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Xia, Bin,Ren, Ziyan,Zhang, Yanli,Koh, Chang-Seop The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.5
In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.
Bin Xia,Baatar, Nyambayar,Ziyan Ren,Chang-Seop Koh IEEE 2015 IEEE transactions on magnetics Vol.51 No.3
<P>A dynamic Taylor Kriging (DTK) is newly developed and combined with a multi-objective differential evolution algorithm to get a numerically efficient multi-objective optimization strategy. In the DTK, basis functions are not predefined but optimally selected so that the fitting error with the given sampling data may be minimized. In the developed multi-objective optimization algorithm, the DTK provides predicted objective function values as an alternative to direct finite-element analysis. The effectiveness of the proposed DTK and multi-objective optimization strategy are verified through applications to analytic example and TEAM 22.</P>
Bin Xia,Ziyan Ren,Chang-Seop Koh IEEE 2014 IEEE transactions on magnetics Vol.50 No.2
<P>This paper presents a multi-objective robust optimization strategy assisted by the surrogate model. In order to guarantee the accurate response prediction, the performances of three different Kriging surrogate models, ordinary Kriging, first-order universal Kriging (UK), and second-order UK, are investigated through analytical benchmark functions. Once the accurate model is constructed, the performance analysis can be efficiently approximated during optimization process. Furthermore, the robustness against uncertainty is evaluated by the worst-case scenario through applying optimization technique to the approximated model in the uncertainty set. The proposed algorithm is validated through one electromagnetic application, a robust version of the TEAM 22.</P>
Bin Xia,Ziyan Ren,Yanli Zhang,Chang-Seop Koh 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.5
In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.
Study on Crystallization Kinetics of Dynamically-Vulcanized PP/EPDM Blends
Bin Yang,Yan-Li Deng,Xia Ru,Ji-Bin Miao,Ming Cao,Jia-Sheng Qian,Li-Fen Su,Peng Chen,Jing-Wang Liu,La-Xia Wu,Tao Pang 한국고분자학회 2016 폴리머 Vol.40 No.4
Two types of β nucleating agents (β-NAs), aryl dicarboxylic acid amide (TMB-5) and diphenyl phthalate diamine (NT-C), were adopted to modify the polypropylene (PP)/ethylene propylene diene monomer (EPDM) blends, which were prepared by dynamic-vulcanization technology. Wide angle X-ray diffraction (WAXD) and differential scanning calorimetry (DSC) were used to study the crystallization kinetics of PP. Our results showed that the addition of β-NAs can considerably increase the crystallization temperature, and significantly decrease the spherulite size of β-PP (L300). The Jeziorny analysis showed there were ~82% and ~89% of relative crystallinity generated from the primary crystallization in the composites containing TMB-5 and NT-C, respectively. The crystallization half time (t0.5) showed that NT-C improved the overall crystallization rate more effectively than TMB-5. In addition, the peaks of the relative crystallization rate curves were shifted towards higher temperature by 14 and 9℃ with the addition of TMB-5 and NT-C, respectively.