Digital Commons Network^™

Multiscale Topology Optimization With A Strong Dependence On Complementary Energy, Dustin Dean Bielecki

All Dissertations

A discrete approach introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. This framework relies on utilizing parameters such as density and nodal deflections to predict optimized topologies. A three-stage NN framework is employed for the discrete approach to reduce computational runtime while maintaining physics constraints.

A continuous representation that uses complementary energy (CE) methods to solve a representative element's homogenized properties consists of an embedded structure that is …

The Development Of A Finite Element Model For Ballistic Impact Predictions, Richard Allen Perkins

Theses and Dissertations

Concrete is a widely used product and is an important application throughout industry due to its inexpensive cost and wide range of applications. This work focuses on understanding the behavior of high strength concrete in high strain rate ballistic impact loading scenarios. A finite element analysis was created with the implementation of the Concrete Damage and Plasticity Model 2 (CDPM2) to represent the material behavior. The model’s parameters were calibrated to existing literature and the results were analyzed by a comparison of the impact velocity to residual velocity and a qualitative assessment of the impact crater. The model captured the …