With the structure of two air gaps and two rotors, the electromagnetic continuously variable transmission（EMCVT） is a novel power-split continuously variable transmission（CVT）. There are two kinds of power flowing...With the structure of two air gaps and two rotors, the electromagnetic continuously variable transmission（EMCVT） is a novel power-split continuously variable transmission（CVT）. There are two kinds of power flowing through the EMCVT, one is mechanical power and the other is electric power. In the mean time, there are three power ports in the EMCVT, one is the outer rotor named mechanical power port and the other two are the inner rotor and the stator named electric power ports. The mechanical power port is connected to the driving wheels through the final gear and the electric ports are connected to the batteries through the transducers. The two kinds of power are coupled on the outer rotor of the EMCVT. The EMCVT can be equipped on the conventional vehicle being regarded as the CVT and it also can be equipped on the hybrid electric vehicle（HEV） as the multi-energy sources assembly. The power flows of these two kinds of applications are analysed. The back electromotive force（EMF） equations are illatively studied and so the dynamic mathematic model is theorized. In order to certify the feasibility of the above theories, three simulations are carried out in allusion to the above two kinds of mentioned applications of the EMCVT and a five speed automatic transmission（AT） vehicle. The simulation results illustrate that the efficiency of the EMCVT vehicles is higher than that of the AT vehicle owed to the optimized operation area of the engine. Hence the fuel consumption of the EMCVT vehicles is knock-down.展开更多
Back-streaming electrons gain significant energy due to the high voltage of the extraction system for a high-current ion source.By theoretical calculation,the particle flux accounts for 13.88% of the total beam curren...Back-streaming electrons gain significant energy due to the high voltage of the extraction system for a high-current ion source.By theoretical calculation,the particle flux accounts for 13.88% of the total beam current,and the power flux accounts for about 7.5% of the total beam power.This shows that back-streaming electrons are very destructive to the plate of electron absorption that is installed opposite of the accelerator.At the same time,as particles impinge on grids,the energy level that the grids absorb will be really high.Compared with the water flow calorimetry data of ion sources on the ASIPP-NBI testbed,it can be found that,as the high voltage of the extraction system rises,the particle flux and the power flux of the back-streaming electrons are essentially in the same proportions.Therefore,the corresponding energy deposited on the components of the ion source will grow by the same percentage with the increase in high voltage,which demonstrates strong inhibition to improving the neutral beam power injected into a tokamak.展开更多
The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃...The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.展开更多
基金supported by National Natural Science Foundation of China(No.50605020)Guangdong Provincial Science and Technology Project of China(No.2006A10501001).
文摘With the structure of two air gaps and two rotors, the electromagnetic continuously variable transmission（EMCVT） is a novel power-split continuously variable transmission（CVT）. There are two kinds of power flowing through the EMCVT, one is mechanical power and the other is electric power. In the mean time, there are three power ports in the EMCVT, one is the outer rotor named mechanical power port and the other two are the inner rotor and the stator named electric power ports. The mechanical power port is connected to the driving wheels through the final gear and the electric ports are connected to the batteries through the transducers. The two kinds of power are coupled on the outer rotor of the EMCVT. The EMCVT can be equipped on the conventional vehicle being regarded as the CVT and it also can be equipped on the hybrid electric vehicle（HEV） as the multi-energy sources assembly. The power flows of these two kinds of applications are analysed. The back electromotive force（EMF） equations are illatively studied and so the dynamic mathematic model is theorized. In order to certify the feasibility of the above theories, three simulations are carried out in allusion to the above two kinds of mentioned applications of the EMCVT and a five speed automatic transmission（AT） vehicle. The simulation results illustrate that the efficiency of the EMCVT vehicles is higher than that of the AT vehicle owed to the optimized operation area of the engine. Hence the fuel consumption of the EMCVT vehicles is knock-down.
基金supported by National Natural Science Foundation of China(No.11405207,No.11505225 and No.11675215)partly supported by the International Science and Technology Cooperation Program of China(No. 2014DFG61950)the Sciences foundation of ASIPP(No. DSJJ-15-GC03)
文摘Back-streaming electrons gain significant energy due to the high voltage of the extraction system for a high-current ion source.By theoretical calculation,the particle flux accounts for 13.88% of the total beam current,and the power flux accounts for about 7.5% of the total beam power.This shows that back-streaming electrons are very destructive to the plate of electron absorption that is installed opposite of the accelerator.At the same time,as particles impinge on grids,the energy level that the grids absorb will be really high.Compared with the water flow calorimetry data of ion sources on the ASIPP-NBI testbed,it can be found that,as the high voltage of the extraction system rises,the particle flux and the power flux of the back-streaming electrons are essentially in the same proportions.Therefore,the corresponding energy deposited on the components of the ion source will grow by the same percentage with the increase in high voltage,which demonstrates strong inhibition to improving the neutral beam power injected into a tokamak.
文摘The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.
