This paper proposes a hybrid vertex-centered fi- nite volume/finite element method for solution of the two di- mensional （2D） incompressible Navier-Stokes equations on unstructured grids. An incremental pressure fra...This paper proposes a hybrid vertex-centered fi- nite volume/finite element method for solution of the two di- mensional （2D） incompressible Navier-Stokes equations on unstructured grids. An incremental pressure fractional step method is adopted to handle the velocity-pressure coupling. The velocity and the pressure are collocated at the node of the vertex-centered control volume which is formed by join- ing the centroid of cells sharing the common vertex. For the temporal integration of the momentum equations, an im- plicit second-order scheme is utilized to enhance the com- putational stability and eliminate the time step limit due to the diffusion term. The momentum equations are discretized by the vertex-centered finite volume method （FVM） and the pressure Poisson equation is solved by the Galerkin finite el- ement method （FEM）. The momentum interpolation is used to damp out the spurious pressure wiggles. The test case with analytical solutions demonstrates second-order accuracy of the current hybrid scheme in time and space for both veloc- ity and pressure. The classic test cases, the lid-driven cavity flow, the skew cavity flow and the backward-facing step flow, show that numerical results are in good agreement with the published benchmark solutions.展开更多
Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment pro-vided by Apache Hadoop can distribute and process the data over differen...Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment pro-vided by Apache Hadoop can distribute and process the data over different desti-nation systems.In this paper,the Hadoop cluster with four nodes integrated with RHadoop,Flume,and Hive is created to analyze the tweets gathered from the Twitter stream.Twitter stream data is collected relevant to an event/topic like IPL-2015,cricket,Royal Challengers Bangalore,Kohli,Modi,from May 24 to 30,2016 using Flume.Hive is used as a data warehouse to store the streamed tweets.Twitter analytics like maximum number of tweets by users,the average number of followers,and maximum number of friends are obtained using Hive.The network graph is constructed with the user’s unique screen name and men-tions using‘R’.A timeline graph of individual users is generated using‘R’.Also,the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized sup-port vector neural network(OSVNN)classification model.To attain better classi-fication accuracy,the performance of SVNN is enhanced using a chimp optimization algorithm(ChOA).Extracting the users’emotions toward an event is beneficial for prediction,but when coupled with visualizations,it becomes more powerful.Bar-chart and wordcloud are generated to visualize the emotional analysis results.展开更多
基金supported by the Natural Science Foundation of China (11061021)the Program of Higher-level talents of Inner Mongolia University (SPH-IMU,Z200901004)the Scientific Research Projection of Higher Schools of Inner Mongolia(NJ10016,NJ10006)
文摘This paper proposes a hybrid vertex-centered fi- nite volume/finite element method for solution of the two di- mensional （2D） incompressible Navier-Stokes equations on unstructured grids. An incremental pressure fractional step method is adopted to handle the velocity-pressure coupling. The velocity and the pressure are collocated at the node of the vertex-centered control volume which is formed by join- ing the centroid of cells sharing the common vertex. For the temporal integration of the momentum equations, an im- plicit second-order scheme is utilized to enhance the com- putational stability and eliminate the time step limit due to the diffusion term. The momentum equations are discretized by the vertex-centered finite volume method （FVM） and the pressure Poisson equation is solved by the Galerkin finite el- ement method （FEM）. The momentum interpolation is used to damp out the spurious pressure wiggles. The test case with analytical solutions demonstrates second-order accuracy of the current hybrid scheme in time and space for both veloc- ity and pressure. The classic test cases, the lid-driven cavity flow, the skew cavity flow and the backward-facing step flow, show that numerical results are in good agreement with the published benchmark solutions.
文摘Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints.A parallel computational environment pro-vided by Apache Hadoop can distribute and process the data over different desti-nation systems.In this paper,the Hadoop cluster with four nodes integrated with RHadoop,Flume,and Hive is created to analyze the tweets gathered from the Twitter stream.Twitter stream data is collected relevant to an event/topic like IPL-2015,cricket,Royal Challengers Bangalore,Kohli,Modi,from May 24 to 30,2016 using Flume.Hive is used as a data warehouse to store the streamed tweets.Twitter analytics like maximum number of tweets by users,the average number of followers,and maximum number of friends are obtained using Hive.The network graph is constructed with the user’s unique screen name and men-tions using‘R’.A timeline graph of individual users is generated using‘R’.Also,the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized sup-port vector neural network(OSVNN)classification model.To attain better classi-fication accuracy,the performance of SVNN is enhanced using a chimp optimization algorithm(ChOA).Extracting the users’emotions toward an event is beneficial for prediction,but when coupled with visualizations,it becomes more powerful.Bar-chart and wordcloud are generated to visualize the emotional analysis results.
