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PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY
Reanalysis of Nonlinear Structures using a Reduction Method of Combined Approximations
M. Guedri1, T. Weisser2 and N. Bouhaddi2
1Nabeul Preparatory Engineering Institute (IPEIN), M'rezgua, Nabeul, Tunisia
M. Guedri, T. Weisser, N. Bouhaddi, "Reanalysis of Nonlinear Structures using a Reduction Method of Combined Approximations", in , (Editors), "Proceedings of the Tenth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 312, 2010. doi:10.4203/ccp.93.312
Keywords: geometrical non-linearities, large displacements, structural reanalysis, combined approximations, robustness.
Complex structure optimization is an important part of the design process where the initial state of the structure is modified, sometimes in a significant way, in order to reach the best possible performance, for a given set of constraints.
By exploring as much as possible the design parameter space, optimal solutions (displacements, constraints, eigenmodes, etc.) are then calculated. These successive multiple analysis or reanalysis thus imply a large computation cost which often remains prohibitive.
The modifications applied to the structure can be of various types: topological, acting on the form of the structure or its boundary conditions; parametric, acting on the physical parameters of the structure (mass, stiffness, thickness); in a global (whole structure level) or a local way (component level).
Therefore, the aim of reanalysis methods  is to approximate the responses of a structure whose parameters have been perturbed or even modified without solving the new equilibrium equation system associated to the updated structure: only the initial solutions and the perturbed data are used.
Moreover, when the problem is non-linear, the re-actualization of the tangent stiffness matrix at each time step of the Newton-Raphson integration algorithm implies many reanalysis leading to a high computational time.
To mitigate these difficulties, one proposes a robust reduction method adapted to non-linear and large sized dynamic models. This study especially focuses on geometrical non-linearities, i.e. large displacements .
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