Investigation of gas purging and cold storage impact on PEM fuel cell system performance for aeronautical applications

Durability of proton exchange membrane fuel cell systems under cold weather conditions is essential and a critical challenge for transportation applications. During cold storage the water remaining in the cells can freeze causing damage to the cell components. In order to avoid this degradation, fuel cells are commonly purged with dried gases during shutdown prior to its storage at subzero temperatures. This work investigates cold storage of PEMFC systems at temperatures down to -40°C with the aim of developing a shutdown procedure that leads to minimal degradation due to cold storage, while meets energy efficient and time requirements of aeronautical applications. To that end, several experiments were carried out with two different stacks (a 4 kW liquid cooled and a 100 W air cooled) under a wide range of operating parameters: cathode gas, purge temperature, anode and cathode gas purge flow rates, purge time and cold storage temperature. The fuel cell performance degradation due to ice formation was measured by the polarization curves conducted prior and after every F/T cycle. The effects of these operating parameters on the durability of the PEMFC systems under cold storage are evaluated. The obtained experimental results showed that very long purge process lead to further performance degradation at -10°C than shorter process at -40°C, which indicates that eliminating all remained water in the cells is not only inefficient, but also lead to degradation due to the drying process. Moreover, guidelines to improve shutdown procedure for cold storage of proton exchange membrane fuel cell systems for aeronautical applications are discussed.

Vesicle-based artificial cells: Materials, construction methods and applications

The construction of artificial cells with specific cell-mimicking functions helps to explore complex biological processes and cell functions in natural cell systems, and provides insight into the origins of life....

Abnormal Aggregation of Invasive Cancer Cells Induced by Collective Polarization and ECM-Mediated Mechanical Coupling in Coculture Systems

Studies on pattern formation in coculture cell systems can provide insights into many physiological and pathological processes. Here, we investigate how the extracellular matrix (ECM) may influence the patterning in coculture systems. The model coculture system we use is composed of highly motile invasive breast cancer cells, initially mixed with inert nonmetastatic cells on a 2D substrate and covered with a Matrigel layer introduced to mimic ECM. We observe that the invasive cells exhibit persistent centripetal motion and yield abnormal aggregation, rather than random spreading, due to a “collective pulling” effect resulting from ECM-mediated transmission of active contractile forces generated by the polarized migration of the invasive cells along the vertical direction. The mechanism we report may open a new window for the understanding of biological processes that involve multiple types of cells.

Dynamic LCA of a single-family house, equipped with a micro-cogeneration unit, using a variable share of biomethane

The current study presents the CO2-eq emissions of the operational energy use of a single-family house, equipped with a micro-cogeneration unit. A back-up boiler and electricity from the grid cover the remaining energy demand, not covered by the micro-CHP. Two different technologies are evaluated, i.e. ICE and fuel cell systems, operating with a variable share of biomethane, while two different substrates were considered for the biomethane generation. A dynamic LCA was applied for the electricity mix, coming from the grid, using different time steps. The results show that producing biomethane from biowaste compared to conventional natural gas is beneficial, in terms of CO2-eq emissions, independently of the micro-CHP technology, while the total CO2-eq emissions of the fuel cell technology are higher than those of the ICE, independently of the substrate and the biomethane share.

INTELLIGENT HEALTH STATUS DETECTION METHOD FOR LOCOMOTIVE FUEL CELL BASED ON DATA-DRIVEN TECHNIQUES

Main drawbacks of fuel cell systems, namely, high cost, poor reliability, and short lifespan, limit the large-scale commercial application of fuel cell systems. The health status detection of fuel cell systems for locomotives is of great significance to the safe and stable operation of locomotives. To identify the failure modes of the fuel cell system accurately and quickly, this study proposed an intelligent health status detection method for locomotive fuel cells based on data-driven techniques. In this study, the actual test data of a 150-kW fuel cell system for locomotives was analyzed. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was combined with the general regression neural network (GRNN) to intelligently detect the health status of the fuel cell system for locomotives. Specifically, t-SNE was used to process the high-dimensionality and strong coupling raw data of health status, enabling the dimensional reduction of the raw data to reflect essential features. Then, GRNN was used to identify the feature data to achieve the fast and accurate detection of the health status of the fuel cell system. Results show that the proposed method can effectively detect four health conditions, namely, normal state, high inlet coolant temperature, low air pressure, and low spray pump pressure, with a diagnostic accuracy of 98.75%. This study is applicable to the analysis of the actual measurement data of high-power level fuel cell systems and provides a reference for the health status detection of fuel cell systems for locomotives. Keywords: fuel cell system for locomotive; data-driven; general regression neural network; t-distributed stochastic neighbor embedding; health status detection