By Sez Atamturktur, Tyler Schoenherr, Babak Moaveni, Costas Papadimitriou
Model Validation and Uncertainty Quantifi cation, quantity three. lawsuits of the thirty fourth IMAC, A convention and Exposition on Dynamics of Multiphysical platforms: From energetic fabrics to Vibroacoustics, 2016, the 3rd quantity of ten from the convention brings jointly contributions to this significant zone of analysis and engineering. Th e assortment provides early findings and case experiences on primary and utilized features of Structural Dynamics, together with papers on:
• Uncertainty Quantifi cation & version Validation
• Uncertainty Propagation in Structural Dynamics
• Bayesian & Markov Chain Monte Carlo Methods
• sensible purposes of MVUQ
• Advances in MVUQ & version Updating
• Robustness in layout & Validation
• Verifi cation & Validation Methods
Read or Download Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 34th IMAC, A Conference and Exposition on Structural Dynamics 2016 PDF
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Additional resources for Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 34th IMAC, A Conference and Exposition on Structural Dynamics 2016
Sample text
The number of iterations was chosen to be 10,000. 3 show the graphical results, histogram of samples of parameters and the Markov chains of the samples. 2 shows the frequency of the samples for all three parameters of interest, stiffness, k, damping coefficient, c and standard deviation of noise, . It can be observed that the most frequent values are k D 4:481 105 N/m, c D 21:5 Ns/m and D 0:85. In Fig. 3 one can check the stationary Markov chains of the samples for all three parameters. The plots show how the Markov chains efficiently explored the space of each parameter.
As discussed previously, the model was simplified to a single noise variance for the entire system. A next step might be to include in the simulation for each level of the structure having its own noise standard deviation. Due to the fact that the three-noise-standard-deviation model might be overly complex, one might like to look at it in a Bayesian context; as this is not possible with the MH algorithm, the RJMCMC methods will be used. Although the mathematical approximation of the real structure is simplified,the proposed approach gives robust results still.
0/, drives the covariance matrices of the state and output prediction errors to zero. We speculate that it is due to the almost zero probability of drawing sequences of random numbers fwn gNnD1 and fvn gNnD1 such that the actual system output y1 : N exactly matches the measured system output zO 1WN . Therefore, the closest approximation to the data vector zO 1WN for a given value of the structural model parameters ™O s is attained by the output of the underlying deterministic state-space model where all the wn and vn are zero.