Investigation of electron cyclotron current drive efficiency on the J-TEXT tokamak

Electron cyclotron current drive(ECCD) efficiency research is of great importance for the neoclassical tearing mode(NTM) stabilization.Improving ECCD efficiency is beneficial for the NTM stabilization and the ECCD power threshold reduction.ECCD efficiency has been investigated on the J-TEXT tokamak.The electron cyclotron wave(ECW) power scan was performed to obtain the current drive efficiency.The current drive efficiency is derived to be approximately η_(0)=(0.06-0.16)×10^(19)A m^(-2)W^(-1)on the J-TEXT tokamak.The effect of the residual toroidal electric field has been included in the determination of the current drive efficiency,which will enhance the ECCD efficiency.At the plasma current of I_(p)=100 kA and electron density of n_(e)=1.5×10^(19)m^(-3),the ratio of Spitzer conductivity between omhic(OH)and ECCD phases is considered and the experimental data have been corrected.The correction results show that the current drive efficiency η_(1)caused by the fast electron hot conductivity decreases by approximately 79%.It can be estimated that the driven current is approximately 24 kA at 300 kW ECW power.

SINAMICS—the high efficiency drive technology for rolling mills

In the Metals Industry,interruption-free processes to produce high-quality end products are a prerequisite. The main drives together with the mill stands play a key role in the success of rolling mills.The crucial demands placed on the drive system are:high-dynamic performance of drive and its control system,ability to handle the process related overloads,smooth running,high availability,high efficiency,easy serviceability and ability to integrate seamlessly with the automation systems.With numerous reference installations and many years of experience Siemens VAI has the wider expertise and the portfolio to provide the right drive for every application. This paper examines the latest innovation,SINAMICS drive technology,for both new drive applications as well as for modernizing the existing drives in rolling mill applications like hot strips mills,plate mills,cold rolling mills,and long rolling mills.

A Study of Energy Conversion Efficiency Versus Plasma Density by Lower Hybrid Current Drive in HT-7 Tokamak

Ramp-up experiments by means of lower hybrid wave on HT-7 superconducting tokamak have been performed and analyzed. A ramp-up rate of over 300 kA/s is obtained and a conversion efficiency of over 1.0% has been achieved during the ramp-up phase. The study of the dependence of conversion efficiency on plasma density shows that the conversion efficiency is affected by the driven current, which is mainly dominated by the competition of impurity concentration with wave accessibility condition. In addition, the effect of current profile may play an important role in determining the conversion efficiency.

Numerical simulation of electron cyclotron current drive characteristics on EAST

Electron cyclotron current drive （ECCD） will be applied in the EAST tokamak during its the new campaign. In order to provide theoretical predictions for relevant physical experiments, some numerical simulations of ECCD with the parameters of EAST have been can＇ied out by using TORAY-GA code based on the understanding of ECCD mechanisms. ECCD efficiencies achieved in different plasma and electron cyclotron （EC） wave parameters are given. The dependences of ECCD characteristics on EC wave injection angle, toroidal magnetic field, plasma density, and temperature are presented and discussed.

Enhanced Lower Hybrid Current Drive Experiments on HT-7 Tokamak

Effective Lower Hybrid Current Driving (LHCD) and improved confinement experiments in higher plasma parameters (Ip> 200 kA, ne > 2 ×1013 cm-3, Te≥1keV) have been curried out in optimized LH wave spectrum and plasma parameters in HT - 7 superconducting tokamak. The dependence of current driving efficiency on LH power spectrum, plasma density ne and toroidal magnetic field BT has been obtained under optimal conditions. A good CD efficiency was obtained at higher plasma current and higher electron density. The improvement of the energy confinement time is accompanied with the increase in line averaged electron density, and in ion and electron temperatures. The highest current driving efficiency reached CD = IpneR/PRF ~ 1.05 × 1019 Am-2/W. Wave-plasma coupling was sustained in a good state and the reflective coefficient was less than 5%. The experiments have also demonstrated the ability of LH wave in the start-up and ramp-up of the plasma current. The measurement of the temporal distribution of plasma parameter shows that lower hybrid leads to a broader profile in plasma parameter. The LH power deposition profile and the plasma current density profile were modeled with a 2D Fokker-Planck code corresponding to the evolution process of the hard x-ray detector array.

A New Topology of a Variable Output-Voltage DC-DC Converter for Fuel Cell Vehicles

The aim of this paper is to present a new topology of a DC-DC power converter for conditioning the current and voltages behaviors of embarked energy sources used in electrical vehicles. The fuel cells in conjunction with ultra-capacitors have been chosen as the power supply. The originality of the proposed converter is to use a variable voltage of the DC bus of the vehicle. The goal is to allow a better energy management of the embedded sources onboard the vehicle by improving its energy efficiency. After presenting and explaining the topology of the converter, some simulation and experiments results are shown to highlight its different operation modes.

Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections

Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency.To address these problems,this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically,safely and efficiently.The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks.Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles.Markov decision process was employed to model the interaction between AVs and other vehicles,and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency.To verify the effectiveness of the proposed decision-making framework,the top three accident-prone crossing path crash scenarios at intersections were simulated,when different initial vehicle states were adopted for better generalization capability.The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.