当前,高频换流器实时仿真在仿真精度和仿真灵活性上难以兼顾。为此采用了基于FPGA+PC的实时多速率协同仿真方法,全面展示了多速率协同仿真系统的仿真原理,以及硬件设计与实现。在30 k Hz/50 k Hz三相两电平逆变算例仿真的研究中,呈现了...当前,高频换流器实时仿真在仿真精度和仿真灵活性上难以兼顾。为此采用了基于FPGA+PC的实时多速率协同仿真方法,全面展示了多速率协同仿真系统的仿真原理,以及硬件设计与实现。在30 k Hz/50 k Hz三相两电平逆变算例仿真的研究中,呈现了换流器建模、算例模型分割和电路求解器实现。以离线精确模型为基准,将多速率协同仿真平台与PC实时仿真平台的实验结果从仿真波形、仿真误差及实时性方面进行比较。结果表明,在开关频率50 k Hz以下多速率仿真的速率转换误差收敛,电磁暂态仿真欧式范数误差达到1%左右,仿真平台仿真步长达到500 ns。该方法提高了高频换流器实时仿真精度、减小了仿真步长,为高性能协同仿真平台的设计提供了参考。展开更多
This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data colle...This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.展开更多
Bridge health monitoring （BHM） has become increasingly significant in the life-cycle of the structure such as maintenance, repair and rehabilitation. It is necessary to use BHM information efficiently to assess the ...Bridge health monitoring （BHM） has become increasingly significant in the life-cycle of the structure such as maintenance, repair and rehabilitation. It is necessary to use BHM information efficiently to assess the working conditions of the bridge. The main objective of this study is to develop an effective method and establish a framework for the real-time reliability assessment based on BHM acceleration information. The first-passage probability and its further development have been proposed to as- sess the reliability probability. The first-passage probability shows the probability of that a scalar process exceeds a designated threshold during a given time interval. The advantage of the proposed method is the assessment of the real-time reliability probability based on the monitoring information during an assessment reference period. Furthermore, the velocity data and displacement data are calculated from the acceleration monitoring data using the relationships between their power spectral density （PSD） functions. The real-time reliability assessment of Donghai Bridge, which is the first large scale cross-sea bridge in China, demonstrates that the proposed method is efficient and effective.展开更多
文摘当前,高频换流器实时仿真在仿真精度和仿真灵活性上难以兼顾。为此采用了基于FPGA+PC的实时多速率协同仿真方法,全面展示了多速率协同仿真系统的仿真原理,以及硬件设计与实现。在30 k Hz/50 k Hz三相两电平逆变算例仿真的研究中,呈现了换流器建模、算例模型分割和电路求解器实现。以离线精确模型为基准,将多速率协同仿真平台与PC实时仿真平台的实验结果从仿真波形、仿真误差及实时性方面进行比较。结果表明,在开关频率50 k Hz以下多速率仿真的速率转换误差收敛,电磁暂态仿真欧式范数误差达到1%左右,仿真平台仿真步长达到500 ns。该方法提高了高频换流器实时仿真精度、减小了仿真步长,为高性能协同仿真平台的设计提供了参考。
文摘This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.
基金supported by the National Basic Research Program of China(“973”Project)(Grant No.2013CB036305)Ministry of Transport of the People’s Republic of China(Grant No.2015318J38230)National Science and Technology Support Plan(Grant No.2012BAJ11B01)
文摘Bridge health monitoring （BHM） has become increasingly significant in the life-cycle of the structure such as maintenance, repair and rehabilitation. It is necessary to use BHM information efficiently to assess the working conditions of the bridge. The main objective of this study is to develop an effective method and establish a framework for the real-time reliability assessment based on BHM acceleration information. The first-passage probability and its further development have been proposed to as- sess the reliability probability. The first-passage probability shows the probability of that a scalar process exceeds a designated threshold during a given time interval. The advantage of the proposed method is the assessment of the real-time reliability probability based on the monitoring information during an assessment reference period. Furthermore, the velocity data and displacement data are calculated from the acceleration monitoring data using the relationships between their power spectral density （PSD） functions. The real-time reliability assessment of Donghai Bridge, which is the first large scale cross-sea bridge in China, demonstrates that the proposed method is efficient and effective.
