Temporal effects in trend prediction: identifying the most popular nodes in the future

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail

A Study Of Two-Phase Flow Regime And Pressure Drop In Vertical Pipe

In the oil industry, after the wellbore is drilled into a reservoir, oil and gas will flow into the bore hole and can be transported to the earth’s surface through tubing. Multiphase flow will take place in the pipe. Flow regime has a significant influence on production. For example, the slug flow will cause a huge pressure-drop in the surface system and can even cause a shut-down of the well.Therefore, it is important to test two-phase upwards flow in the pipe. Different kinds of characteristics are used to distinguish between different kinds of flow patterns. I introduce the development of flow pattern and some methods which were applied to select and determine essential parameters, such as volume fluxes rate, fluid density, viscosity, and surface tension to classify flow regime. The pressure change in the vertical tube is a summation of three factors: friction and liquid-gas interface, gravity, acceleration changing. And making an explain of the pressure drop in sucker rod pumping systems, friction force due to the movement of the plunger and the rod, buoyant force, and gravity force are including in the model. In order to test flow regime changing and pressure-drop in two-phase upwards flow. I created a two-phase flow loop experiment, and choose to use different diameter tubes to have a comparison. The water and air flow velocities range from 0.01 to 20 m/s and 0.05 to 10 m/s, separately. I also set up a model in Ansys-Fluent to simulate the pressure drop and flow regime change in the test tube. With the comparison of the simulation result and the real experiment. The pressure-drop depends on both the diameter changing and water/air inlet superficial velocities, both of the results are coincide

An investigation of techniques to assist with reliable specification and successful simulation of fire field modelling scenarios

Computational fluid dynamics (CFD) based Fire Field Modelling (FFM) codes offer powerful tools for fire safety engineers but their operation requires a high level of skill and an understanding of the mode of operation and limitations, in order to obtain meaningful results in complex scenarios. This problem is compounded by the fact that many FFM cases are barely stable and poor quality set-up can lead to solution failure. There are considerable dangers of misuse of FFM techniques if they are used without adequate knowledge of both the underlying fire science and the associated numerical modelling. CFD modelling can be difficult to set up effectively since there are a number of potential problems: it is not always clear what controls are needed for optimal solution performance, typically there will be no optimal static set of controls for the whole solution period to cover all stages of a complex simulation, there is the generic problem of requiring a high quality mesh - which cannot usually be ascertained until the mesh is actually used for the particular simulation for which it is intended and there are potential handling issues, e.g. for transitional events (and extremes of physical behaviour) which are likely to break the solution process. In order to tackle these key problems, the research described in this thesis has identified and investigated a methodology for analysing, applying and automating a CFD Expert user's knowledge to support various stages of the simulation process - including the key stages of creating a mesh and performing the simulation. This research has also indicated an approach for the control of a FFM CFD simulation which is analogous to the way that a FFM CFD Expert would approach the modelling of a previously unseen scenario. These investigations have led to the identification of a set of requirements and appropriate knowledge which have been instantiated as the, so called, Experiment Engine (EE). This prototype component (which has been built and tested within the SMARTFIRE FFM environment) is capable, both of emulating an Expert users' ability to produce a high quality and appropriate mesh for arbitrary scenarios, and is also able to automatically adjust a key control factor of the solution process. This research has demonstrated that it is possible to emulate an Experts' ability to analyse a series of simulation trials (starting from a simplified, coarse mesh test run) in order to improve subsequent modelling attempts and to improve the scenario specification and/or meshing solution in order to allow the software to recover from a complete solution failure. The research has also shown that it is possible to emulate an Expert user's ability to provide continual run-time control of a simulation and to provide significant benefits in terms of performance, overall reliability and accuracy of the results. The instantiation and testing of the Experiment Engine concept, on a chosen FFM environment - SMARTFIRE, has demonstrated significant performance and stability gains when compared to non Experiment Engine controlled simulations, for a range of complex "real world" fire scenarios. Preliminary tests have shown that the Experiment Engine controlled simulation was generally able to finish the simulations successfully without experiencing any difficulty, even for very complex scenarios, and that the run-time solution control adjustments, made to the time step size by both the Experiment Engine and by the Expert, showed similar trends and responses in reacting to the physical and/or numerical changes in the solution. This was also noticed for transitional events seen during the simulation. It has also been shown that the Experiment Engine (EE) controlled simulation demonstrates a saving of up to 40% of simulation sweeps for complex fire scenarios when compared with non-EE controlled simulations. Analysis of the results has demonstrated that the control technique, deployed by the EE, have no significant impact on the final solution results - hence, the Experiment Engine controlled simulations are able to produce physically sound results, which are almost identical to Expert controlled simulations. The research has investigated a number of new methods and algorithms (e.g. case categorisation, case recognition, block-wise mesh justification, local adaptive mesh refinements, etc.) that are combined into a novel approach to enhance the robustness, efficiency and the ease-of-use of the existing FFM software package. The instantiation of these methods as a prototype control system (within the target FFM environment - SMARTFIRE) has enhanced the software with a valuable tool-set and arguably will make the FFM techniques more accessible and reliable for novice users. The component based design and implementation of the Experiment Engine has proved to be highly robust and flexible. The Experiment Engine (EE) provides a bi­directional communication channel between the existing SMARTFIRE Case Specification Environment and the solution module (the CFD Engine). These key components can now communicate directly via status- and control- messages. In this way, it is possible to maintain the original Case Specification Environment and the CFD Engine processes completely independently. The two components interact with each other when the EE is operating. This componentization has enabled rapid prototyping and implementation of new development requirements (as well as the integration of other support techniques) as they have been identified

Unique continuation property with partial information for two-dimensional anisotropic elasticity systems

In this paper, we establish a novel unique continuation property for two-dimensional anisotropic elasticity systems with partial information. More precisely, given a homogeneous elasticity system in a domain, we investigate the unique continuation by assuming only the vanishing of one component of the solution in a subdomain. Using the corresponding Riemann function, we prove that the solution vanishes in the whole domain provided that the other component vanishes at one point up to its second derivatives. Further, we construct several examples showing the possibility of further reducing the additional information of the other component. This result possesses remarkable significance in both theoretical and practical aspects because the required data is almost halved for the unique determination of the whole solution.Comment: 14 pages, 1 figur

Research on the Distribution of Freight with Time Windows in Consideration of Traffic Congestion

Since the implementation of the regulations on the limit-driving of truck in urban areas of big cities, the study on route and time of city distribution gradually gets more attention. To improve the efficiency of distribution transport, not only the length of the routes need to be considered, but also the traffic conditions as well, even along with freight station locations and etc. Based on the traffic data of Beijing as an example, this paper analyze the differences in traffic distribution in aspect of time and areas, which will be taken into considered the distribution selection strategy with time window, so that we can ensure the freight trucks in delivery and pick-up processing avoids peak congestion. Finally take company A as an example, introduce the dynamic replenishment method to different districts considering their own particular congestion status. We expect to bring some inspiration to the vehicle allocation decision of online freight companies