The thermal issue is of great importance during the layout design of heat source components in systems engineering,especially for high functional-density products.Thermal analysis requires complex simulation,which lea...The thermal issue is of great importance during the layout design of heat source components in systems engineering,especially for high functional-density products.Thermal analysis requires complex simulation,which leads to an unaffordable computational burden to layout optimization as it iteratively evaluates different schemes.Surrogate modeling is an effective method for alleviating computation complexity.However,the temperature field prediction(TFP)with complex heat source layout(HSL)input is an ultra-high dimensional nonlinear regression problem,which brings great difficulty to traditional regression models.The deep neural network(DNN)regression method is a feasible way for its good approximation performance.However,it faces great challenges in data preparation for sample diversity and uniformity in the layout space with physical constraints and proper DNN model selection and training for good generality,which necessitates the efforts of layout designers and DNN experts.To advance this cross-domain research,this paper proposes a DNN-based HSL-TFP surrogate modeling task benchmark.With consideration for engineering applicability,sample generation,dataset evaluation,DNN model,and surrogate performance metrics are thoroughly investigated.Experiments are conducted with ten representative state-of-the-art DNN models.A detailed discussion on baseline results is provided,and future prospects are analyzed for DNN-based HSL-TFP tasks.展开更多
介绍了国际测地/天体测量学甚长基线干涉测量服务(International Very Long Baseline Interferometry(VLBI)Service for Geodesy and Astrometry,IVS)组织机构及下属分析中心概况.系统归纳了目前IVS发布的地球定向参数(Earth Orientatio...介绍了国际测地/天体测量学甚长基线干涉测量服务(International Very Long Baseline Interferometry(VLBI)Service for Geodesy and Astrometry,IVS)组织机构及下属分析中心概况.系统归纳了目前IVS发布的地球定向参数(Earth Orientation Parameters,EOP)产品类型及不同观测类型的用途.利用2010—2019年公开发布的观测资料,对IVS不同分析中心的EOP日常监测和服务能力进行了评估.通过构造观测台站所构成的几何体积,分析了EOP精度与测站数量、测站网分布的关系,统计了IVS不同观测类型的EOP解算精度.此外,综合公开发布的美国、欧洲等区域网观测数据,分析了不同地区区域网的常规及加强观测结果与IVS结果的差异.结果表明:EOP的解算精度与观测台站的分布密切相关,IVS常规观测确定的极移分量的外符合精度优于0.2 mas,世界时(Universal Time,UT1)与协调世界时(Coordinated Universal Time,UTC)之差(UT1-UTC)的精度在0.015 ms左右,加强观测的UT1-UTC值与国际自转服务组织(International Earth Rotation Service,IERS)的C04之间存在0.02–0.03 ms的差异.区域观测网的精度受观测网形和基线长度制约,总体劣于IVS观测精度,其中,美国甚长基线干涉阵列(Very Long Baseline Array,VLBA)的常规及加强观测结果与IVS全球观测结果最接近.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.11725211,52005505,and 62001502)Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.CX20200023).
文摘The thermal issue is of great importance during the layout design of heat source components in systems engineering,especially for high functional-density products.Thermal analysis requires complex simulation,which leads to an unaffordable computational burden to layout optimization as it iteratively evaluates different schemes.Surrogate modeling is an effective method for alleviating computation complexity.However,the temperature field prediction(TFP)with complex heat source layout(HSL)input is an ultra-high dimensional nonlinear regression problem,which brings great difficulty to traditional regression models.The deep neural network(DNN)regression method is a feasible way for its good approximation performance.However,it faces great challenges in data preparation for sample diversity and uniformity in the layout space with physical constraints and proper DNN model selection and training for good generality,which necessitates the efforts of layout designers and DNN experts.To advance this cross-domain research,this paper proposes a DNN-based HSL-TFP surrogate modeling task benchmark.With consideration for engineering applicability,sample generation,dataset evaluation,DNN model,and surrogate performance metrics are thoroughly investigated.Experiments are conducted with ten representative state-of-the-art DNN models.A detailed discussion on baseline results is provided,and future prospects are analyzed for DNN-based HSL-TFP tasks.
文摘介绍了国际测地/天体测量学甚长基线干涉测量服务(International Very Long Baseline Interferometry(VLBI)Service for Geodesy and Astrometry,IVS)组织机构及下属分析中心概况.系统归纳了目前IVS发布的地球定向参数(Earth Orientation Parameters,EOP)产品类型及不同观测类型的用途.利用2010—2019年公开发布的观测资料,对IVS不同分析中心的EOP日常监测和服务能力进行了评估.通过构造观测台站所构成的几何体积,分析了EOP精度与测站数量、测站网分布的关系,统计了IVS不同观测类型的EOP解算精度.此外,综合公开发布的美国、欧洲等区域网观测数据,分析了不同地区区域网的常规及加强观测结果与IVS结果的差异.结果表明:EOP的解算精度与观测台站的分布密切相关,IVS常规观测确定的极移分量的外符合精度优于0.2 mas,世界时(Universal Time,UT1)与协调世界时(Coordinated Universal Time,UTC)之差(UT1-UTC)的精度在0.015 ms左右,加强观测的UT1-UTC值与国际自转服务组织(International Earth Rotation Service,IERS)的C04之间存在0.02–0.03 ms的差异.区域观测网的精度受观测网形和基线长度制约,总体劣于IVS观测精度,其中,美国甚长基线干涉阵列(Very Long Baseline Array,VLBA)的常规及加强观测结果与IVS全球观测结果最接近.
