Engineering topics

Mendez, Jennifer M.

Publications and source records attributed to Mendez, Jennifer M..

3 records

DOE OSTI2022

Projected network performance for next generation aerosol monitoring systems

Aerosol monitoring for radioactivity is a mature and proven technology. However, by improving key specifications of aerosol monitoring equipment, more samples per day can be collected and analyzed with the same minimum detectable concentrations as current systems. This work models hypothetical releases of 140 Ba and 131 I over a range of magnitudes corresponding to the inventory produced from the fission of about 100 g to 1 kiloton TNT-equivalent of 235 U. The releases occur over an entire year to incorporate the natural variability in atmospheric transport. Sampling equipment located at the 79 locations for radionuclide stations identified in the Comprehensive Nuclear-Test-Ban Treaty (CTBT) for the International Monitoring System are used to determine the detections of the individual releases. Alternative collection schemes in next generation equipment that collect 2, 3, or 4 samples per day, rather than the current 1 sample per day, would result in detections in many more samples at more stations with detections for a given release level. The authors posit that next generation equipment will result in increased network resilience to outages and improved source-location capability for lower yield source releases. The application of dual-detector and coincidence measurements to these systems would significantly boost sensitivity for some isotopes and would further enhance the monitoring capability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Report on Next-Gen AI for Proliferation Detection Workshop: Domain-Aware Methods

The emergence of artificial intelligence (AI) and machine learning (ML) in the modern world has impacted nearly every application imaginable. This includes nuclear proliferation detection, which offers the potential to improve existing capabilities as well as create new ones. Proliferation detection seeks to detect and characterize attempts by state and non-state actors to acquire nuclear weapons or associated technology, materials, or knowledge. Such a mission is vitally important for global stability and security but is notoriously difficult. By leveraging advances in AI, exciting opportunities exist to enhance the proliferation detection regime. The Data Science and AI portfolio within the National Nuclear Security Administration’s Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) seeks to leverage the capabilities of the Department of Energy’s (DOE’s) national laboratories and other partners to develop AI systems that can accomplish otherwise impossible tasks in support of proliferation detection. As part of its efforts, the portfolio has created a series of workshops on Next-Gen AI for Proliferation Detection to help define the requirements for suitable AI systems, share successful research and best practices, and foster connection and understanding between the relevant parties including researchers and end-users. Each workshop in the series focuses on a specific and critical aspect of AI to enable it to accomplish proliferation detection objectives. The first workshop focused on explainability techniques; the second workshop and the topic of this report, covers methods for incorporating domain awareness into AI. The Next-Gen AI for Proliferation Detection Workshop: Domain-Aware Methods took place virtually over two days in February 2021 and included four keynote presentations, 22 technical presentations, and a concluding panel. The presentations, discussions, and workshop findings are summarized in this report.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

High Throughput Argon-37 Field System

We report Pacific Northwest National Laboratory (PNNL) has developed a unique fieldable 37 Ar measurement system designed to measure 37 Ar activity concentrations from soil gas samples to detect above ground and underground nuclear explosions. The Argon-37 Field System is modular in design to accommodate both chemical processing and nuclear detection. The system can be packed into shipping crates and shipped to a location near where the sampling is taking place. The system can process six 2-m 3 whole-air samples in 24 hours and can measure the 37 Ar activity in each of the samples using six proportional counters. The proportional counters, designed and built at PNNL, are surrounded with both active and passive shielding to reduce background and can achieve a minimum detection concentration of 10 mBq/m 3 of 37 Ar in whole-air equivalent. The Argon-37 Field System has undergone extensive testing against rigorous requirements to assure the system meets the needs of the noble gas nuclear explosion monitoring community.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗