Numerical prediction of load-displacement behaviors of adhesively bonded joints at different extension rates and temperatures

The present work focuses on simulation of nonlinear mechanical behaviors of adhesively bonded DLS (double lap shear) joints for variable extension rates and temperatures using the implicit ABAQUS solver. Load-displacement curves of DLS joints at nine combinations of extension rates and environmental temperatures are initially obtained by conducting tensile tests in a UTM. The joint specimens are made from dual phase (DP) steel coupons bonded with a rubber-toughened adhesive. It is shown that the shell-solid model of a DLS joint, in which substrates are modeled with shell elements and adhesive with solid elements, can effectively predict the mechanical behavior of the joint. Exponent Drucker-Prager or Von Mises yield criterion together with nonlinear isotropic hardening is used for the simulation of DLS joint tests. It has been found that at a low temperature (-20 degrees C), both Von Mises and exponent Drucker-Prager criteria give close prediction of experimental load-extension curves. However. at a high temperature (82 degrees C), Von Mises condition tends to yield a perceptibly softer joint behavior, while the corresponding response obtained using exponent Drucker-Prager criterion is much closer to the experimental load-displacement curve.

Wireless Sensor Networks for Organizational Network Analysis

Since their emergence, wireless sensor networks (WSNs) have become increasingly popular in the pervasive computing industry. This is particularly true within the past five years, which has seen sensor networks being adapted for wide variety of applications. Most of these applications are restricted to ambience monitoring and military use, however, very few commercial sensor applications have been explored till date. For WSNs to be truly ubiquitous, many more commercial sensor applications are yet to be investigated. As an effort to probe for such an application, we explore the potential of using WSNs in the field of Organizational Network Analysis (ONA). In this short paper, we propose a WSN based framework for analyzing organizational networks. We describe the role of WSNs in learning relationships among the people of an organization and investigate the research challenges involved in realizing the proposed framework.