The uncertainty-based sequential design of experiment method based on Stochastic Kriging metamodel
WANG Bo1, GEA Haechang2, BAI Jun-qiang3, ZHANG Yu-dong1, GONG Jian1, ZHANG Wei-min1
1. Research and Development Center, China Academy of Aerospace Aerodynamics, Beijing 100074, China;
2. Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscataway NJ 08854;
3. School of Aeronautics, Northwestern Polytechnical University of China, Xi'an 710072, China
The research on uncertainty requires many duplications and undoubtedly it puts forward a giant challenge to numerical simulations which is time-consuming. The amount of computation in the study of uncertainty can be effectively reduced through design of experiment method, but the current researches on design of experiment method about uncertainty mainly concentrate on traditional methods. Aiming at the problem, in order to address the problem and attain an accurate uncertainty assessment through reasonably allocating computational resources, the sequential design of experiment method with three stages was constructed based on the Stochastic Kriging metamodel with finite sampling. At the beginning, the criterion to choose the predetermined number and distribution of samples to attain certain accuracy of stochastic metamodel was proposed through the simplification of IMSE at specific sampling states. In addition, the criterion to obtain the optimum based on the predetermined information was also derived to simultaneously take the state and distribution of samples into account. Moreover, traditional methods were used to do the comparison with the proposed method, and the feasibility and advantages of proposed method were verified by examples with uncertainty, in which stochastic metamodel with more accuracy was achieved by using the same amount of sampling as traditional methods.
WANG Bo, GEA Haechang, BAI Jun-qiang, ZHANG Yu-dong, GONG Jian, ZHANG Wei-min. The uncertainty-based sequential design of experiment method based on Stochastic Kriging metamodel. Chinese Journal of Engineering Design, 2016, 23(6): 530-536.
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