VALIDATION OF TRANSIENT STRUCTURAL DYNAMICS SIMULATIONS: AN EXAMPLE
The field of computational structural dynamics is on the threshold of revolutionary change. The ever-increasing costs of physical experiments coupled with advances in massively parallel computer architecture are steering the engineering analyst to be more and more reliant on numerical calculations with little to no data available for experimental confirmation. New areas of research in engineering analysis have come about as a result of the changing roles of computations and experiments. Whereas in the past the primary function of physical experiments has been to confirm or ''prove'' the accuracy of a computational simulation, the new environment of engineering is forcing engineers to allocate precious experimental resources differently. Rather than trying to ''prove'' whether a calculation is correct, the focus is on learning how to use experimental data to ''improve'' the accuracy of computational simulations. This process of improving the accuracy of calculations through the use of experimental data is termed ''model validation.'' Model validation emphasizes the need for quantitative techniques of assessing the accuracy of a computational prediction with respect to experimental measurements, taking into account that both the prediction and the measurement have uncertainties associated with them. The ''vugraph norm,'' where one overlays transparencies of simulated data and experimental data in an attempt to show consistency, is no longer an adequate means of demonstrating validity of predictions. To approach this problem, a paradigm from the field of statistical pattern recognition has been adopted [1]. This paradigm generalizes the extraction of corresponding ''features'' from the experimental data and the simulated data, and treats the comparison of these sets of features as a statistical test. The parameters that influence the output of the simulation (such as equation parameters, initial and boundary conditions, etc.) can then be adjusted to minimize the distance between the data sets as measured via the statistical test. However, the simple adjustment of parameters to calibrate the simulation to the test data does not fully accomplish the goal of ''improving'' the ability to model effectively, as there is no indication that the model will maintain accuracy at any other experimental data points.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 776325
- Report Number(s):
- LA-UR-01-1593; TRN: AH200129%%203
- Resource Relation:
- Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Mar 2001
- Country of Publication:
- United States
- Language:
- English
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