Training realistically to respond to the threat of radiological terrorism is a real problem. Using actual radiological materials to train federal, state, and local agencies who detect and respond to these threats is extremely expensive, adds risk, and can’t replicate many of the scenarios of concern. LLNL’s Radiation Field Training Simulator (RaFTS) is a programmable device that injects realistic radiation source signals into suitably adapted operational radiation detection and identification devices (spectrometers). RaFTS enables highly realistic scenarios to simulate truly hazardous situations but without the need, expense or risks of using actual radiological material. In 2020, RaFTS was licensed by Argon Electronics Ltd (UK) to add significant capability to their line of CBRN hazard simulators.
The National Security mission at the lab supports advanced technology needs of the nation. We support some of the advanced needs for the Departments of Defense, Homeland Security, Justice, State, EPA as well as international partners and state governments. LLNL excels in programs for High Explosives, Sensors, Space missions, Materials, Intelligence, Forensic Sciences, High Performance Simulation and Computing. The LLNL facilities have some of the largest research labs in the nation spread over several thousand acres.
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LLNL, Penn State, Columbia University, Tufts University, University of Kentucky, Purdue University and industry partner Western Rare Earths will use microbial and biomolecular engineering to develop a scalable bio-based separation and purification strategy for rare-earth elements
A trio of LLNL scientists have been inducted into the laboratory's Entrepreneur's Hall of Fame. Each developed technologies during or after their Lab careers that created major economic impacts or spawned new companies.
A set of images generated by multiple passes over the same area can be coherently integrated by this technology developed by LLNL researchers. The primary difficulty with coherently combining different passes is registering the images obtained from each pass, particularly if a pass only partially covers a given area.
LLNL researchers have developed a lightweight drone-based GPR array that when flown over a surface with laid and/or buried objects could image the field of view and be able to detect targets and discriminate them from clutter. The imaging method employs a modified multi-static architecture to provide the highest signal to noise with the lowest system weight, making it ideal for airborne or…
This technology uses three different frequency bands to create intensity maps of returned signals. Signals have traditionally been displayed as raw return data. The intensity of the return is represented by level of brightness. Assignment of a scalar value for intensity is used to determine the brightness of the image. In this technology, each frequency is given a designated primary color…
LLNL has developed a wide band (WB) ground penetrating radar (GPR) technology to detect and image buried objects under a moving vehicle. Efficient and high performance processing algorithms reconstruct images of buried or hidden objects in two or three dimensions under a scanning array. The technology includes a mobile high-performance computing system allowing GPR array sensor data to be…