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A Physics-Based Machine Learning Study Of The Behavior Of Interstitial Helium In Single Crystal W–Mo Binary Alloys, Adib J. Samin
A Physics-Based Machine Learning Study Of The Behavior Of Interstitial Helium In Single Crystal W–Mo Binary Alloys, Adib J. Samin
Faculty Publications
In this work, the behavior of dilute interstitial helium in W–Mo binary alloys was explored through the application of a first principles-informed neural network (NN) in order to study the early stages of helium-induced damage and inform the design of next generation materials for fusion reactors. The neural network (NN) was trained using a database of 120 density functional theory (DFT) calculations on the alloy. The DFT database of computed solution energies showed a linear dependence on the composition of the first nearest neighbor metallic shell. This NN was then employed in a kinetic Monte Carlo simulation, which took into …
Kinetics Of High Pressure Argon-Helium Pulsed Gas Discharge, Daniel J. Emmons, David E. Weeks
Kinetics Of High Pressure Argon-Helium Pulsed Gas Discharge, Daniel J. Emmons, David E. Weeks
Faculty Publications
Simulations of a pulsed direct current discharge are performed for a 7% argon in helium mixture at a pressure of 270 Torr using both zero- and one-dimensional models. Kinetics of species relevant to the operation of an optically pumped rare-gas laser are analyzed throughout the pulse duration to identify key reaction pathways. Time dependent densities, electron temperatures, current densities, and reduced electric fields in the positive column are analyzed over a single 20 μs pulse, showing temporal agreement between the two models. Through the use of a robust reaction rate package, radiation trapping is determined to play a key role …