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Research on the dynamic management of cloud simulation derived data

International Journal of Modeling, simulation, and Scientific Computing 2017年第3期8卷 135-158页

作者： Zongshao Che Chun Zhao Yuanjun Laili Lin Zhang School of Automation Science and Electrical Engineering Beihang University HaidianBeijingP.R.China Engineering Research Center of Complex Product Advanced Manufacturing Systems Ministry of EducationHaidian BeijingP.R.China

cloud simulation derived data is defined as the data related to service version,characteristics,relationships,runtime environments and cross-domain communication during service execution in cloud simulation environment,*** is of great value and significance in cloud simulation for service description,service composition and resource *** types of derived data are abundant and the amount of it is *** studies on cloud simulation usually assume all of the derived data required for a specific task is well organized and available anytime,which is of course *** data needs to be expressed and managed in terms of knowledge to make sure the smooth execution of cloud simulation ***,this paper presents a derived data management method in cloud simulation platform to enable derived data collection and dynamic knowledge *** prototype system of the proposed method are established and a virtual prototype of double girder crane is taken as an example to verify the effectiveness of the method.

关键词： cloud simulation derived data data collector deployment non-relational database knowledge storage

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Assessment of the Southern Ocean Sea Surface Temperature Biases in CMIP5 and CMIP6 Models

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Journal of Ocean University of China 2024年第5期23卷 1135-1150页

作者： GAO Zhen ZHAO Shichang LIU Qinyu LONG Shang-Min SUN Shantong Key Laboratory of Marine Hazards Forecasting Ministry of Natural ResourcesHohai UniversityNanjing 210098China College of Oceanography Hohai UniversityNanjing 210098China Laoshan Laboratory Qingdao 266237China Physical Oceanography Laboratory Ocean University of ChinaQingdao 266100China

This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).By combining models from the same community sharing highly similar SO SST biases and eliminating the effect of global-mean biases on local SST biases,the results reveal that the ensemble-mean SO SST bias at 70°-30°S decreases from 0.38℃ in CMIP5 to 0.28℃ in CMIP6,together with increased intermodel *** dominant mode of the intermodel variations in the zonal-mean SST biases is characterized as a meridional uniform warm bias pattern,explaining 79.1% of the intermodel variance and exhibiting positive principal values for most *** ocean mixed layer heat budget further demonstrates that the SST biases at 70°-50°S primarily result from the excessive summertime heating effect from surface net heat *** biases in surface net heat flux south of 50°S are largely impacted by surface shortwave radiation from cloud and clear sky components at different *** of 50°S,the underestimated westerlies reduce the northward Ekman transport and hence northward cold advection in models,leading to warm SST biases *** addition,the westerly biases are primarily traced back to the atmosphere-alone model simulations forced by the observed SST and sea *** results disclose the thermal origin at the high latitude and dynamical origin at the low latitude of the SO SST biases and underscore the significance of the deficiencies of atmospheric models in producing the SO SST biases.

关键词： Southern Ocean CMIP5 CMIP6 SST biases surface heat flux westerlies atmospheric biases cloud simulation

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The Technique Review in System of Systems simulation

The Technique Review in System of Systems Simulation

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第四届体系工程学术会议——数字化转型中的体系工程

作者： Peichen Zhang Jiqing Zhang Kewei Yang Bingfeng Ge Yuming Huang Mingyin Zou Zeqiang Hou 国防科技大学系统工程学院中国国际工程咨询有限公司

In System of Systems confrontation,System of Systems simulation has many advantages,such as low cost and *** the coming of an informational era and the development of artificial intelligence technology,System of Systems simulation is facing many unprecedented *** has high complexity,a large amount of data,and distributed *** the same time,many System of Systems simulation technology is also rapidly *** data management,data mining,and "cloud" technology support the development of System of Systems *** of the technology promotes the development of the System of Systems confrontation *** of Systems simulation is becoming more and more intelligent gradually.

关键词： System of Systems Big data cloud simulation Big data Management Intelligent form

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