Experimental study on flow characteristics of tetrahydrofuran hydrate slurry in pipelines

Tetrahydrofuran（THF） was selected as the substitute to study the flow behaviors and the mechanism of the hydrates blockage in pipelines.The slurrylike hydrates and slushlike hydrates are observed with the formation of hydrates in pipeline.There is a critical hydrate volume concentration of 50.6% for THF slurries and pipeline will be free of hydrate blockage while the hydrate volume concentration is lower than the critical volume concentration;otherwise,pipeline will be easy to be blocked.Fully turbulent flow occurs and friction factors tend to be constant when the velocity reaches 1.5 m/s.And then,constant values of friction factors that depend on the volume concentrations in the slurry were regressed to estimate the pressure drops of THF hydrate slurry at large mean velocity.Finally,a safe region,defined according to the critical hydrate volume concentration,was proposed for THF hydrate slurry,which may provide some insight for further studying the natural gas hydrate slurries and judge whether the pipeline can be run safely or not.

FLUID FLOW SEPARATION CHARACTER ON NOVEL HYBRID JOURNAL BEARING

The influence of the structure and running parameters of a novel spiral oil wedge hybrid journal bearing on the fluid flow trace is investigated. The governing equation of the flow trace of lubricant is set up, and the simulation is carried out by using finite difference method. The results show that the lubricant flow status and end leakage quantity are greatly influenced by spiral angle,and that the rotating speed has little influence on the flow status. With advisable geometry design, the separation of lubricant between different oil wedges can be obtained, which can decrease the temperature rise effectively.

A Character Flow Framework for Multi-Oriented Scene Text Detection

Scene text detection plays a significant role in various applications,such as object recognition,document management,and visual navigation.The instance segmentation based method has been mostly used in existing research due to its advantages in dealing with multi-oriented texts.However,a large number of non-text pixels exist in the labels during the model training,leading to text mis-segmentation.In this paper,we propose a novel multi-oriented scene text detection framework,which includes two main modules:character instance segmentation(one instance corresponds to one character),and character flow construction(one character flow corresponds to one word).We use feature pyramid network(FPN)to predict character and non-character instances with arbitrary directions.A joint network of FPN and bidirectional long short-term memory(BLSTM)is developed to explore the context information among isolated characters,which are finally grouped into character flows.Extensive experiments are conducted on ICDAR2013,ICDAR2015,MSRA-TD500 and MLT datasets to demonstrate the effectiveness of our approach.The F-measures are 92.62%,88.02%,83.69%and 77.81%,respectively.