Automated Analysis, Reporting, and Archiving for Robotic Nondestructive Assay of Holdup Deposits

TL;DR

This paper presents PCAMS, an automated robotic system for measuring and analyzing uranium deposits inside pipes, significantly reducing manual effort and analysis time during facility decommissioning.

Contribution

Introduction of PCAMS, a robotic system with automated data processing and reporting for nondestructive assay of pipe holdup deposits in nuclear facilities.

Findings

Automated data collection and analysis reduce measurement time from months to minutes.

System provides auto-generated reports with integrated spectra, images, and geometric models.

User feedback indicates improved efficiency and accuracy in decommissioning processes.

Abstract

To decommission deactivated gaseous diffusion enrichment facilities, miles of contaminated pipe must be measured. The current method requires thousands of manual measurements, repeated manual data transcription, and months of manual analysis. The Pipe Crawling Activity Measurement System (PCAMS), developed by Carnegie Mellon University and in commissioning for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, uses a robot to measure Uranium-235 from inside pipes and automatically log the data. Radiation measurements, as well as imagery, geometric modeling, and precise measurement positioning data are digitally transferred to the PCAMS server. On the server, data can be automatically processed in minutes and summarized for analyst review. Measurement reports are auto-generated with the push of a button. A database specially-configured to hold heterogeneous data such as spectra, images, and robot trajectories serves as archive. This paper outlines the features and design of the PCAMS Post-Processing Software, currently in commissioning for use at the Portsmouth Gaseous Diffusion Enrichment Facility. The analysis process, the analyst interface to the system, and the content of auto-generated reports are each described. Example pipe-interior geometric surface models, illustration of how key report features apply in operational runs, and user feedback are discussed.