Thermally-regenerative ammonia-based flow battery (TR-AFB) is considered to be an efficient way to harvest low-grade waste heat, but its performace such as power density and voltage are still need to improve. In this paper, an isothermal three-dimension (3-D) numerical model for TR-AFB has been proposed in order to optimize its performance. Firstly, the concentration distribution of species in the flow channel of TR-AFB and effects of different flow forms on power production are described. The results indicate that dead zones are found in the initial experimental flow channel, which greatly affect the performance of the battery. Therefore, in order to improve the performance, flow channels of TR-AFB have been optimized using silicon rubber sheet channel and copper electrode flow channel. According to the optimization results, it is found that the maximum power density of the battery can be increased by about 33% (12.1 W m −2 ), while the pump power consumption only increase by 2.13 × 10 −2 W m −2 . By comparing the performance of batteries with different flow channels, the general laws of designing flow channel are summarized. That is to minimize the flow dead zones, increase the reaction areas and reduce the distance between electrodes within the allowable range.
Abstract-Currently numerous theories and model have been developed to associate various findings or in relating EEG patterns to develop a software simulator. Here we develop a hardware simulator of the EEG model or to simulator any EEG data set in either .edf or .tdmsot .txtformat from any patient or database depository. The proposed hardware simulator will enhance researchers and hardware validators to simulate, validate and test their detection algorithms forehand, before actual testing the algorithm in the actual standalone hardware. This system make use of signal generator block and then pass this data to the external hardware data acquisition system like the NI-DAQ with an external option to transfer the data wirelessly(Bluetooth, Zigbee, Wi-Fi) or wired (analog port, serial bus etc). This simulator can simulate or generate seizure, pre-seizure and normal EEG waveform. The paired cost effective Arduino microcontroller (in case of wireless system) will be having the algorithm in built in order to classify the type of signal received. This can help in developing wearable EEG Seizure monitoring system(WBAN-HL7). Thispaper will enhance the purpose of developing a system which can alert locally in a form of wearable gadget, whenever a pre-seizure occurs. This can help the epileptic patient or the user to take precautionary action to save themselves from accidents or injury, just before the occurrence of the seizure. Useable of this embedded wearable version can ensure a better everyday activities and the psychological stress can be reduces to leverage the social interaction.
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