Microstructure Sensitive Design and Quantitative Prediction of Effective Conductivity in Fuel Cell Design

Hamid Garemstani; Dong Sheng Li; Moe A. Khaleel

doi:10.4028/www.scientific.net/MSF.561-565.315

Paper Titles

Phase Analysis for Mg-Zn-Y Magnesium Alloys by Using Extraction Technology
p.299

HRTEM Observation of Precipitates at Early Stage of Aging in Mg-Gd-Zr Alloy
p.303

HRTEM Observation of the Age Hardening Precipitates in Mg-Gd-Sc Alloy
p.307

Changes in the Mechanical Properties and Microstructure of AZ91 Cast Mg Alloy Caused by Heat Treatment
p.311

Microstructure Sensitive Design and Quantitative Prediction of Effective Conductivity in Fuel Cell Design
p.315

Effects of Various Thermal and Environmental Exposure on the Mechanical Properties of Aluminum Alloys
p.319

Effect of Initial Microstructure under Different Solidification Conditions on Hot Working Behavior of AZ31 Alloy
p.325

Effect of TiH₂ on the Microstructures and Refining Performance of Al-Ti-C Master Alloy
p.329

Solute Content and the Tensile Behavior of High Pressure Die Cast Mg-Al Alloys
p.333

HomeMaterials Science ForumMaterials Science Forum Vols. 561-565Microstructure Sensitive Design and Quantitative...

Microstructure Sensitive Design and Quantitative Prediction of Effective Conductivity in Fuel Cell Design

Abstract:

Statistical continuum approach is used to predict effective conductivity of anisotropic random porous heterogeneous media using two-point correlation functions. Probability functions play a critical role in describing the statistical distribution of different constituents in a heterogeneous media. In this study a 3-dimensional two-point correlation function is utilized to characterize the anisotropic porous media of a Cathode materials to incorporate all the details of the microstructure. These correlation functions are then linked to the effective properties using homogenization relations. An anisotropioc Green’s function solution is used to solve the set of field equations. Examples in this study demonstrated how the model captured the anisotropy in effective conductivity of the random heterogeneous media. Predicted results showed the influence of microstructure on the effective conductivity tensor.