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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 86
Edited by: B.H.V. Topping
Paper 43

Reliability Prediction in Fluid-Structure Interaction Problems

J.E. Rojas12, O. Bendaou31, A. El Hami1 and D.A. Rade2

1Mechanics Laboratory of Rouen, National Institute for Applied Sciences, Rouen, France
2School of Mechanical Engineering, Federal University of Uberlândia, Brazil
3Mechanical Department, School of Engineers of Mohammadia, Rabat-Agdal, Morocco

Full Bibliographic Reference for this paper
J.E. Rojas, O. Bendaou, A. El Hami, D.A. Rade, "Reliability Prediction in Fluid-Structure Interaction Problems", in B.H.V. Topping, (Editor), "Proceedings of the Eleventh International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 43, 2007. doi:10.4203/ccp.86.43
Keywords: fluid-structure interaction, stochastic analysis, perturbation, reliability analysis, heuristic based reliability method.

Reliability analysis (RA) intends to find the best compromise between cost and safety and to supply guidelines for carrying out reliable and cost-effective projects, accounting for the statistical variability of the design parameters. The understanding of the interaction mechanisms between a fluid and an elastic solid has a capital importance in several industrial applications knowing that the fluid generates a structural deformation and, or the movement of a solid provokes the movement of the fluid. These applications require an effective model of the mechanical as shown [1].

One of the main hypotheses in the study of mechanical systems is that the model is deterministic. However experimental works show the limitations of such assumption. This is because there are always differences between what can be calculate and what can be measure due mainly to the uncertainties parameters, which has a considerable impact on the vibrating behaviour of mechanical systems. In order to account for that, the Muscolino perturbation method [3] is extended to dynamic stochastic analysis of coupled fluid-structure finite element models.

Regarding optimization, it is well known that the solution of RA problems by using classical methods, such as first and second order reliability method, is a difficult task due to the existence of multiple most probable points of the limit state functions, mainly in the cases where a large number of design variables are involved. Aiming to eliminate these difficulties, this paper suggests using a modified approach based on first and second order reliability method (FORM and SORM) for the determination of reliability index and design points, based on ant colony optimization (ACO). In this context, an algorithm is proposed which is able to solve the global search optimization in reliability problems by using a Heuristic Based Reliability Method (HBRM) [2].

The RA and stochastic methods are illustrated by means of numerical simulations in 2-D and 3-D dynamic fluid-structure interaction problems. In these applications, it is assumed that the probability distributions of system parameters are known and the mean and the standard deviation of the first natural frequency are calculated. The perturbation methods allied to RA are applied to fluid-structure finite element models. RA methodology integrates FORM, SORM and HBRM (specifically the ACO) with finite element analysis. The results demonstrate the applicability, accuracy and efficiency of the proposed methodology. The results tend to confirm the potential of these methods.

A. El Hami, D. Borza, J.E. Souza de Cursi, "Vibroacoustic analysis of cyclic structures by using dof's size reduction and holographic measurements", Shock and Vibration, 13(4), 355-366, 2006.
J.E. Rojas, F.A. Viana, D. Rade, "Natured-inspired Optimization Techniques Applied to Structural Reliability Analysis", 7th Multidisciplinary International Conference Quality and Reliability (RAMS), March 20-22, 2007, Tangier, Morocco, 2007.
G. Muscolino, G. Ricciardi, N. Impollonia, "Improved dynamic analysis of structures with mechanical uncertainties under deterministic input", Probabilistic Engineering Mechanics, 15, 199-212, 1999. doi:10.1016/S0266-8920(99)00021-1

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