دانلود رایگان مقاله روش پارامتر گذاری شکل برای بهینه سازی جهانی

Abstract

Multidisciplinary global shape optimization requires a geometric parameterization method that keeps the shape generality while lowering the number of free variables. This paper presents a reduced parameter set parameterization method based on integral B-spline surface capable of both shape and topology variations and suitable for global multidisciplinary optimization. The objective of the paper is to illustrate the advantages of the proposed method in comparison to standard parameterization and to prove that the proposed method can be used in an integrated multidisciplinary workflow. Non-linear fitting is used to test the proposed parameterization performance before the actual optimization. The parameterization method can in this way be tested and pre-selected based on previously existing geometries. Fitting tests were conducted on three shapes with dissimilar geometrical features, and great improvement in shape generality while reducing the number of shape parameters was achieved. The best results are obtained for a small number (up to 50) of optimization variables, where a classical applying of parameterization method requires about two times as many optimization variables to obtain the same fitting capacity. The proposed shape parameterization method was tested in a multidisciplinary ship hull optimization workflow to confirm that it can actually be used in multiobjective optimization problems. The workflow integrates shape parameterization with hydrodynamic, structural and geometry analysis tools. In comparison to classical local and global optimization methods, the evolutionary algorithm allows for fully autonomous design with an ability to generate a wide Pareto front without a need for an initial solution.

6. Conclusion

Two proposed reduced parameter set (RPS) shape parameterization methods have been compared to classical B-spline parameterization. It has been shown that the proposed shape parameterization methods are able to keep the shape generality while lowering the number of shape parameters on three mutually different test shapes. This means that for the same number of variables, the proposed RPS method could obtain a solution closer to the global optimum or likewise a reduction of the computational time could be obtained while keeping the same shape generality. Non-linear fitting method has shown an ability to be used as a performance prediction tool for shape parameterization before the actual optimization. Both proposed RPS methods have shown good results for DTMB shape containing a bulb-like sonar dome where a reduction of shape variables by a factor of three was obtained. Some difficulties have appeared with describing a hull shape with a normal pointing in the negative y direction, but the RPS methods have shown advantages in all cases. While the modified RPS method has kept the advantage over classical parameterization for other test cases and number of shape variables, the basic RPS method shown good results only for low number (<50) of shape parameters.