Image-Based Multiphysics Modeling of Random Metallic Meshes

This research focuses on the development and application of image-based multiphysics modeling techniques specifically for random metallic meshes. The study explores how to translate visual data, such as images of these meshes, into detailed computational models that can simulate their physical behavior under various conditions. Multiphysics modeling is crucial as it allows for the simultaneous analysis of multiple physical phenomena, such as thermal, mechanical, and electrical properties, which are often interconnected in metallic structures. The use of image-based methods means that the models are derived directly from real-world structures, increasing their accuracy and relevance for practical applications. Random metallic meshes, characterized by their non-uniform and often complex geometries, present unique challenges for traditional modeling approaches. This work aims to overcome these challenges by leveraging advanced imaging and computational techniques. The findings are expected to have implications for fields requiring precise understanding of material performance, including aerospace, automotive, and structural engineering, where the performance of mesh-like metallic components is critical.