Permeability is a vital property of rock mass, which is highly affected by tectonic stress and human engineering activities. A comprehensive monitoring of pore pressure and flow rate distributions inside the rock mass...Permeability is a vital property of rock mass, which is highly affected by tectonic stress and human engineering activities. A comprehensive monitoring of pore pressure and flow rate distributions inside the rock mass is very important to elucidate the permeability evolution mechanisms, which is difficult to realize in laboratory, but easy to be achieved in numerical simulations. Therefore, the particle flow code （PFC）, a discrete element method, is used to simulate permeability behaviors of rock materials in this study. Owe to the limitation of the existed solid-fluid coupling algorithm in PFC, an improved flow-coupling algorithm is presented to better reflect the preferential flow in rock fractures. The comparative analysis is conducted between original and improved algorithm when simulating rock permeability evolution during triaxial compression, showing that the improved algorithm can better describe the experimental phenomenon. Furthermore, the evolution of pore pressure and flow rate distribution during the flow process are analyzed by using the improved algorithm. It is concluded that during the steady flow process in the fractured specimen, the pore pressure and flow rate both prefer transmitting through the fractures rather than rock matrix. Based on the results, fractures are divided into the following three types： I） fractures link to both the inlet and outlet, II） fractures only link to the inlet, and III） fractures only link to the outlet. The type I fracture is always the preferential propagating path for both the pore pressure and flow rate. For type II fractures, the pore pressure increases and then becomes steady. However, the flow rate increases first and begins to decrease after the flow reaches the stop end of the fracture and finally vanishes. There is no obvious pore pressure or flow rate concentration within type III fractures.展开更多
Injection molding machine,hydraulic elevator,speed actuators belong to variable speed pump control cylinder system.Because variable speed pump control cylinder system is a nonlinear hydraulic system,it has some proble...Injection molding machine,hydraulic elevator,speed actuators belong to variable speed pump control cylinder system.Because variable speed pump control cylinder system is a nonlinear hydraulic system,it has some problems such as response lag and poor steady-state accuracy.To solve these problems,for the hydraulic cylinder of injection molding machine driven by the servo motor,a fractional order proportion-integration-diferentiation(FOPID)control strategy is proposed to realize the speed tracking control.Combined with the adaptive differential evolution algorithm,FOPID control strategy is used to determine the parameters of controller on line based on the test on the servo-motor-driven gear-pump-controlled hydraulic cylinder injection molding machine.Then the slef-adaptive differential evolution fractional order PID controller(SADE-FOPID)model of variable speed pump-controlled hydraulic cylinder is established in the test system with simulated loading.The simulation results show that compared with the classical PID control,the FOPID has better steady-state accuracy and fast response when the control parameters are optimized by the adaptive differential evolution algorithm.Experimental results show that SADE-FOPID control strategy is effective and feasible,and has good anti-load disturbance performance.展开更多
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （S...In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.展开更多
基金Project（BK20150005） supported by the Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars, China Project（2015XKZD05） supported by the Fundamental Research Funds for the Central Universities, China
文摘Permeability is a vital property of rock mass, which is highly affected by tectonic stress and human engineering activities. A comprehensive monitoring of pore pressure and flow rate distributions inside the rock mass is very important to elucidate the permeability evolution mechanisms, which is difficult to realize in laboratory, but easy to be achieved in numerical simulations. Therefore, the particle flow code （PFC）, a discrete element method, is used to simulate permeability behaviors of rock materials in this study. Owe to the limitation of the existed solid-fluid coupling algorithm in PFC, an improved flow-coupling algorithm is presented to better reflect the preferential flow in rock fractures. The comparative analysis is conducted between original and improved algorithm when simulating rock permeability evolution during triaxial compression, showing that the improved algorithm can better describe the experimental phenomenon. Furthermore, the evolution of pore pressure and flow rate distribution during the flow process are analyzed by using the improved algorithm. It is concluded that during the steady flow process in the fractured specimen, the pore pressure and flow rate both prefer transmitting through the fractures rather than rock matrix. Based on the results, fractures are divided into the following three types： I） fractures link to both the inlet and outlet, II） fractures only link to the inlet, and III） fractures only link to the outlet. The type I fracture is always the preferential propagating path for both the pore pressure and flow rate. For type II fractures, the pore pressure increases and then becomes steady. However, the flow rate increases first and begins to decrease after the flow reaches the stop end of the fracture and finally vanishes. There is no obvious pore pressure or flow rate concentration within type III fractures.
基金National Natural Science Foundation of China(No.51675399)。
文摘Injection molding machine,hydraulic elevator,speed actuators belong to variable speed pump control cylinder system.Because variable speed pump control cylinder system is a nonlinear hydraulic system,it has some problems such as response lag and poor steady-state accuracy.To solve these problems,for the hydraulic cylinder of injection molding machine driven by the servo motor,a fractional order proportion-integration-diferentiation(FOPID)control strategy is proposed to realize the speed tracking control.Combined with the adaptive differential evolution algorithm,FOPID control strategy is used to determine the parameters of controller on line based on the test on the servo-motor-driven gear-pump-controlled hydraulic cylinder injection molding machine.Then the slef-adaptive differential evolution fractional order PID controller(SADE-FOPID)model of variable speed pump-controlled hydraulic cylinder is established in the test system with simulated loading.The simulation results show that compared with the classical PID control,the FOPID has better steady-state accuracy and fast response when the control parameters are optimized by the adaptive differential evolution algorithm.Experimental results show that SADE-FOPID control strategy is effective and feasible,and has good anti-load disturbance performance.
文摘In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.
