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![Plasma wind](/sites/default/files/styles/scale_exact_400x400_/public/2022-08/Plasma%20wind%20.png?itok=RB5iLhMv)
CRETIN is a 1D, 2D, and 3D non-local thermodynamic equilibrium (NLTE) atomic kinetics/radiation transport code which follows the time evolution of atomic populations and photon distributions as radiation interacts with a plasma consisting of an arbitrary mix of elements. It can provide detailed spectra for comparing with experimental diagnostics.
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![graphic_of_simulation.png graphic_of_simulation](/sites/default/files/styles/scale_exact_400x400_/public/2019-08/graphic_of_simulation.png?itok=eyhMWp8B)
Livermore researchers have developed a method for implementing closed-loop control in extrusion printing processes by means of novel sensing, machine learning, and optimal control algorithms for the optimization of printing parameters and controllability. The system includes a suite of sensors, including cameras, voltage and current meters, scales, etc., that provide in-situ process monitoring…
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![multichannel_pyrometer.jpg multichannel_pyrometer](/sites/default/files/styles/scale_exact_400x400_/public/2019-08/multichannel_pyrometer.jpg?itok=x0sCe_BN)
LLNL researchers have designed and tested performance characteristics for a multichannel pyrometer that works in the NIR from 1200 to 2000 nm. A single datapoint without averaging can be acquired in 14 microseconds (sampling rate of 70,000/s). In conjunction with a diamond anvil cell, the system still works down to about 830K.
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![Nanoporus gold](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/nanoporus%20gold%20875x500.jpg?itok=A0gFmVPT)
By combining 3D printing and dealloying., researchers at LLNL have developed a method for fabricating metal foams with engineered hierarchical architectures consisting of pores at least 3 distinct length scales. LLNL’s method uses direct ink writing (DIW), a 3D printing technique for additive manufacturing to fabricate hierarchical nanoporous metal foams with deterministically controlled 3D…
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![Machine Learning for Monitoring microfluidic microcapsules](/sites/default/files/styles/scale_exact_400x400_/public/2022-06/Machine%20Learning%20for%20Monitoring%20microfluidic%20microcapsules%20875_0.jpg?itok=cLdsZh03)
LLNL researchers have developed a system that relies on machine learning to monitor microfluidic devices. The system includes (at least) a microfluidic device, sensor(s), and a local network computer. The system could also include a camera that takes real-time images of channel(s) within an operating microfluidic device. A subset of these images can be used to train/teach a machine learning…