The IBM Agent Building and Learning Environment(ABLE) provides a lightweight Java^(TM) agent frame- work,a comprehensive JavaBeansTM library of intelligent software components,a set of development and test tools, and ...The IBM Agent Building and Learning Environment(ABLE) provides a lightweight Java^(TM) agent frame- work,a comprehensive JavaBeansTM library of intelligent software components,a set of development and test tools, and an agent platform.After the introduction to ABLE,classes and interfaces in the ABLE agent framework were put forward.At last an autonomic agent that is an ABLE-based architecture for incrementally building autonomic systems was discussed.展开更多
特高压(ultra high voltage,UHV)换流站阀厅的金属屋面系统在风荷载作用下易发生屋面表层风揭事故。为深入探讨该类建筑屋面的风压极值特性,基于风洞试验分别探讨了大气边界层(atmospheric-boundary-layer,ABL)风、壁面射流、均匀湍流...特高压(ultra high voltage,UHV)换流站阀厅的金属屋面系统在风荷载作用下易发生屋面表层风揭事故。为深入探讨该类建筑屋面的风压极值特性,基于风洞试验分别探讨了大气边界层(atmospheric-boundary-layer,ABL)风、壁面射流、均匀湍流三种风场作用下的屋面风压特性,比较了平均风剖面、风速、风向、湍流强度等因素对屋面风压的影响。结果表明:阀厅屋盖迎风前缘负风压最大,且控制风向角在45°左右;壁面射流风场下平均风压系数与脉动风压系数均超过大气边界层风场的结果;风速对阀厅屋盖的负风压系数均值和极值影响较小,而湍流度对风压系数的极值影响较大;大气边界风场时,JGJ/T 481—2019《屋盖结构风荷载标准》的最不利风压系数建议值偏于安全;而在壁面射流风场下,阀厅屋盖全风向最不利风压系数在所有区域都大于JGJ/T 481—2019的建议值,设计中应加以重视。展开更多
High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution fl...High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution flow field data,while the high experiment cost and computing resources for simulation hinder the specificanalysis of flow field evolution.With the development of deep learning technology,convolutional neural networks areused to achieve high-resolution reconstruction of the flow field.In this paper,an ordinary convolutional neuralnetwork and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylorinstability.These two methods can reconstruct the high-resolution flow field in just a few seconds,and further greatlyenrich the application of high-resolution reconstruction technology in fluid instability.Compared with the ordinaryconvolutional neural network,the multi-time-path convolutional neural network model has smaller error and canrestore more details of the flow field.The influence of low-resolution flow field data obtained by the two poolingmethods on the convolutional neural networks model is also discussed.展开更多
文摘The IBM Agent Building and Learning Environment(ABLE) provides a lightweight Java^(TM) agent frame- work,a comprehensive JavaBeansTM library of intelligent software components,a set of development and test tools, and an agent platform.After the introduction to ABLE,classes and interfaces in the ABLE agent framework were put forward.At last an autonomic agent that is an ABLE-based architecture for incrementally building autonomic systems was discussed.
文摘特高压(ultra high voltage,UHV)换流站阀厅的金属屋面系统在风荷载作用下易发生屋面表层风揭事故。为深入探讨该类建筑屋面的风压极值特性,基于风洞试验分别探讨了大气边界层(atmospheric-boundary-layer,ABL)风、壁面射流、均匀湍流三种风场作用下的屋面风压特性,比较了平均风剖面、风速、风向、湍流强度等因素对屋面风压的影响。结果表明:阀厅屋盖迎风前缘负风压最大,且控制风向角在45°左右;壁面射流风场下平均风压系数与脉动风压系数均超过大气边界层风场的结果;风速对阀厅屋盖的负风压系数均值和极值影响较小,而湍流度对风压系数的极值影响较大;大气边界风场时,JGJ/T 481—2019《屋盖结构风荷载标准》的最不利风压系数建议值偏于安全;而在壁面射流风场下,阀厅屋盖全风向最不利风压系数在所有区域都大于JGJ/T 481—2019的建议值,设计中应加以重视。
基金National Natural Science Foundation of China(1180500311947102+4 种基金12004005)Natural Science Foundation of Anhui Province(2008085MA162008085QA26)University Synergy Innovation Program of Anhui Province(GXXT-2022-039)State Key Laboratory of Advanced Electromagnetic Technology(Grant No.AET 2024KF006)。
文摘High-resolution flow field data has important applications in meteorology,aerospace engineering,high-energy physics and other fields.Experiments and numerical simulations are two main ways to obtain high-resolution flow field data,while the high experiment cost and computing resources for simulation hinder the specificanalysis of flow field evolution.With the development of deep learning technology,convolutional neural networks areused to achieve high-resolution reconstruction of the flow field.In this paper,an ordinary convolutional neuralnetwork and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylorinstability.These two methods can reconstruct the high-resolution flow field in just a few seconds,and further greatlyenrich the application of high-resolution reconstruction technology in fluid instability.Compared with the ordinaryconvolutional neural network,the multi-time-path convolutional neural network model has smaller error and canrestore more details of the flow field.The influence of low-resolution flow field data obtained by the two poolingmethods on the convolutional neural networks model is also discussed.
