Leakage-before-break technique is widely used in high energy pipeline of nuclear plant, for which crack stability of pipeline under complex loading condition is a key issue, and crack growth resistance curve of pipeli...Leakage-before-break technique is widely used in high energy pipeline of nuclear plant, for which crack stability of pipeline under complex loading condition is a key issue, and crack growth resistance curve of pipeline material is the important foundation for crack stability analysis. In this paper, ferritic steel A533B is studied, Gurson damage model is used to simulate crack process of contact tension specimen under unitension, and effect of Gurson model parameter on simulation result is discussed. The following results are found during simulation: initial porosity factor is the main parameter, when it increases gradually, unstable crack extension will be observed;however, only initial J toughness is affected by critical porosity factor;the minor parameter is load step control, when it increases, stable and convergent result is obtained. All results in this paper can be used to determine parameters in Gurson mode, which will be foundation for crack extension analysis of pipeline.展开更多
Gasoline is the lifeblood of the national economy.The forecasting of gasoline prices is difficult because of frequent price fluctuations,its complex nature,diverse influencing factors,and low accuracy of prediction re...Gasoline is the lifeblood of the national economy.The forecasting of gasoline prices is difficult because of frequent price fluctuations,its complex nature,diverse influencing factors,and low accuracy of prediction results.Previous studies mainly focus on forecasting gasoline prices in a single region by single time series analysis which ignores the daily price co-movement of different series from multiple regions.Because price co-movement may contain useful information for price forecasting,this paper proposes the LassoCNN ensemble model that combines statistical models and deep neural networks to forecast gasoline prices.In this model,the Least Absolute Shrinkage and Selection Operator(Lasso)screens and chooses the correlated time series to enhance the performance of forecasting and avoid overfitting,while Convolutional Neural Network(CNN)takes the selected multiple series as its input and then forecasts the gasoline prices in a certain region.Forecasting results of gasoline prices at the national level and regional levels by using the new method demonstrate that the new approach provides more accurate results for the predictions of gasoline prices than those results generated by alternative methods.Thus,the relevant series can enhance the performance of forecasting and help to gain better results.展开更多
文摘Leakage-before-break technique is widely used in high energy pipeline of nuclear plant, for which crack stability of pipeline under complex loading condition is a key issue, and crack growth resistance curve of pipeline material is the important foundation for crack stability analysis. In this paper, ferritic steel A533B is studied, Gurson damage model is used to simulate crack process of contact tension specimen under unitension, and effect of Gurson model parameter on simulation result is discussed. The following results are found during simulation: initial porosity factor is the main parameter, when it increases gradually, unstable crack extension will be observed;however, only initial J toughness is affected by critical porosity factor;the minor parameter is load step control, when it increases, stable and convergent result is obtained. All results in this paper can be used to determine parameters in Gurson mode, which will be foundation for crack extension analysis of pipeline.
基金supported by the National Natural Science Foundation for Distinguished Young Scholars of China(No.71701223)the National Statistical Science Foundation of China(No.2018LZ08)+2 种基金the Central University of Finance and Economics Young Talents Training Support Project(No.QYP2014)Fundamental Research Funds for the Central Universities(China):the Central University of Finance and Economics Scientific Research and Innovation Team Support Project,the Strategic Economy Interdisciplinarity(Beijing Universities Advanced Disciplines Initiative(No.GJJ2019163))the Emerging Interdisciplinary Project of CUFE(No.020659919002).
文摘Gasoline is the lifeblood of the national economy.The forecasting of gasoline prices is difficult because of frequent price fluctuations,its complex nature,diverse influencing factors,and low accuracy of prediction results.Previous studies mainly focus on forecasting gasoline prices in a single region by single time series analysis which ignores the daily price co-movement of different series from multiple regions.Because price co-movement may contain useful information for price forecasting,this paper proposes the LassoCNN ensemble model that combines statistical models and deep neural networks to forecast gasoline prices.In this model,the Least Absolute Shrinkage and Selection Operator(Lasso)screens and chooses the correlated time series to enhance the performance of forecasting and avoid overfitting,while Convolutional Neural Network(CNN)takes the selected multiple series as its input and then forecasts the gasoline prices in a certain region.Forecasting results of gasoline prices at the national level and regional levels by using the new method demonstrate that the new approach provides more accurate results for the predictions of gasoline prices than those results generated by alternative methods.Thus,the relevant series can enhance the performance of forecasting and help to gain better results.
