1 - 10 of 34 Results
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.
We have developed a novel extrusion-based 3D printing technique that can achieve a resolution of 0.51 mm layer thickness, and catalyst loading of 44% and 90.5% before and after drying, respectively.
High-gradient magnetic filtration (HGMF) is a non-destructive separation technique that captures magnetic constituents from a matrix containing other non-magnetic species. One characteristic that actinide metals share across much of the group is that they are magnetic.
A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.
The invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.
A pressure burst feature has been designed and demonstrated for relieving potentially hazardous excess pressure within irradiation capsules used in the ORNL High Flux Isotope Reactor (HFIR).
The lattice collimator places a grid of shielding material in front of a radiation detector to reduce the effect of background from surrounding materials and to enhance the RPM sensitivity to point sources rather than distributed sources that are commonly associated with Natur
V-Cr-Ti alloys have been proposed as candidate structural materials in fusion reactor blanket concepts with operation temperatures greater than that for reduced activation ferritic martensitic steels (RAFMs).
This invention is directed to a machine leaning methodology to quantify the association of a set of input variables to a set of output variables, specifically for the one-to-many scenarios in which the output exhibits a range of variations under the same replicated input condi
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.