comparative-study-of-simulation-of-temperature-rise-in-ring-main-unit

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

The Ring Main Unit (RMU) is a critical device in power distribution systems used for connecting and distributing electricity. However, due to its compact ...

Wenzhou University

Wecome Intelligent Manufacturing Co., Ltd.

Nanjing University of Aeronautics and Astronautics

Excess water seriously affects the performance and lifetime of proton exchange membrane fuel cells (PEMFCs). This study proposes a novel compound flow field, which is named the active drainage flow field (ADFF). The new design enhances the drainage performance by under-ribs flow, while the output performance is very close to that of the conventional serpentine flow field (CSFF). Additionally, the ADFF bipolar plate has been taken as a cathode while retaining CSFF as an anode; the combination shows a good output performance under high relative humidity. The peak power density has reached to 0.59 W/cm, which is 13% higher than that of the CSFF.

Experimental and Numerical Study of Proton Exchange Membrane Fuel Cells with a Novel Compound Flow Field.

In developing a proton exchange membrane fuel cell, a high current density is encountered, thus requiring a novel flow field with great drainage performance. Our previous study proposed a novel compound flow field called an active drainage flow field with three inlets, which has an excellent output and drainage performance. Furthermore, the influence of muti-inlet reactant gas allocation on the three inlets is discussed in this study. The results showed that the small mass flow in the active drainage (AD) channel causes a waste of active area, while the large mass flow in the AD channel causes under-ribs convection. Considering the output and drainage performance, the 15% AD mass flow case shows the best performance under the high relative humidity investigation.

Effects of Inlet Gas Allocations on Performance of an Active Drainage Flow Field Proton Exchange Membrane Fuel Cell.

Hydroponic lettuce was prone to pest and disease problems after transplantation. Manual identification of the current growth status of each hydroponic lettuce not only consumed time and was prone to errors but also failed to meet the requirements of high-quality and efficient lettuce cultivation. In response to this issue, this paper proposed a method called YOLO-EfficientNet for identifying the growth status of hydroponic lettuce. Firstly, the video data of hydroponic lettuce were processed to obtain individual frame images. And 2240 images were selected from these frames as the image dataset A. Secondly, the YOLO-v8n object detection model was trained using image dataset A to detect the position of each hydroponic lettuce in the video data. After selecting the targets based on the predicted bounding boxes, 12,000 individual lettuce images were obtained by cropping, which served as image dataset B. Finally, the EfficientNet-v2s object classification model was trained using image dataset B to identify three growth statuses (Healthy, Diseases, and Pests) of hydroponic lettuce. The results showed that, after training image dataset A using the YOLO-v8n model, the accuracy and recall were consistently around 99%. After training image dataset B using the EfficientNet-v2s model, it achieved excellent scores of 95.78 for Val-acc, 94.68 for Test-acc, 96.02 for Recall, 96.32 for Precision, and 96.18 for F1-score. Thus, the method proposed in this paper had potential in the agricultural application of identifying and classifying the growth status in hydroponic lettuce.

Mingge Wu

College of Mechanical and Electrical Engineering,
Institute of Laser and Optoelectronic Intelligent Manufacturing

Wenzhou University

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

Mingge Wu

College of Mechanical and Electrical Engineering,Institute of Laser and Optoelectronic Intelligent Manufacturing

Wenzhou University

Comparative Study of Simulation of Temperature Rise in Ring Main Unit

College of Mechanical and Electrical Engineering,
Institute of Laser and Optoelectronic Intelligent Manufacturing