Stack shut-down strategy optimisation of proton exchange membrane fuel cell with the segment stack technology

International audienceIn the previous researches, researchers mainly focus on the single cell which is far away from the practical application. In this paper, shut-down process is studied in a 5-cell stack with segment technology. In the unprotected group, the hydrogen/air boundary is observed, and the output voltage performance degrades greatly after 300 start-stop cycles. A 2-phase auxiliary load strategy is proposed to avoid the hydrogen/air boundary. The lifetime is extended. But a serious local starvation is observed during the shut-down process. And corrosion happened in the inlet region. To avoid the starvation, the second strategy is designed, which combines 2-phase auxiliary and air purge (2-phase loadand air purge strategy). With the new strategy, the degradation of the stack after 1500 cycles is acceptable, and the carbon corrosion in the inlet is effectively reduced. It could conclude that the hydrogen/air boundary is the main cause of the degradation of fuel cell during an unprotected shut-down process. And a strategy only with auxiliary load may suffer from the local starvation. The purge process can avoid the vacuum effect in the fuel cell caused by the auxiliary load. Therefore, adding an air purge during the shut-down process is promising in vehicle fuel cell

A virtual reality system to augment teaching of wood structure and protection

Medical students have enthusiastically embraced the use of virtual reality (VR) systems to help them understand the complex anatomy of body components. We hypothesize that students studying the structure and protection of wood will show similar acceptance of VR systems. We developed X-ray micro-CT models to show the distribution of silica in the Australian marine borer resistant timber, satinay and copper in treated pine. Students taking a course in wood protection used a VR device to explore the distribution of silica in satinay and copper in pine. Students were surveyed to assess their views on the system as a learning tool. The results showed that students were very positive about the VR system, and they frequently commented that the system was better than traditional methods at aiding understanding of wood structure/protection. We discuss the limitations and potential of our VR system as a learning tool for wood technology

Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction

Recently, Acritical Intelligent (AI) methodologies such as Long and Short-term Memory (LSTM) have been widely considered promising tools for engine performance calibration, especially for engine emission performance prediction and optimization, and Transformer is also gradually applied to sequence prediction. To carry out high-precision engine control and calibration, predicting long time step emission sequences is required. However, LSTM has the problem of gradient disappearance on too long input and output sequences, and Transformer cannot reflect the dynamic features of historic emission information which derives from cycle-by-cycle engine combustion events, which leads to low accuracy and weak algorithm adaptability due to the inherent limitations of the encoder-decoder structure. In this paper, considering the highly nonlinear relation between the multi-dimensional engine operating parameters the engine emission data outputs, an Embedding-Graph-Neural-Network (EGNN) model was developed combined with self-attention mechanism for the adaptive graph generation part of the GNN to capture the relationship between the sequences, improve the ability of predicting long time step sequences, and reduce the number of parameters to simplify network structure. Then, a sensor embedding method was adopted to make the model adapt to the data characteristics of different sensors, so as to reduce the impact of experimental hardware on prediction accuracy. The experimental results show that under the condition of long-time step forecasting, the prediction error of our model decreased by 31.04% on average compared with five other baseline models, which demonstrates the EGNN model can potentially be used in future engine calibration procedures

The producing of an ECR plasma using 2450MHz Whistler wave and the investigating of its parameters

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