5,162 research outputs found
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
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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
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