Manufacturing

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements. 

Generating energy models from scratch is a costly and time consuming task. A detailed energy model can have thousands of input parameters that are necessary for a complete simulation of a building. A solution to this problem is the AutoBEMGen framework.

In nuclear and industrial facilities, fine particles, including radioactive residues—can accumulate on the interior surfaces of ventilation ducts and equipment, posing serious safety and operational risks.

Efficient thermal management in polymers is essential for developing lightweight, high-strength materials with multifunctional capabilities.

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

US coastal and island communities have vulnerable energy infrastructure and high energy costs, which are exacerbated by climate change.

Wind turbine blades face a harsh environment in which erosion of the leading edge is a major factor for in-use maintenance. Current industrial practices to address this leading edge erosion are replacement of reinforcing materials upon significant damage infliction.

In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.