Characterization Of Porous Media In Proton Exchange Membrane Fuel Cell Based on Percolation Studies

Water management in the porous media of proton exchange membrane (PEM) fuel cells, catalyst layer and porous transport layers (PTL) is confronted by two issues, flooding and dry out, both of which result in improper functioning of the fuel cell and lead to poor performance and degradation. The data that has been reported about water percolation and wettability within a fuel cell catalyst layer is limited to porosimetry. A new method and apparatus for measuring the percolation pressure in the catalyst layer has been developed. The experimental setup is similar to a Hele-Shaw experiment where samples are compressed and a fluid is injected into the sample. Pressure-Wetted Volume plots as well as Permeability plots for the catalyst layers were generated from the percolation testing. PTL samples were also characterizes using a Hele-Shaw method. Characterization for the PTLs was completed for the three states: new, conditioned and aged. This is represented in a Ce-t* plots, which show a large offset between new and aged samples.

SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.