Engineering topics

Grow, David Isaac

Publications and source records attributed to Grow, David Isaac.

3 records

DOE OSTI2023

Packaging Capacity Calculation: Pu Oxide Packaging Options with the 2 Quart SAVY Container

This calculation supports programmatic efforts to Dilute and Dispose Pu. Metal is to be oxidized at LANL, then transported to SRS for dilution at SRS. Ultimately, the diluted oxide will be disposed of at the Waste Isolation Pilot Plant (WIPP). Currently 3013 containers are used to package the oxide, which are placed in to 9975 Type B shipping containers. The Oxide Packaging Technology Maturation Plan determined that a transition to the used of 2-qt SAVY containers placed in 9977 would increase efficiency in shipping and handling and increase programmatic flexibility. The 2-quart SAVY container meets DOE M 441.1 and TA-55 Documented Safety Analysis requirements for handling and storage of Pu oxide at LANL.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Advancing Vision-based Feedback and Convolutional Neural Networks for Visual Outlier Detection

Machine learning has matured into a technology that has immediate applicability to the surveillance needs of nuclear material storage containers. These containers at LANL are the barrier preventing release of radioactive material to the workers, public, and environment during the storage period of the material. Annual surveillance activities can only provide coverage on a handful of containers. There is a significant need for surveillance tools to identify potential issues and precursors to containment failure that can be used during opportunistic inspections and, more generally, outside of annual surveillance activities. In this report we provide details on the advancement of our proposed embodiment that combines an automation system for taking pictures and a high-accuracy machine learning-driven object detection software. We further showcase the improvements on the software side with progress on extracting unique identification features and advances in detecting damage. The current state of the system captures subject matter expert training and a space-conscious design whose implementation is envisioned in the near future.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Improving Non-Destructive Detection Technology Through SAVY Feature Detection

Surveillance of special nuclear material (SNM) storage containers is required by the DOE to assess their integrity across the Complex. This work aims to improve the task of container inspections by leveraging automation through machine learning (ML) tools to reduce the human-intensive effort and expert-level knowledge needed to assess container status. A field-deployable, non-destructive technology was designed using off-the-shelf components to collect multiple images from different perspectives of containers in storage to detect both spatial features of interest and anomalies of concern. Nine ML models were generated using unique training datasets and parameters. Learned features include SAVY surface regions including the body side wall, collar, lid, filter, and printed/etched information. Average Precision (AP) is used to calculate detection performance when both viewing previously seen environments and previously unseen environments. The application of image transformations and resolution scaling while training greatly improved the detection performance in unseen environments, and significantly increasing the number of computation iterations improved detection performance on previously seen environments. Additional capabilities were developed including the novel detection of procedural non-compliance and the ability to localize anomalies relative to SAVY surface features.

97 MATHEMATICS AND COMPUTING↗