In recent years,the construction of offshore wind farms is developing rapidly.As the wake effect of the upstream wind turbines seriously affect the performance of the downstream wind turbines,the wake effect of offsho...In recent years,the construction of offshore wind farms is developing rapidly.As the wake effect of the upstream wind turbines seriously affect the performance of the downstream wind turbines,the wake effect of offshore wind turbines has become one of the research hotspots.First,this article reviews the research methods of wake effects,including CFD numerical simulation method,wind turbine wake model based on roughness and engineering wake models.However,there is no general model that can be used directly.Then it puts forward some factors that affect the wake of offshore wind turbines.The turbulence intensity in offshore wind fields is lower than that in onshore wind fields.This makes the wake recovery length of offshore wind turbines longer than that of onshore wind turbines.Floating offshore wind turbines are simultaneously disturbed by wind loads and wave loads.Unsteady movement of the platform caused by wave loads.It affects the development and changes of the wake of wind turbines.In this regard,the focus of research on the wake effects of offshore wind farms will be the proposal of accurate prediction models for the wake effects of sea wind farms.展开更多
Mobile edge computing(MEC)is a promising technology for the Internet of Vehicles,especially in terms of application offloading and resource allocation.Most existing offloading schemes are sub-optimal,since these offlo...Mobile edge computing(MEC)is a promising technology for the Internet of Vehicles,especially in terms of application offloading and resource allocation.Most existing offloading schemes are sub-optimal,since these offloading strategies consider an application as a whole.In comparison,in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning.In our framework,each application is modelled as a directed acyclic graph,where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks.Both vehicles and MEC server within the communication range can be used as candidate offloading nodes.Then,the offloading involves assigning these computing nodes to subtasks.In addition,the proposed offloading scheme deal with the delay constraint of each subtask.The experimental evaluation show that,compared to existing non-partitioning offloading schemes,this proposed one effectively improves the performance of the application in terms of execution time and throughput.展开更多
基金The work was sponsored by the Open Fund of Key Laboratory of Wind Energy and Solar Energy Technology(Inner Mongolia University of Technology),Ministry of Education(No.2020ZD01)in Chinathe Fund of Southern Marine Science and Engineering Guangdong Laboratory(Zhanjiang)(ZJW-2019-02).
文摘In recent years,the construction of offshore wind farms is developing rapidly.As the wake effect of the upstream wind turbines seriously affect the performance of the downstream wind turbines,the wake effect of offshore wind turbines has become one of the research hotspots.First,this article reviews the research methods of wake effects,including CFD numerical simulation method,wind turbine wake model based on roughness and engineering wake models.However,there is no general model that can be used directly.Then it puts forward some factors that affect the wake of offshore wind turbines.The turbulence intensity in offshore wind fields is lower than that in onshore wind fields.This makes the wake recovery length of offshore wind turbines longer than that of onshore wind turbines.Floating offshore wind turbines are simultaneously disturbed by wind loads and wave loads.Unsteady movement of the platform caused by wave loads.It affects the development and changes of the wake of wind turbines.In this regard,the focus of research on the wake effects of offshore wind farms will be the proposal of accurate prediction models for the wake effects of sea wind farms.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.U20A20177,61772377,91746206)the Fundamental Research Funds for the Central Universities,and Science and Technology planning project of ShenZhen(JCYJ20170818112550194).
文摘Mobile edge computing(MEC)is a promising technology for the Internet of Vehicles,especially in terms of application offloading and resource allocation.Most existing offloading schemes are sub-optimal,since these offloading strategies consider an application as a whole.In comparison,in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning.In our framework,each application is modelled as a directed acyclic graph,where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks.Both vehicles and MEC server within the communication range can be used as candidate offloading nodes.Then,the offloading involves assigning these computing nodes to subtasks.In addition,the proposed offloading scheme deal with the delay constraint of each subtask.The experimental evaluation show that,compared to existing non-partitioning offloading schemes,this proposed one effectively improves the performance of the application in terms of execution time and throughput.
