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In this study, a proton exchange membrane (PEM) fuel cell stack composed of five cells in series is numerically investigated to study the impact of the nonuniform reactant flow rate on the performa...

Many researchers have taken interests in time series data mining to discover potential knowledge and information as the amount of data from various domains rapidly increases. Representation, as a necessary implementation component of data mining, is critical to reduce the high dimensionality of time series data and generate a corresponding distance measure to process time series data effectively and efficiently. Many high-level representation approaches for mining time series data have been proposed in the past decades, e.g., PAA, SAX, PWCA and 2D-NCR. In this paper, a novel representation method for time series data, which is named Three-Dimensional Piecewise Cloud Representation (TDPCR), is proposed. The new representation contains a flexible partitioning strategy which protects the connection information between consecutive points by overlapping two adjacent segments. Using the improved cloud model theory, the proposed representation achieves the reduction of the data dimensionality and captures distribution and variation features of segments. Furthermore, a new distance measure, which has adaptive weight factors to adjust the proportion of data information, is defined to describe the relationship between two three-dimensional clouds. Accompanied with the comparisons of state-of-the-art representation methods, a sufficient performance evaluation for the proposed representation is carried out in the classification and query by content tasks. The experimental results show that TDPCR is effective and competitive on most of datasets from several domains.

Membrane is one of the most important components in proton exchange membrane fuel cells (PEMFCs), which determines the transport phenomena, performance, and durability. With the rapid development of novel membranes, many transport coefficients in membranes applied in numerical studies are outdated due to the lack of experimental data for new membranes. In this review, the fundamentals of commercially available membranes are scrutinized, followed by the fundamental working mechanisms. A detailed examination of the transport phenomena within the membranes, including transport mechanisms, mathematical description, and experimental methods, is conducted for protonic conduction, electro-osmosis drag, diffusion, hydraulic permeation, and gas crossover, which are urgently needed for theoretical and numerical studies. It is found that various empirical or analytical correlations have been established to predict the transport coefficients of the membranes. However, empirical models may not be accurate for all types of membranes since there is no sufficient experimental data for a solid correlation and validation. The experimental methods reviewed in the present study can be applied for new membranes, which is essential to quantify the transport phenomena and its further impact on cell performance and durability. The key transport-phenomena-related factors that affect the performance and failure modes of membranes are also reviewed in this study, which helps to develop strategies in improving membranes’ performance and durability during operation. This review deepens the understanding of the short-term and long-term performance of the membrane in PEMFCs and provides important insights into the further design of novel membranes.

It is necessary to investigate the designs of model to maximize the performance of proton exchange membrane fuel cell (PEMFC). In this study, a novel design and modeling method are proposed based on different flow fields. The proposed model considers the voltage losses caused by the current leakage, gas crossover, and by-product pollution. A regression analysis is conducted to study the influence of the working conditions on the accuracy. Final results showed that the coefficient of determination increased both from 0.977 to 0.952 to 0.998 at 50 °C and 70 °C, respectively. The prediction accuracy for the open-circuit voltage and low-current density is significantly improved. In addition, the prediction accuracy of different impedance materials decreases with increasing temperature and pressure.

The aim of this study was to reveal the mechanisms and mutual relationships of the lettuce resistance to Botrytis cinerea under the different combinations treatments of copper, zinc, iron, and manganese compounds. The results of this research confirmed: The copper sulfate 0.16 mg/L, zinc sulfate 0.22 mg/L, EDTA-iron 60 mg/L, and manganese sulfate 3.20 mg/L, were added to a Yamasaki nutrient solution, which lead to the antioxidant capacity of the lettuce leaves became stronger. Following the inoculated-pathogen treatment, the contents of superoxide anion radical (O2.-), Hydrogen peroxide (H2O2), and malondialdehyde (MDA) in the lettuce leaves were decreased. The superoxide dismutase (SOD) played a dominant role in eliminating the influence of O2.-. The elimination of the H2O2 , and the maintenance of a certain level was the combined effect of the catalase (CAT), ascorbate peroxidase (APX), and glutathione peroxidase (GPX), the CAT was the dominant enzyme, the APX and CAT enzymes showed a synergic action, and the GPX and CAT enzymes were complementary. The copper, zinc, iron, and manganese were found to have affected the mechanism of this reaction. Therefore, it was a feasible approach to add the correct amount of metal elements into the Yamasaki nutrient solution, which would potentially improve the resistance of the lettuce to Botrytis cinerea and activate its antioxidant enzymes.