Influence of wettability on liquid water transport in gas diffusion layer of proton exchange membrane fuel cells (PEMFC)

Water management is a key factor that limits PEFC's performance. We show how insights into this problem can be gained from pore-scale simulations of water invasion in a model fibrous medium. We explore the influence of contact angle on the water invasion pattern and water saturation at breakthrough and show that a dramatic change in the invasion pattern, from fractal to compact, occurs as the system changes from hydrophobic to hydrophilic. Then, we explore the case of a system of mixed wettability, i.e. containing both hydrophilic and hydrophobic pores. The saturation at breakthrough is studied as a function of the fraction of hydrophilic pores. The results are discussed in relation with the water management problem, the optimal design of a GDL and the fuel cell performance degradation mechanisms. We outline how the study could be extended to 3D systems, notably from binarised images of GDLs obtained by X ray microtomography

On the relevance of bubbles and potential flows for stellar convection

Recently Pasetto et al. have proposed a new method to derive a convection theory appropriate for the implementation in stellar evolution codes. Their approach is based on the simple physical picture of spherical bubbles moving within a potential flow in dynamically unstable regions, and a detailed computation of the bubble dynamics. Based on this approach the authors derive a new theory of convection which is claimed to be parameter free, non-local and time-dependent. This is a very strong claim, as such a theory is the holy grail of stellar physics. Unfortunately we have identified several distinct problems in the derivation which ultimately render their theory inapplicable to any physical regime. In addition we show that the framework of spherical bubbles in potential flows is unable to capture the essence of stellar convection, even when equations are derived correctly.Comment: 14 pages, 3 figures. Accepted for publication in Monthly Notices of the Royal Astronomical Society. (Comments and criticism are welcomed

A Bayesian approach for statistical–physical bulk parameterization of rain microphysics. Part II: Idealized Markov chain Monte Carlo experiments

Observationally informed development of a new framework for bulk rain microphysics, the Bayesian Observationally Constrained Statistical–Physical Scheme (BOSS; described in Part I of this study), is demonstrated. This scheme’s development is motivated by large uncertainties in cloud and weather simulations associated with approximations and assumptions in existing microphysics schemes. Here, a proof-of-concept study is presented using a Markov chain Monte Carlo sampling algorithm with BOSS to probabilistically estimate microphysical process rates and parameters directly from a set of synthetically generated rain observations. The framework utilized is an idealized steady-state one-dimensional column rainshaft model with specified column-top rain properties and a fixed thermodynamical profile. Different configurations of BOSS—flexibility being a key feature of this approach—are constrained via synthetic observations generated from a traditional three-moment bulk microphysics scheme. The ability to retrieve correct parameter values when the true parameter values are known is illustrated. For cases when there is no set of true parameter values, the accuracy of configurations of BOSS that have different levels of complexity is compared. It is found that addition of the sixth moment as a prognostic variable improves prediction of the third moment (proportional to bulk rain mass) and rain rate. In contrast, increasing process rate formulation complexity by adding more power terms has little benefit—a result that is explained using further-idealized experiments. BOSS rainshaft simulations are shown to well estimate the true process rates from constraint by bulk rain observations, with the additional benefit of rigorously quantified uncertainty of these estimates

Quasi full-disk maps of solar horizontal velocities using SDO/HMI data

For the first time, the motion of granules (solar plasma on the surface on scales larger than 2.5 Mm) has been followed over the entire visible surface of the Sun, using SDO/HMI white-light data. Horizontal velocity fields are derived from image correlation tracking using a new version of the coherent structure tracking algorithm.The spatial and temporal resolutions of the horizontal velocity map are 2.5 Mm and 30 min respectively . From this reconstruction, using the multi-resolution analysis, one can obtain to the velocity field at different scales with its derivatives such as the horizontal divergence or the vertical component of the vorticity. The intrinsic error on the velocity is ~0.25 km/s for a time sequence of 30 minutes and a mesh size of 2.5 Mm.This is acceptable compared to the granule velocities, which range between 0.3 km/s and 1.8 km/s. A high correlation between velocities computed from Hinode and SDO/HMI has been found (85%). From the data we derive the power spectrum of the supergranulation horizontal velocity field, the solar differential rotation, and the meridional velocity.Comment: 8 pages, 11 figures, accepted in Astronomy and Astrophysic