About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Integrated Computational Materials Engineering for Physics-Based Machine Learning Models
|
| Presentation Title |
Thermal response of stochastically modeled mesoscale metal foam |
| Author(s) |
Ryan Thomas Griffith, Matthew J. Beck |
| On-Site Speaker (Planned) |
Ryan Thomas Griffith |
| Abstract Scope |
Duocel aluminum foam, a porous material well documented on its lightweight structure and customizable properties, is useful in providing shielding for spacecraft. Previous studies have observed this customizability by characterizing the homogenized elastic response during the transition from micro-scale to macro-scale as well as the effect caused by MMOD impacts via cylindrical cavities. This work aims to observe how these same factors affect the bulk thermal conductivity and how the response compares to the trends seen in the elastic behavior. To achieve this, a FEM toolset known as KRaSTk was used to measure the thermal response when randomly generated representative volume elements (RVEs) of the foam were placed under a temperature gradient. Results for the variation in reduced density as well as the in-plane versus through-the-thickness cavity response mirrored the elastic response for the same material under the same constraints. |