Adaptive SVR-HDMR Metamodeling Technique for High Dimensional Problems

Xiong, Huadi; Chen, Zhenzhong; Qiu, Haobo; Hao, Hongyan; Xu, Han

doi:10.1016/j.aasri.2012.11.017

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Since the second order function term constructed can meet the accuracy requirements, omitting the function term above the second order, the original expression can be written as follows. 34,35 Secondly, the solution method of cutting center point bold-italicx0 36 is introduced to express each term of output function ffalse(xfalse) as a function containing bold-italicx0, as follows: where false(xi,bold-italicx0ifalse) is the same as the value of bold-italicx0 except for the i -th dimension which can be sampled, that is, point false(x10,…,xi−10,xik,xi+10,…,xn0false).…”

Section: Optimization Methods and Strategymentioning

confidence: 99%

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Optimization of twisted blade of pump as turbine based on high dimensional surrogate model

et al. 2021

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In order to improve the operation efficiency of the twisted blade pump as turbine (PAT), a medium specific speed PAT was selected as the research object. The variables of the twisted blade plane blade profile were defined, the twisted blade was transformed into three plane blade profiles, and the blade profiles were parameterized by MATLAB 9.7 software. MATLAB 9.7, CFturbo 2020 and Fluent 19.2 were used to build the support vector machine-high dimensional model representation (SVM-HDMR) surrogate model function for efficiency optimization of PAT. Genetic algorithm was run on MATLAB 9.7 to optimize the surrogate model function, and the optimized blade profiles were fed back. The optimization results were verified by numerical simulation and experiment. The results show that the simulation efficiency of the PAT after optimization at the design operating point is 3.51% higher than the efficiency of the PAT before optimization, and the output power is increased by 5.3%. The test efficiency of the PAT after optimization at the design operating point is 3.4% higher than the efficiency of the PAT before optimization, and the output power is increased by 5.1%.

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