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RECENT PROJECTS
Directional Diffusion Model Schmatic
NSF-CMMI-MPM 0720399—Phenomenological-Based Constitutive Model and Simulation of Fiber Interaction for Short Fiber Composite Processing
The objective of this research is to develop a phenomenological-based constitutive model for fiber collisions in a general fluid and use this new approach to predict the fiber orientation state for industrial-processes.

AFRL TO #3—Modeling of High Current Loading of Carbon Nanomembranes: Simulation of the Electrical and Thermal Behavior CNT Thin Film Tube Potential Layout

The research seeks provide insight into the behavior and properties of carbon nanomembranes, a macro-scale network of neat single-walled carbon nanotubes (CNT), by developing computational simulations that stochastically incorporate the network nanostructure to understand the membrane’s response to electrical loadings.
Schematic of Ultrasonic Detector L3 Communications, Phase 1—3D Hi Resolution NDT for Composites
Baylor University and Birkeland Current seek to develop a proof of concept ultrasonic detector simulation in order to establish the necessary requirements to develop a three dimensional non-destructive testing (NDT) sensor system capable of determining inter-lamina manufacturing, installation, and use effects for comparison to as-designed and as-analyzed carbon fiber composites structures.

AFRL TO#14—Modeling and Simulation of SWNT Buckypaper Electrical Conductivity CNT Thin Film Schmatic
This research seeks to develop computational simulations to model the electrical conductivity from steady-state current loadings of macro-scale networks of neat carbon nanotubes (CNT) (also referred to as buckypapers), and to capture, in a concise numerical model, the dependency bulk conductivity has on stochastic nanoscale effects.
NNET Flow Chart Neural Network Closures for Modeling the Orientation of Short-Fiber Composites
This research studies a new class of fitted orientation tensor closures where components of the fourth-order tensor are computed from corresponding second-order tensors using artificial neural networks (ANN). ANNs offer a unique advantage due to their computational efficiencies while representing complex relationships between inputs and outputs where representative mathematical models do not exist.


Baylor University School of Engineering and Computer Science Department of Mechanical Engineering Sic'Em - Scientific Innovations in Composites and Engineering Materials