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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.
The technologies provides additives for polymer synthesis.
The increasing demand for high-purity lanthanides, essential for advanced technologies such as electronics, renewable energy, and medical applications, presents a significant challenge due to their similar chemical properties.
Distortion generated during additive manufacturing of metallic components affect the build as well as the baseplate geometries. These distortions are significant enough to disqualify components for functional purposes.
A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.
The hybrid powder-encapsulated solvent over comes carbon capture challenges by providing a solution for easy handling of a non-toxic solid that is non-volatile and stable upon alternative energy regeneration methods.
For additive manufacturing of large-scale parts, significant part distortion can result from the build-up of residual stresses during deposition and cooling. This can result in part scarp if the final part geometry is not contained in the additively manufactured preform.