Kinetic modelling of runaway electron generation in argon-induced disruptions in ASDEX Upgrade

Massive material injection has been proposed as a way to mitigate the formation of a beam of relativistic runaway electrons that may result from a disruption in tokamak plasmas. In this paper we analyse runaway generation observed in eleven ASDEX Upgrade discharges where disruption was triggered using massive gas injection. We present numerical simulations in scenarios characteristic of on-axis plasma conditions, constrained by experimental observations, using a description of the runaway dynamics with a self-consistent electric field and temperature evolution in two-dimensional momentum space and zero-dimensional real space. We describe the evolution of the electron distribution function during the disruption, and show that the runaway seed generation is dominated by hot-tail in all of the simulated discharges. We reproduce the observed dependence of the current dissipation rate on the amount of injected argon during the runaway plateau phase. Our simulations also indicate that above a threshold amount of injected argon, the current density after the current quench depends strongly on the argon densities. This trend is not observed in the experiments, which suggests that effects not captured by zero-dimensional kinetic modelling – such as runaway seed transport – are also important.

An On-Device Deep Learning Approach to Battery Saving on Industrial Mobile Terminals

The mobile terminals used in the logistics industry can be exposed to wildly varying environments, which may hinder effective operation. In particular, those used in cold storages can be subject to frosting in the scanner window when they are carried out of the warehouses to a room-temperature space outside. To prevent this, they usually employ a film heater on the scanner window. However, the temperature and humidity conditions of the surrounding environment and the temperature of the terminal itself that cause frosting vary widely. Due to the complicated frost-forming conditions, existing industrial mobile terminals choose to implement rather simple rules that operate the film heater well above the freezing point, which inevitably leads to inefficient energy use. This paper demonstrates that to avoid such waste, on-device artificial intelligence (AI) a.k.a. edge AI can be readily employed to industrial mobile terminals and can improve their energy efficiency. We propose an artificial-intelligence-based approach that utilizes deep learning technology to avoid the energy-wasting defrosting operations. By combining the traditional temperature-sensing logic with a convolutional neural network (CNN) classifier that visually checks for frost, we can more precisely control the defrosting operation. We embed the CNN classifier in the device and demonstrate that the approach significantly reduces the energy consumption. On our test terminal, the net ratio of the energy consumption by the existing system to that of the edge AI for the heating film is almost 14:1. Even with the common current-dissipation accounted for, our edge AI system would increase the operating hours by 86%, or by more than 6 h compared with the system without the edge AI.

Stray Current Induced Corrosion Control in Reinforced Concrete by Addition of Carbon Fiber and Silica Fume

ABSTRACTStray current arising from direct current electrified traction systems and then circulating in reinforced concrete near railways is known to induce corrosion on embedded steel reinforcement. The present paper will review the principles of stray current induced corrosion in reinforced concrete, which is relatively uncommon but with significant impact in practice.Within one of the approaches to ease this kind of specific corrosion in reinforced concrete, carbon fibres (CF) can be added to enhance the conductivity of concrete, subsequently reduce the stray current density and/or direct the stray current dissipation in a desired manner. The side effects (such as increasing the bulk matrix porosity) caused by CF, which can in turn reduce the general corrosion resistance of reinforced concrete, will be compensated by adding silica fume (SF). The combination of CF and SF can be a potentially feasible and original application to reduce the risk of stray current induced corrosion in reinforced concrete, without obvious negative side effects.