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In IEC 61312-2 equations for the assessment of the magnetic fields inside structures due to a direct lightning strike are given. These equations are based on computer simulations for shields consisting of a single-layer steel grid of a given mesh width. Real constructions, however, contain at least two layers of reinforcement steel grids. The objective of this study was to experimentally determine the additional shielding effectiveness of a second reinforcement layer compared to a single-layer grid. To this end, simulated structures were set up in the high current laboratory. The structures consisted of cubic cages of 2 m side length with one or with two reinforcement grids, respectively. The structures were exposed to direct lightning currents representing the variety of anticipated lightning current waveforms. The magnetic fields and their derivatives at several positions inside the structure as well as the voltage between “floor” and “roof” in the center were determined for different current injection points. From these data the improvement of the shielding caused by a second reinforcement layer is derived.
Recognition of subjects with mild cognitive impairment (MCI) by the use of retinal arterial vessels.
(2019)
Concentrated Solar Power (CSP) systems are able to store energy cost-effectively in their integrated thermal energy storage (TES). By intelligently combining Photovoltaics (PV) systems with CSP, a further cost reduction of solar power plants is expected, as well as an increase in dispatchability and flexibility of power generation. PV-powered Resistance Heaters (RH) can be deployed to raise the temperature of the molten salt hot storage from 385 °C up to 565 °C in a Parabolic Trough Collector (PTC) plant. To avoid freezing and decomposition of molten salt, the temperature distribution in the electrical resistance heater is investigated in the present study. For this purpose, a RH has been modeled and CFD simulations have been performed. The simulation results show that the hottest regions occur on the electric rod surface behind the last baffle. A technical optimization was performed by adjusting three parameters: Shell-baffle clearance, electric rod-baffle clearance and number of baffles. After the technical optimization was carried out, the temperature difference between the maximum temperature and the average outlet temperature of the salt is within the acceptable limits, thus critical salt decomposition has been avoided. Additionally, the CFD simulations results were analyzed and compared with results obtained with a one-dimensional model in Modelica.
Smart pixel : photonic mixer device (PMD) ; new system concept of a 3D-imaging camera-on-a-chip
(1998)
An application of a scanning light-addressable potentiometric sensor for label-free DNA detection
(2013)
DNA-hybridization detection using light-addressable potentiometric sensor modified with gold layer
(2014)
Wireless systems for machinery safety : Requirements and solutions for wireless real time systems
(2015)
In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.