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  • 1.
    Lindström, David
    University West, Department of Engineering Science, Division of Mechanical Engineering and Natural Sciences.
    Evaluation of a Surrogate Based Method for Global Optimization2015In: International Journal of Computer, Electrical, Automation, Control and Information Engineering, E-ISSN 1307-6892, Vol. 9, no 7, p. 1636-1642Article in journal (Refereed)
    Abstract [en]

    We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cyclic parameters of the optimization method to get a balance between local and global search. We also discuss the eventual problem with Runge oscillations in the response surface.

  • 2.
    Lindström, David
    University West, Department of Engineering Science.
    Robustness analysis of airfoil performance: DETC2010-282612010In: Proceedings of the ASME 2010 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference: August 15-18, 2010, Montreal, Quebec, Canada, ASME, the American Society of Mechanical Engineers , 2010, p. 1-6Conference paper (Refereed)
    Abstract [en]

    We demonstrate a technique to evaluate the aerodynamic robustness of a given blade profile which it is exposed to stochastic geometrical variation. The technique is based on random fields, with geometrical deviations continuously defined over the entire structure, with a prescribed statistical distribution function and a given correlation between these deviations. Control points are defined on the blade surface to model the blade geometry disturbances. At each control point a stochastic deviation is defined, which acts in the normal direction of the blade. By modeling disturbances in the normal direction instead of in the separate Cartesian directions, we automatically reduce the number of stochastic variables by a factor two. The perturbation variables are transformed via Karhunen-Loève eigenvalue decomposition, giving stochastically independent variables. The robustness is finally estimated by a Monte Carlo simulation, where computational fluid dynamic simulations are performed to evaluate the resulting change in blade performance for given geometrical perturbations.

  • 3.
    Lindström, David
    et al.
    University West, Department of Engineering Science, Division of Mechanical Engineering and Natural Sciences.
    Eriksson, Kenneth
    University West, Department of Engineering Science, Division of Mechanical Engineering and Natural Sciences.
    A surrogate model based global optimization method2008In: 38th International Conference on Computers and Industrial Engineering 2008, 2008, p. 226-232Conference paper (Refereed)
    Abstract [en]

    A new surrogate model based algorithm for global optimization is derived. A metamodel such as a kriging model or a radial basis function model is used to build an interpolant of the objective function. Evaluation points are chosen in such a way that local search and global exploration is balanced. Instead of putting the next point exactly where it is most likely to find the global optimum, the new method also prepares for steps to come by minimizing the total uncertainty of the interpolated function within the most interesting areas of the search space.

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