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- 저자 : Fuzhang Wang (검색결과 2 건)
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Fuzhang Wang,Tanveer Sajid,Nek Muhammad Katbar,Wasim Jamshed,Usman,Mohamed R Eid,Assmaa Abd-Elmonem,Siti Suzilliana Putri Mohamed Isa,Sayed M El Din 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4
The goal of this research is to provide a novel conceptual base for ternary hybridity nanofluids that will improve heat transmission. This model illustrates how to produce heat conduction that is superior to the hybridity nanofluid. The ternary hybridity nanofluid is made by suspending three distinct kinds of nanostructures (TiO2, Al2O3, and SiO2) in ethylene glycol with varying physical and chemical linkages. The combination of these nanoparticles aids in the decomposition of hazardous compounds, environment cleansing, and the cooling of other equipment. This article describes the ternary hybrid nanofluids such as the conductivity of thermal and electrical, specific heat capacitance, viscosity, and density. The ordinary differential equations for the liquid and temperature are solved utilizing the Keller box method (KBM). The main finding discovered that the tri-hybrid nanofluid transferred more heat compared to the hybrid nanofluid. The velocity and temperature field diminishes for these ranges of parameters: 0≤γ≤2 (velocity slipγ ) and 0≤γ1≤2 (temperature slip γ1). The Darcy–Forchheimer Fr ranges between 0.5≤Fr≤1.4 diminishes the fluid velocity. The fluid moving subjected to a rotating cone has various applications in engineering like cone clutch, steam generators, loudspeaker cooling, lubricating grease for seals, hydrology, geosciences, etc.
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A Text Clustering Algorithm based on Weeds and Differential Optimization
Lipeng YANG,Fuzhang WANG,Chunmei FAN 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.12
Invasive weed optimization (IWO) is a swarm optimization algorithm with both explorative and exploitive power where the diverisity of the population is obtained by allowing the reproduction and mutation of individuals with poor fitness .Differential optimization algorithm is a random parallel algorithm according to a vector change that can make individuals change toward outstanding individuals with global convergence. For k-means algorithm , the traditional algorirhm is prone to get stuck at local optimum and is sensitive to random initialization. Based on the aforementiond background a novel optimization algorithm based hybriding DE and IWO which denoted IWODE-KM is employed to optimize the parameters of k-means and is further applied to chinese text clustering. Experiment results shows that the proposed method outperforms both of its ancestors.
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