The variation of casting hot spot with proceeding of solidification andcomponents of casting-mold system is studied by the technique of numerical simulation ofsolidification. The result shows that the thickest part of...The variation of casting hot spot with proceeding of solidification andcomponents of casting-mold system is studied by the technique of numerical simulation ofsolidification. The result shows that the thickest part of casting is not exactly the last part ofsolidification in the casting, while the last part of solidification is not exactly casting hot spotat the early stage of solidification. The location, size, shape and number of casting hot spotchange with geomitric, physical and technological factors of the casting-mold system such asthickness of the casting secondary wall and with the passage of time in the course of thesolidification. The former is known as the systematic property of hot spot and the latter, dynamicproperty. Only when the properties of hot spot are grasped completely and accurately, can it be fedmore effectively. By doing so, not only sound castings can be obtained, but also riser efficiencycan be improved.展开更多
The Loess Plateau, covered with thick loess, lies in the middle reaches of the YellowRiver to the west of the Taihangshan Mountains, east of the Wuqiao Mountains south ofYinshan Mountains and north of the Qinling Moun...The Loess Plateau, covered with thick loess, lies in the middle reaches of the YellowRiver to the west of the Taihangshan Mountains, east of the Wuqiao Mountains south ofYinshan Mountains and north of the Qinling Mountains with a total area of 56×10~4km^2.The plateau is 1000--2500m above sea level and has loess as thick as 100--200 metres, be-展开更多
Since the high penetration of renewable energy complicates the dynamic characteristics of the AC power electronic system(ACPES),it is essential to establish an accurate dynamic model to obtain its dynamic behavior for...Since the high penetration of renewable energy complicates the dynamic characteristics of the AC power electronic system(ACPES),it is essential to establish an accurate dynamic model to obtain its dynamic behavior for ensure the safe and stable operation of the system.However,due to the no or limited internal control details,the state-space modeling method cannot be realized.It leads to the ACPES system becoming a black-box dynamic system.The dynamic modeling method based on deep neural network can simulate the dynamic behavior using port data without obtaining internal control details.However,deep neural network modeling methods are rarely systematically evaluated.In practice,the construction of neural network faces the selection of massive data and various network structure parameters.However,different sample distributions make the trained network performance quite different.Different network structure hyperparameters also mean different convergence time.Due to the lack of systematic evaluation and targeted suggestions,neural network modeling with high precision and high training speed cannot be realized quickly and conveniently in practical engineering applications.To fill this gap,this paper systematically evaluates the deep neural network from sample distribution and structural hyperparameter selection.The influence on modeling accuracy is analyzed in detail,then some modeling suggestions are presented.Simulation results under multiple operating points verify the effectiveness of the proposed method.展开更多
A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model for monthly-scale regional climate forecast over East China. The systematic errors are formulated fo...A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model for monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system, and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC), U.S.A., which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surface air temperature, it is found that the corrected forecast is apparently better than the original, suggesting that the approach can be applied for improving monthly-scale regional climate dynamical forecast.展开更多
基金This project is supported by Science Technology Development Foundation of Shanghai(No.0lJCl400l)+1 种基金Scientific Foundation of Hebei University of ScienceTechnology (No.XZ9906)
文摘The variation of casting hot spot with proceeding of solidification andcomponents of casting-mold system is studied by the technique of numerical simulation ofsolidification. The result shows that the thickest part of casting is not exactly the last part ofsolidification in the casting, while the last part of solidification is not exactly casting hot spotat the early stage of solidification. The location, size, shape and number of casting hot spotchange with geomitric, physical and technological factors of the casting-mold system such asthickness of the casting secondary wall and with the passage of time in the course of thesolidification. The former is known as the systematic property of hot spot and the latter, dynamicproperty. Only when the properties of hot spot are grasped completely and accurately, can it be fedmore effectively. By doing so, not only sound castings can be obtained, but also riser efficiencycan be improved.
文摘The Loess Plateau, covered with thick loess, lies in the middle reaches of the YellowRiver to the west of the Taihangshan Mountains, east of the Wuqiao Mountains south ofYinshan Mountains and north of the Qinling Mountains with a total area of 56×10~4km^2.The plateau is 1000--2500m above sea level and has loess as thick as 100--200 metres, be-
基金supported in part by the Science Search Foundation of Liaoning Educational Department。
文摘Since the high penetration of renewable energy complicates the dynamic characteristics of the AC power electronic system(ACPES),it is essential to establish an accurate dynamic model to obtain its dynamic behavior for ensure the safe and stable operation of the system.However,due to the no or limited internal control details,the state-space modeling method cannot be realized.It leads to the ACPES system becoming a black-box dynamic system.The dynamic modeling method based on deep neural network can simulate the dynamic behavior using port data without obtaining internal control details.However,deep neural network modeling methods are rarely systematically evaluated.In practice,the construction of neural network faces the selection of massive data and various network structure parameters.However,different sample distributions make the trained network performance quite different.Different network structure hyperparameters also mean different convergence time.Due to the lack of systematic evaluation and targeted suggestions,neural network modeling with high precision and high training speed cannot be realized quickly and conveniently in practical engineering applications.To fill this gap,this paper systematically evaluates the deep neural network from sample distribution and structural hyperparameter selection.The influence on modeling accuracy is analyzed in detail,then some modeling suggestions are presented.Simulation results under multiple operating points verify the effectiveness of the proposed method.
基金National Natural Science Foundation of China (40875067, 40675040)Knowledge Innovation Program of the Chinese Academy of Sciences (IAP09306)National Basic Research Program of China. (2006CB400505)
文摘A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model for monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system, and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC), U.S.A., which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surface air temperature, it is found that the corrected forecast is apparently better than the original, suggesting that the approach can be applied for improving monthly-scale regional climate dynamical forecast.
