: 10.1115/1.4036180
A systematic approach to defining margin in a manner that incorporates statistical information and accommodates data uncertainty, but does not require assumptions about specific forms of the tails of distributions is developed. A margin that is insensitive to the character of the tails of the relevant distributions (Tail Insensitive Margin, TIM) is defined. This is complemented by the calculation of probability of failure were the load distribution augmented by a quantity equal to the TIM. This approach avoids some of the perplexing results common to traditional reliability theory where, on the basis of very small amounts of data, one is led to extraordinary claims of infinitesimal probability of failure. Additionally, this approach permits a more meaningful separation of statistical and engineering issues.
TOPICS: Uncertainty, Failure, Probability, Reliability theory, Separation (Technology), Stress
research-article  
Dan Ao, Zhen Hu and Sankaran Mahadevan
J. Verif. Valid. Uncert   doi: 10.1115/1.4036182
Validation of dynamics model prediction is challenging due to the involvement of various sources of uncertainty and variations among validation experiments and over time. This paper investigates quantitative approaches for the validation of dynamics models using fully characterized experiments, in which both inputs and outputs of the models and experiments are measured and reported. Existing validation methods for dynamics models use feature-based metrics to give an overall measure of agreement over the entire time history, but do not capture the model's performance at specific time instants or duration; this is important for systems that operate in different regimes in different stages of the time history. Therefore, three new validation metrics are proposed by extending the model reliability metric (a distance-based probabilistic metric) to dynamics problems. The proposed three time-domain model reliability metrics consider instantaneous reliability, first-passage reliability, and accumulated reliability. These three reliability metrics that perform time-domain comparison overcome the limitations of current feature-based validation metrics and provide quantitative assessment regarding the agreement between the simulation model and experiment over time from three different perspectives. The selection of validation metrics from a decision-making point of view is also discussed. Two engineering examples, including a simply supported beam under stochastic loading and the Sandia National Laboratories structural dynamics challenge problem, are used to illustrate the proposed time-domain validation metrics.
TOPICS: Dynamics (Mechanics), Model validation, Reliability, Structural dynamics, Decision making, Simply supported beams, Simulation models, Uncertainty

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