Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636987
Title: Uncertainty in structural dynamic models
Author: Fonseca, J. M. R.
Awarding Body: University of Wales Swansea
Current Institution: Swansea University
Date of Award: 2006
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Abstract:
Modelling of uncertainty increases trust in analysis tools by providing predictions with confidence levels, produces more robust designs, and reduces design cycle time/cost by reducing the amount of experimental verification and validation that is required. However, uncertainty-based methods are more complex and computationally expensive than their deterministic counterparts, the characterisation of uncertainties is a non-trivial task, and the industry feels comfortable with the traditional design methods. In this work the three most popular uncertainty propagation methods (Monte Carlo simulation, perturbation, and fuzzy) are extensively benchmarked in structural dynamics applications. The main focus of the benchmark is accuracy, simplicity, and scalability. Some general guidelines for choosing the adequate uncertainty propagation method for an application are given. Since direct measurements is often prohibitively costly or even impossible, a novel method to characterise uncertainty sources from indirect measurements is presented. This method can accurately estimate the probability distribution of uncertain parameters by maximising the likelihood of the measurements. The likelihood is estimated using efficient variations of the Monte Carlo simulation and perturbation methods, which shift the computational burden to the outside of the optimisation loop, achieving a substantial time-saving without compromising accuracy. The approach was verified experimental in several applications with promising results. A novel probabilistic procedure for robust design is proposed. It is based on reweighting of the Monte Carlo samples to avoid the numerical efficiencies of resampling for every candidate design. Although not globally convergent, the proposed method is able to quickly estimate with high accuracy the optimum design. The method is applied to a numerical example, and the obtained designs are verified with regular Monte Carlo.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.636987  DOI: Not available
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