In order to satisfy the demand of validity and real time operating performance of diesel engine model used in hardware-in-the-loop simulation system,a simplified quasi-dimensional model for diesel engine working proce...In order to satisfy the demand of validity and real time operating performance of diesel engine model used in hardware-in-the-loop simulation system,a simplified quasi-dimensional model for diesel engine working process was proposed,which was based on the phase-divided spray mixing model.The software MATLAB/Simulink was utilized to simulate diesel engine performance parameters.The comparisons between calculated results and experimental data show that the relative error of power and brake specific fuel consumption is less than 2.8%,and the relative error of nitric oxide and soot emissions is less than 9.1%.At the same time,the average computational time for simulation of one working process with the new model is 36 s,which presents good real time operating performance of the model.The simulation results also indicate that the nozzle flow coefficient has great influence on the prediction precision of performance parameters in diesel engine simulation model.展开更多
This work is to observe the performance of PC based robot manipulator under general purpose (Windows), Soft (Linux) and Hard (RT Linux) Real Time Operating Systems (OS). The same open loop control system is ob...This work is to observe the performance of PC based robot manipulator under general purpose (Windows), Soft (Linux) and Hard (RT Linux) Real Time Operating Systems (OS). The same open loop control system is observed in different operating systems with and without multitasking environment. The Data Acquisition (DAQ, PLC-812PG) card is used as a hardware interface. From the experiment, it could be seen that in the non real time operating system (Windows), the delay of the control system is larger than the Soft Real Time OS (Linux). Further, the authors observed the same control system under Hard Real Time OS (RT-Linux). At this point, the experiment showed that the real time error (jitter) is minimum in RT-Linux OS than the both of the previous OS. It is because the RT-Linux OS kernel can set the priority level and the control system was given the highest priority. The same experiment was observed under multitasking environment and the comparison of delay was similar to the preceding evaluation.展开更多
During the operation of complex process, such as oil production or refming, abnormal situations may occur, leading to an alarm flooding. Alarm flooding is the signalling of a large number of alarms in a few minutes, i...During the operation of complex process, such as oil production or refming, abnormal situations may occur, leading to an alarm flooding. Alarm flooding is the signalling of a large number of alarms in a few minutes, in such a way that it is impossible for the operator to attend to all alarms. On these occasions, it is usual that the operator leaves the alarm summary list and gets an analysis of the plant through the screens of the DCS (digital control system), seeking to understand the situation. The alarm summary list ceases to be a useful tool. In such cases, the operator might have the aid of a filter that would present the highest priority alarms and other information associated with them, enabling him to gain a better knowledge of the situation. This paper describes the interface of a system aimed to help the operator to have a more comprehensive knowledge of the process (a better situational awareness) during process upsets that cause alarm flooding, recovering the utility of the alarm layer to the safety of industrial processes.展开更多
The ensemble technique has been widely used in numerical weather prediction and extended-range forecasting.Current approaches to evaluate the predictability using the ensemble technique can be divided into two major g...The ensemble technique has been widely used in numerical weather prediction and extended-range forecasting.Current approaches to evaluate the predictability using the ensemble technique can be divided into two major groups.One is dynamical,including generating Lyapunov vectors,bred vectors,and singular vectors,sampling the fastest error-growing directions of the phase space,and examining the dependence of prediction efficiency on ensemble size.The other is statistical,including distributional analysis and quantifying prediction utility by the Shannon entropy and the relative entropy.Currently,with simple models,one could choose as many ensembles as possible,with each ensemble containing a large number of members.When the forecast models become increasingly complicated,however,one would only be able to afford a small number of ensembles,each with limited number of members,thus sacrificing estimation accuracy of the forecast errors.To uncover connections between different information theoretic approaches and between dynamical and statistical approaches,we propose an (∈;T)-entropy and scale-dependent Lyapunov exponent——based general theoretical framework to quantify information loss in ensemble forecasting.More importantly,to tremendously expedite computations,reduce data storage,and improve forecasting accuracy,we propose a technique for constructing a large number of "pseudo" ensembles from one single solution or scalar dataset.This pseudo-ensemble technique appears to be applicable under rather general conditions,one important situation being that observational data are available but the exact dynamical model is unknown.展开更多
基金Project(2006A10GX059) supported by the Science and Technology Plan of Dalian,China
文摘In order to satisfy the demand of validity and real time operating performance of diesel engine model used in hardware-in-the-loop simulation system,a simplified quasi-dimensional model for diesel engine working process was proposed,which was based on the phase-divided spray mixing model.The software MATLAB/Simulink was utilized to simulate diesel engine performance parameters.The comparisons between calculated results and experimental data show that the relative error of power and brake specific fuel consumption is less than 2.8%,and the relative error of nitric oxide and soot emissions is less than 9.1%.At the same time,the average computational time for simulation of one working process with the new model is 36 s,which presents good real time operating performance of the model.The simulation results also indicate that the nozzle flow coefficient has great influence on the prediction precision of performance parameters in diesel engine simulation model.
文摘This work is to observe the performance of PC based robot manipulator under general purpose (Windows), Soft (Linux) and Hard (RT Linux) Real Time Operating Systems (OS). The same open loop control system is observed in different operating systems with and without multitasking environment. The Data Acquisition (DAQ, PLC-812PG) card is used as a hardware interface. From the experiment, it could be seen that in the non real time operating system (Windows), the delay of the control system is larger than the Soft Real Time OS (Linux). Further, the authors observed the same control system under Hard Real Time OS (RT-Linux). At this point, the experiment showed that the real time error (jitter) is minimum in RT-Linux OS than the both of the previous OS. It is because the RT-Linux OS kernel can set the priority level and the control system was given the highest priority. The same experiment was observed under multitasking environment and the comparison of delay was similar to the preceding evaluation.
文摘During the operation of complex process, such as oil production or refming, abnormal situations may occur, leading to an alarm flooding. Alarm flooding is the signalling of a large number of alarms in a few minutes, in such a way that it is impossible for the operator to attend to all alarms. On these occasions, it is usual that the operator leaves the alarm summary list and gets an analysis of the plant through the screens of the DCS (digital control system), seeking to understand the situation. The alarm summary list ceases to be a useful tool. In such cases, the operator might have the aid of a filter that would present the highest priority alarms and other information associated with them, enabling him to gain a better knowledge of the situation. This paper describes the interface of a system aimed to help the operator to have a more comprehensive knowledge of the process (a better situational awareness) during process upsets that cause alarm flooding, recovering the utility of the alarm layer to the safety of industrial processes.
基金Project supported by the National Science Foundation (Nos.CMMI-0825311,CMMI-0826119)
文摘The ensemble technique has been widely used in numerical weather prediction and extended-range forecasting.Current approaches to evaluate the predictability using the ensemble technique can be divided into two major groups.One is dynamical,including generating Lyapunov vectors,bred vectors,and singular vectors,sampling the fastest error-growing directions of the phase space,and examining the dependence of prediction efficiency on ensemble size.The other is statistical,including distributional analysis and quantifying prediction utility by the Shannon entropy and the relative entropy.Currently,with simple models,one could choose as many ensembles as possible,with each ensemble containing a large number of members.When the forecast models become increasingly complicated,however,one would only be able to afford a small number of ensembles,each with limited number of members,thus sacrificing estimation accuracy of the forecast errors.To uncover connections between different information theoretic approaches and between dynamical and statistical approaches,we propose an (∈;T)-entropy and scale-dependent Lyapunov exponent——based general theoretical framework to quantify information loss in ensemble forecasting.More importantly,to tremendously expedite computations,reduce data storage,and improve forecasting accuracy,we propose a technique for constructing a large number of "pseudo" ensembles from one single solution or scalar dataset.This pseudo-ensemble technique appears to be applicable under rather general conditions,one important situation being that observational data are available but the exact dynamical model is unknown.