Two-phase flow and oxygen transport in the perforated gas diffusion layer of proton exchange membrane fuel cell

Liquid water transport in perforated gas diffusion layers (GDLs)is numerically investigated using a three-dimensional (3D)two-phase volume of fluid (VOF)model and a stochastic reconstruction model of GDL microstructures. Different perforation depths and diameters are investigated, in comparison with the GDL without perforation. It is found that perforation can considerably reduce the liquid water level inside a GDL. The perforation diameter (D = 100 μm)and the depth (H = 100 μm)show pronounced effect. In addition, two different perforation locations, i.e. the GDL center and the liquid water break-through point, are investigated. Results show that the latter perforation location works more efficiently. Moreover, the perforation perimeter wettability is studied, and it is found that a hydrophilic region around the perforation further reduces the water saturation. Finally, the oxygen transport in the partially-saturated GDL is studied using an oxygen diffusion model. Results indicate that perforation reduces the oxygen diffusion resistance in GDLs and improves the oxygen concentration at the GDL bottom up to 101% (D = 100 μm and H = 100 μm)

Investigating the in-/through-plane effective diffusivities of dry and partially-saturated gas diffusion layers

In this study, the effective oxygen diffusivity in the dry or partially-saturated gas diffusion layer (GDL) is numerically investigated by an oxygen diffusion model in GDLs reconstructed by a stochastic method. The predicted effective diffusivity in dry GDLs is compared with various diffusivity models from literatures. Reasonable agreements with other models were obtained. The effect of the PTFE loading in the dry Toray carbon paper is also investigated and compared with recent experimental data. It is found that the effective diffusivity becomes lower under higher PTFE loading due to the decreased pore volume, as expected. The relative effective oxygen diffusivity in partially-saturated GDLs is calculated using the two-phase volume of fluid (VOF) model and an oxygen diffusion model. The effects of different local water profiles and porosity distribution on the effective oxygen diffusivity in both the through-plane (TP) and in-plane (IP) directions are investigated and compared with a lattice Boltzmann model and experimental data. The present results are in good agreement with other studies. It is found that local water profile has significant impacts on the effective diffusivity in partially-saturated GDLs and the diffusivity in the TP direction is more sensitive to the water distribution than the IP direction

Numerical simulation of two-phase cross flow in the gas diffusion layer microstructure of proton exchange membrane fuel cells

The cross flow in the under-land gas diffusion layer (GDL) between 2 adjacent channels plays an important role on water transport in proton exchange membrane fuel cell. A 3-dimensional (3D) two-phase model that is based on volume of fluid is developed to study the liquid water-air cross flow within the GDL between 2 adjacent channels. By considering the detailed GDL microstructures, various types of air-water cross flows are investigated by 3D numerical simulation. Liquid water at 4 locations is studied, including droplets at the GDL surface and liquid at the GDL-catalyst layer interface. It is found that the water droplet at the higher-pressure channel corner is easier to be removed by cross flow compared with droplets at other locations. Large pressure difference Δp facilitates the faster water removal from the higher-pressure channel. The contact angle of the GDL fiber is the key parameter that determines the cross flow of the droplet in the higher-pressure channel. It is observed that the droplet in the higher-pressure channel is difficult to flow through the hydrophobic GDL. Numerical simulations are also performed to investigate the water emerging process from different pores of the GDL bottom. It is found that the amount of liquid water removed by cross flow mainly depends on the pore's location, and the water under the land is removed entirely into the lower-pressure channel by cross flow

Searching for high-KK isomers in the proton-rich A∼80A\sim80 mass region

Configuration-constrained potential-energy-surface calculations have been performed to investigate the $K$ isomerism in the proton-rich $A\sim80$ mass region. An abundance of high-$K$ states are predicted. These high-$K$ states arise from two and four-quasi-particle excitations, with $K^{\pi}=8^{+}$ and $K^{\pi}=16^{+}$, respectively. Their excitation energies are comparatively low, making them good candidates for long-lived isomers. Since most nuclei under studies are prolate spheroids in their ground states, the oblate shapes of the predicted high-$K$ states may indicate a combination of $K$ isomerism and shape isomerism

Defining Species When There is Gene Flow

Whatever one’s definition of species, it is generally expected that individuals of the same species should be genetically more similar to each other than they are to individuals of another species. Here, we show that in the presence of cross-species gene flow, this expectation may be incorrect. We use the multispecies coalescent model with continuous-time migration or episodic introgression to study the impact of gene flow on genetic differences within and between species and highlight a surprising but plausible scenario in which different population sizes and asymmetrical migration rates cause a genetic sequence to be on average more closely related to a sequence from another species than to a sequence from the same species. Our results highlight the extraordinary impact that even a small amount of gene flow may have on the genetic history of the species. We suggest that contrasting long-term migration rate and short-term hybridization rate, both of which can be estimated using genetic data, may be a powerful approach to detecting the presence of reproductive barriers and to define species boundaries.[Gene flow; introgression; migration; multispecies coalescent; species concept; species delimitation.

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.Comment: MICCAI 2017 Camera Read