动态流量计量性能是流量计的重要指标之一,动态气体流量发生源是研究流量计动态性能的必备单元。基于评价气体流量计动态性能的可行性与准确性,设计一种活塞式动态气体流量发生器。在理论分析的基础上,建立装置的AMESim仿真模型,获得目...动态流量计量性能是流量计的重要指标之一,动态气体流量发生源是研究流量计动态性能的必备单元。基于评价气体流量计动态性能的可行性与准确性,设计一种活塞式动态气体流量发生器。在理论分析的基础上,建立装置的AMESim仿真模型,获得目标动态流量曲线与活塞速度的对应关系。开发基于DSP的控制程序,并结合临界流喷嘴开展实验研究。测试结果表明:该装置可以稳定输出目标流量为1 m 3/h、频率0.1 Hz的动态气体流量。展开更多
Numerical simulation of complex systems and components by computers is a fundamental phase of any modern engineering activity. The traditional methods of simulation typically entail long, iterative processes which lea...Numerical simulation of complex systems and components by computers is a fundamental phase of any modern engineering activity. The traditional methods of simulation typically entail long, iterative processes which lead to large simulation times, often exceeding transient real time. Artificial neural networks （ANNs） may be advantageous in this context, the main advantage being the speed of computation, the capability of generalizing from the few examples, robustness to noisy and partially incomplete data and the capability of performing empirical input-output mapping without complete knowledge of underlying physics. In this paper, the simulation of steam generator is considered as an example to show the potentialities of this tool. The data required for training and testing the ANN is taken from the steam generator at Abott Power Plant, Champaign （USA）. The total number of samples is 9600 which are taken at a sampling time of three seconds. The performance of boiler （drum pressure, steam flow rate） has been verified and tested using ANN, under the changes in fuel flow rate, air flow rate and load disturbance. Using ANN, input-output mapping is done and it is observed that ANN allows a good reproduction of non-linear behaviors of inputs and outputs.展开更多
文摘动态流量计量性能是流量计的重要指标之一,动态气体流量发生源是研究流量计动态性能的必备单元。基于评价气体流量计动态性能的可行性与准确性,设计一种活塞式动态气体流量发生器。在理论分析的基础上,建立装置的AMESim仿真模型,获得目标动态流量曲线与活塞速度的对应关系。开发基于DSP的控制程序,并结合临界流喷嘴开展实验研究。测试结果表明:该装置可以稳定输出目标流量为1 m 3/h、频率0.1 Hz的动态气体流量。
文摘Numerical simulation of complex systems and components by computers is a fundamental phase of any modern engineering activity. The traditional methods of simulation typically entail long, iterative processes which lead to large simulation times, often exceeding transient real time. Artificial neural networks （ANNs） may be advantageous in this context, the main advantage being the speed of computation, the capability of generalizing from the few examples, robustness to noisy and partially incomplete data and the capability of performing empirical input-output mapping without complete knowledge of underlying physics. In this paper, the simulation of steam generator is considered as an example to show the potentialities of this tool. The data required for training and testing the ANN is taken from the steam generator at Abott Power Plant, Champaign （USA）. The total number of samples is 9600 which are taken at a sampling time of three seconds. The performance of boiler （drum pressure, steam flow rate） has been verified and tested using ANN, under the changes in fuel flow rate, air flow rate and load disturbance. Using ANN, input-output mapping is done and it is observed that ANN allows a good reproduction of non-linear behaviors of inputs and outputs.
