Year: 2001
Type: report
Abstract: Our increased dependence on computer models leads to the natural question -how do we know whether a computer model is valid?Models have traditionally been tested against experimental measurements through simple comparisons such as x-y plots, scatter plots, or two-dimensional contour plots.We are then faced with two questions: When is the agreement between experimental measurement and model prediction good enough, and how should we quantify this agreement?Here we present the use of statistical methods to develop metrics for this agreement.We start with the simplest case in shock wave physics for which one can use the validation results directly to develop probability-based metrics.As an example, common statistical methods are used to test the validity of CTH hydrocode predictions of shock wave speed using experimental data for aluminum on aluminum impact.We then move to more complex multivariate validation scenarios for which the model predictive uncertainty is characterized using propagation of uncertainty analysis.Methodology for the multivariate case is developed and examples are presented using CTH predictions of shock wave speed.
DOI: 10.2172/782589
Authors: RICHARD HILLS, TIMOTHY TRUCANO
Institution: Office of Scientific and Technical Information
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W101144848