Validation Of Transient Structural Dynamics Simulations
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VALIDATION OF TRANSIENT STRUCTURAL DYNAMICS SIMULATIONS
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.
Process-Oriented Analysis and Validation of Multi-Agent-Based Simulations
Author: Nicolas Denz
language: en
Publisher: Logos Verlag Berlin GmbH
Release Date: 2014-12-31
In multi-agent-based simulation (MABS) the behavior of individual actors is modeled in detail. The analysis and validation of these models is rated as difficult and requires support by innovative techniques and tools. Problems include model complexity, the amount and often qualitative representation of simulation results, and the typical dichotomy between microscopic modeling and macroscopic observation perspectives. In recent years, data mining has been increasingly applied as a support technique in this context. A particularly promising approach is found in the field of process mining. Due to its rooting in business process analysis, process mining shares several process- and organization-oriented analysis perspectives and use cases with agent-based modeling. This thesis proposes a conceptual framework for the systematic application of process mining to the analysis and validation of MABS. As a foundation, agent-oriented analysis perspectives and simulation-specific use cases are identified and complemented with methods, techniques, and results from the literature. A partial formalization of perspectives and use cases is sketched by utilizing concepts from process modeling and software engineering. Beyond the conceptual work, process mining is applied in two case studies related to different modeling and simulation approaches.
Topics in Model Validation and Uncertainty Quantification, Volume 5
Author: Todd Simmermacher
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-05-30
Topics in Model Validation and Uncertainty Quantification, Volume : Proceedings of the 31st IMAC, A Conference and Exposition on Structural Dynamics, 2013, the fifth volume of seven from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Uncertainty Quantification & Propagation in Structural Dynamics Robustness to Lack of Knowledge in Design Model Validation