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Technology Portfolios

Machine Learning for Monitoring microfluidic microcapsules
Machine Learning for Automated Monitoring and Control of Microfluidic Systems
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…
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Machine Learning Informed Control Systems for Extrusion Printing Processes to provide Optimal Control for Additive Manufacturing
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…
Marine helmet
Traumatic Brain Injury Protection: Blast Pressure Sensors in Helmets

LLNL's high fidelity hydrocode is capable of predicting blast loads and directly coupling those loads to structures to predict a mechanical response. By combining this code and our expertise in modeling blast-structure interaction and damage, along with our access to experimental data and testing facilities, we can contribute to the design of protective equipment that can better mitigate the…

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A Multi-Channel Pyrometer allowing non-contact temperature measurements down to 800K on the microsecond scale

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.

Stock image UAV drone monitoring gas near pipeline valves
Rugged, power-efficient and compact system based on TDLAS for remote sensing of multiple gases in challenging environments

LLNL researchers have developed a TDLAS-based, standalone, real-time gas analyzer in a small form-factor for continuous or single-point monitoring.  The system can analyze multiple gases with ultra-high sensitivity (ppm detection levels) in harsh conditions when utilizing wavelength-modulation spectroscopy (WMS).