提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制...提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制信号为脉宽调制波。使用MATLAB/Simulink完成电路仿真,在仿真基础上搭建实物实验电路,实验结果验证了仿真及电路的正确性和可行性。展开更多
Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for st...Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.展开更多
文摘提出一种输出本质安全型准阻抗源Boost电压变换器,介绍了其电路的基本组成,分析了准阻抗源网络中电感器和电容器的元件选型。该电路由准阻抗源电路和Boost斩波电路组成,然后连接电感电容(inductance and capacitance,LC)滤波电路,控制信号为脉宽调制波。使用MATLAB/Simulink完成电路仿真,在仿真基础上搭建实物实验电路,实验结果验证了仿真及电路的正确性和可行性。
基金supported by the National Nat-ural Science Foundation of China(No.52203376)the National Key Research and Development Program of China(No.2023YFB3813200).
文摘Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.
