Equivalent stiffness model of a proton exchange membrane fuel cell stack including hygrothermal effects and dimensional tolerances

Proton exchange membrane fuel cells (PEMFCs) require mechanical compression to ensure structural integrity, prevent leakage, and to minimize the electrical contact resistance. The mechanical properties and dimensions of the fuel cell vary during assembly due to manufacturing tolerances and during operation due to both temperature and humidity. Variation in stack compression affects the interfacial contact pressures between components and hence fuel cell performance. This paper presents a one-dimensional equivalent stiffness model of a PEMFC stack capable of predicting independent membrane and gasket contact pressures for an applied external load. The model accounts for nonlinear component compression behavior, thickness variation due to manufacturing tolerances, thermal expansion, membrane expansion due to water uptake, and stack dimensional change due to clamping mechanism stiffness. The equivalent stiffness model is compared to a three-dimensional (3D) finite element model, showing good agreement for multicell stacks. Results demonstrate that the correct specification of gasket thickness and stiffness is essential in ensuring a predictable membrane contact pressure, adequate sealing, and avoiding excessive stresses in the bi-polar plate (BPP). Increase in membrane contact pressure due to membrane water uptake is shown to be significantly greater than the increase due to component thermal expansion in the PEMFC operating range. The predicted increase in membrane contact pressure due to thermal and hydration effects is 18% for a stack containing fully hydrated Nafion® 117 membranes at 80 °C, 90% relative humidity (RH) using an eight bolt clamping design and a nominal 1.2 MPa assembly pressure

Thermophoretic deposition of particles in fully developed mixed convection flow in a parallel-plate vertical channel: the full analytical solution

In a recent paper (Grosan et al. in Heat Mass Transf 45:503-509, 2009) a mostly numerical approach to the title problem has been reported. In the present paper the full analytical solution is given. Several new features emerging from this approach are discussed in detai

Energy- and exergy-based working fluid selection and performance analysis of a high-temperature PEMFC-based micro combined cooling heating and power system

A combined cooling heating and power (CCHP) system based on high-temperature proton exchange membrane fuel cell (PEMFC) is proposed. This CCHP system consists of a PEMFC subsystem, an organic Rankine cycle (ORC) subsystem and a vapor compression cycle (VCC) subsystem. The electric power of the CCHP system is 8 kW under normal operating conditions, the domestic hot water power is approximately 18 kW, and the cooling and heating capacities are 12.5 kW and 20 kW, respectively. Energy and exergy performance of the CCHP system are thoroughly analyzed for six organic working fluids using Matlab coupled with REFPROP. R601 is chosen as the working fluid for ORC subsystem based on energy and exergy analysis. The results show that the average coefficient of performance (COP) of the CCHP system is 1.19 in summer and 1.42 in winter, and the average exergy efficiencies are 46% and 47% under normal operating conditions. It can also be concluded that both the current density and operating temperature have significant effects on the energy performance of the CCHP system, while only the current density affects the exergy performance noticeably. The ambient temperature can affect both the energy and exergy performance of the CCHP system. This system has the advantages of high facility availability, high efficiency, high stability, low noise and low emission; it has a good prospect for residential applications

Pearson correlation coefficient (<i>r</i>) and AUROC for the affymetrix dataset (AUROC in columns 3 and 4).

<p>Pearson correlation coefficient (<i>r</i>) and AUROC for the affymetrix dataset (AUROC in columns 3 and 4).</p

Plot of average MAD for different sample size.

<p>Plot of average MAD calculated from varying the sample size for all the methods (simulated datasets).</p

Plot of proportions.

<p>Plot of the true proportions vs. estimated proportions obtained from the PNMF method (GSE19830 dataset).</p

Pearson correlation coefficient (<i>r</i>) for the GSE19830 dataset.

<p>Pearson correlation coefficient (<i>r</i>) for the GSE19830 dataset.</p

Effect of the choice of priors for the proposed SMC algorithm.

<p>Effect of the choice of priors for the proposed SMC algorithm.</p

Runtime of different methods on the same dataset.

<p>Runtime of different methods on the same dataset.</p

Jurkat > IM-9.

<p>ROC plot obtained from the proposed SMC method for Jurkat vs. IM-9 cell types, Jurkat upregulated (GSE11058 dataset).</p