Minimization of vortex induced vibrations using Surrogate Based Optimization

• Published in: Structural and multidisciplinary optimization Vol. 52; no. 4; pp. 717 - 735
• Main Authors: Farias Filho, Ulisses P., E. Antunes, Alessandro R., A. Bastos, Silvana M., M. Lyra, Paulo R.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-10-2015; Springer Nature B.V
• Subjects: Acoustic excitation; Acoustic noise; Algorithms; Computational fluid dynamics; Computational Mathematics and Numerical Analysis; Computer simulation; Cylinders; Design optimization; Design parameters; Engineering; Engineering Design; Finite element method; Flat plates; Fluid flow; Fluid-structure interaction; Incompressible flow; Kriging; Local optimization; Mathematical models; Optimization techniques; Plates (structural members); Quadratic programming; Regularization; Research Paper; Robustness (mathematics); Theoretical and Applied Mechanics; Vortex-induced vibrations; VIV; Surrogate based optimization; FSI; Kriging; ALE FEM formulation
• ISSN: 1615-147X; 1615-1488
• DOI: 10.1007/s00158-015-1264-6

In this work we consider the application of optimization techniques in fluid-structure interaction (FSI) problems. An arbitrary Lagrangian Eulerian (ALE) finite element formulation, based on a Fractional Step Method is extended to deal with incompressible flow problems with moving interfaces. The vortex induced vibrations (VIV) phenomena are evaluated, and the reduction of such vibrations is attempted by using an acoustic excitation on the surface of the cylinder or by positioning a flat plate behind the cylinder, with the optima design parameters obtained through the minimization of the cylinder vibration. As the cost of FSI numerical simulation can be very high it is generally not feasible to couple the simulator directly to the optimizer. Therefore, a cheap surrogate model is used to capture the main trends of the objective and constraint functions. In this work we adopt Kriging data fitting approximation to build surrogate models to be used in the context of local optimization. The Sequential Approximate Optimization (SAO) strategy is used to solve the problem as a sequence of local problems. A trust region based framework is employed to adaptively update the design variable space for each local optimization. Sequential Quadratic Programming (SQP) is the algorithm of choice for the local problems. This optimizer will operate solely on the surrogates, which is smooth and also allows for the gradient computation. The integrated approach presented for optimization of FSI problems using surrogate models and the proposed strategy for sampling reuse leads to a robust and efficient tool, which were successful in solving the model problems analyzed. Also the proposed tool proved to be accurate and its performance confirms the efficient regularization of simulator numerical noise.