Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufactur- ing. How to effectively address the problems of unified modeling, composition and reuse based o...Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufactur- ing. How to effectively address the problems of unified modeling, composition and reuse based on the multi-disciplinary heteroge- neous models has brought great challenges to the modeling and simulation （M＆S） science and technology. This paper presents a top-level modeling theory based on the meta modeling framework （M2F） of the COllaborative SIMulation （COSlM） theory of virtual prototyping to solve the problems. Firstly the fundamental prin- ciples of the top-level modeling theory are decribed to expound the premise, assumptions, basic conventions and special require- ments in the description of complex heterogeneous systems. Next the formalized definitions for each factor in top level modeling are proposed and the hierarchical nature of them is illustrated. After demonstrating that they are self-closing, this paper divides the top- level modeling into two views, static structural graph and dynamic behavioral graph. Finally, a case study is discussed to demon- strate the feasibility of the theory.展开更多
Production logistics involve the co-ordination of ac tivities such as production and materials control (PMC), inventory management, p roduct life cycle management, etc. Those activities demand for an accurate forec as...Production logistics involve the co-ordination of ac tivities such as production and materials control (PMC), inventory management, p roduct life cycle management, etc. Those activities demand for an accurate forec asting model. However, the conventional methods of making sell and buy decision based on human forecast or conventional moving average and exponential smoothing methods is no longer be sufficient to meet the future need. Furthermore, the un derlying statistics of the market information change from time to time due to a number of reasons such as change of global economic environment, government poli cies and business risks. This demands for highly adaptive forecasting model which is robust enough to response and adapt well to the fast changes in the dat a characteristics, in other words, the trajectory of the "dynamic characteristic s" of the data. In this paper, an adaptive time-series modelling method was proposed for short -term dynamic forecasting. The method employs an autoregressive (AR) time-seri es model to carry out the forecasting process. A modified least mean square (MLM S) adaptive filter algorithm was established for adjusting the AR model coeffici ents so as to minimise the sum of squared of forecasting errors. A prototype dyn amic forecasting system was built based on the adaptive time-series modelling m ethod. Basically, the dynamic forecasting system can be divided into two phases, i.e. the Learning Phase and the Application Phase. The learning procedures star t with the determination of upper limit of the adaptation gain based on the conv ergence in the mean square criterion. Hence, the optimum ELMS filter parameters are determined using an iteration algorithm which changes each filter parameter i.e. the order, the adaptation gain andthe values initial coefficient vector on e by one inside a predetermined iteration range. The set of parameters which giv es the minimum value for sum of squared errors within the iteration range is sel ected as the optimum set of filter parameters. In the Application Phase, the sys tem is operated under a real-time environment. The sampled data is processed by the optimised ELMS filter and the forecasted data are calculated based on the a daptive time-series model. The error of forecasting is continuously monitored w ithin the predefined tolerance. When the system detects excessive forecasting er ror, a feedback alarm signal was issued for system re-calibration. Experimental results indicated that the convergence rate and sum of squared erro rs during initial adaptation could be significantly improved using the MLMS algorithm. The performance of the system was verified through a series of experi ments conducted on the forecast of materials demand and costing in productio n logistics. Satisfactory results were achieved with the forecast errors confini ng within in most instances. Further applications of the system can be found i n sales demand forecast, inventory management as well as collaborative planning, forecast and replenishment (CPFR) in logistics engineering.展开更多
Coalbed gas non-Darcy flow has been observed in high permeable fracture systems,and some mathematical and numerical models have been proposed to study the effects of non-Darcy flow using Forchheimer non-Darcy model.Ho...Coalbed gas non-Darcy flow has been observed in high permeable fracture systems,and some mathematical and numerical models have been proposed to study the effects of non-Darcy flow using Forchheimer non-Darcy model.However,experimental results show that the assumption of a constant Forchheimer factor may cause some limitations in using Forchheimer model to describe non-Darcy flow in porous media.In order to investigate the effects of non-Darcy flow on coalbed methane production,this work presents a more general coalbed gas non-Darcy flow model according to Barree-Conway equation,which could describe the entire range of relationships between flow velocity and pressure gradient from low to high flow velocity.An expanded mixed finite element method is introduced to solve the coalbed gas non-Darcy flow model,in which the gas pressure and velocity can be approximated simultaneously.Error estimate results indicate that pressure and velocity could achieve first-order convergence rate.Non-Darcy simulation results indicate that the non-Darcy effect is significant in the zone near the wellbore,and with the distance from the wellbore increasing,the non-Darcy effect becomes weak gradually.From simulation results,we have also found that the non-Darcy effect is more significant at a lower bottom-hole pressure,and the gas production from non-Darcy flow is lower than the production from Darcy flow under the same permeable condition.展开更多
基金supported by the National High Technology Research and Development Program (863 Program) (2011AA040502).
文摘Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufactur- ing. How to effectively address the problems of unified modeling, composition and reuse based on the multi-disciplinary heteroge- neous models has brought great challenges to the modeling and simulation （M＆S） science and technology. This paper presents a top-level modeling theory based on the meta modeling framework （M2F） of the COllaborative SIMulation （COSlM） theory of virtual prototyping to solve the problems. Firstly the fundamental prin- ciples of the top-level modeling theory are decribed to expound the premise, assumptions, basic conventions and special require- ments in the description of complex heterogeneous systems. Next the formalized definitions for each factor in top level modeling are proposed and the hierarchical nature of them is illustrated. After demonstrating that they are self-closing, this paper divides the top- level modeling into two views, static structural graph and dynamic behavioral graph. Finally, a case study is discussed to demon- strate the feasibility of the theory.
文摘Production logistics involve the co-ordination of ac tivities such as production and materials control (PMC), inventory management, p roduct life cycle management, etc. Those activities demand for an accurate forec asting model. However, the conventional methods of making sell and buy decision based on human forecast or conventional moving average and exponential smoothing methods is no longer be sufficient to meet the future need. Furthermore, the un derlying statistics of the market information change from time to time due to a number of reasons such as change of global economic environment, government poli cies and business risks. This demands for highly adaptive forecasting model which is robust enough to response and adapt well to the fast changes in the dat a characteristics, in other words, the trajectory of the "dynamic characteristic s" of the data. In this paper, an adaptive time-series modelling method was proposed for short -term dynamic forecasting. The method employs an autoregressive (AR) time-seri es model to carry out the forecasting process. A modified least mean square (MLM S) adaptive filter algorithm was established for adjusting the AR model coeffici ents so as to minimise the sum of squared of forecasting errors. A prototype dyn amic forecasting system was built based on the adaptive time-series modelling m ethod. Basically, the dynamic forecasting system can be divided into two phases, i.e. the Learning Phase and the Application Phase. The learning procedures star t with the determination of upper limit of the adaptation gain based on the conv ergence in the mean square criterion. Hence, the optimum ELMS filter parameters are determined using an iteration algorithm which changes each filter parameter i.e. the order, the adaptation gain andthe values initial coefficient vector on e by one inside a predetermined iteration range. The set of parameters which giv es the minimum value for sum of squared errors within the iteration range is sel ected as the optimum set of filter parameters. In the Application Phase, the sys tem is operated under a real-time environment. The sampled data is processed by the optimised ELMS filter and the forecasted data are calculated based on the a daptive time-series model. The error of forecasting is continuously monitored w ithin the predefined tolerance. When the system detects excessive forecasting er ror, a feedback alarm signal was issued for system re-calibration. Experimental results indicated that the convergence rate and sum of squared erro rs during initial adaptation could be significantly improved using the MLMS algorithm. The performance of the system was verified through a series of experi ments conducted on the forecast of materials demand and costing in productio n logistics. Satisfactory results were achieved with the forecast errors confini ng within in most instances. Further applications of the system can be found i n sales demand forecast, inventory management as well as collaborative planning, forecast and replenishment (CPFR) in logistics engineering.
基金Projects(91330106,11171190)supported by the National Natural Science Foundation of ChinaProjects(15CX05065A,15CX05003A)supported by the Fundamental Research Funds for the Central Universities,China
文摘Coalbed gas non-Darcy flow has been observed in high permeable fracture systems,and some mathematical and numerical models have been proposed to study the effects of non-Darcy flow using Forchheimer non-Darcy model.However,experimental results show that the assumption of a constant Forchheimer factor may cause some limitations in using Forchheimer model to describe non-Darcy flow in porous media.In order to investigate the effects of non-Darcy flow on coalbed methane production,this work presents a more general coalbed gas non-Darcy flow model according to Barree-Conway equation,which could describe the entire range of relationships between flow velocity and pressure gradient from low to high flow velocity.An expanded mixed finite element method is introduced to solve the coalbed gas non-Darcy flow model,in which the gas pressure and velocity can be approximated simultaneously.Error estimate results indicate that pressure and velocity could achieve first-order convergence rate.Non-Darcy simulation results indicate that the non-Darcy effect is significant in the zone near the wellbore,and with the distance from the wellbore increasing,the non-Darcy effect becomes weak gradually.From simulation results,we have also found that the non-Darcy effect is more significant at a lower bottom-hole pressure,and the gas production from non-Darcy flow is lower than the production from Darcy flow under the same permeable condition.
