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.

A COMPARISON OF PIXEL-BASED VERSUS OBJECT ORIENTED ANALYSIS OF LANDSLIDES USING HISTORICAL REMOTE SENSING DATA

With recent advances in remote sensing processing technology, it has become more feasible to begin analysis of the enormous historic archive of remotely sensed data. This historical data provides valuable information on a wide variety of topics which can influence the lives of millions of people if processed correctly and in a timely manner. One such field of benefit is that of landslide mapping and inventory. This data provides a historical reference to those who live near high risk areas so future disasters may be avoided. In order to properly map landslides remotely, an optimum method must first be determined. Historically, mapping has been attempted using pixel based methods such as unsupervised and supervised classification. These methods are limited by their ability to only characterize an image spectrally based on single pixel values. This creates a result prone to false positives and often without meaningful objects created. Recently, several reliable methods of Object Oriented Analysis (OOA) have been developed which utilize a full range of spectral, spatial, textural, and contextual parameters to delineate regions of interest. A comparison of these two methods on a historical dataset of the landslide affected city of San Juan La Laguna, Guatemala has proven the benefits of OOA methods over those of unsupervised classification. Overall accuracies of 96.5% and 94.3% and F-score of 84.3% and 77.9% were achieved for OOA and unsupervised classification methods respectively. The greater difference in F-score is a result of the low precision values of unsupervised classification caused by poor false positive removal, the greatest shortcoming of this method.