TRISO Pebble X-Ray Fingerprinting for Individual Reactor Tracking
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Solution Overview
Problem
Current methods fail to effectively track and identify individual TRISO-fueled pebbles in pebble bed reactors due to random packing and heterogeneity, which complicates pebble flow management and burnup determination.
Innovation Solution
Utilize machine learning algorithms to analyze ionizing radiation images of TRISO-fueled pebbles, deriving unique particle distribution fingerprints and assigning individual identifiers, stored in a database, to track pebble identity through the reactor core without altering manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tracking methods are used for pebbles in pebble bed reactors, then the system is simple to operate, but the ability to identify and track individual pebbles is insufficient due to random packing and heterogeneity
Solution Approach 1:
The patent uses X-ray imaging to detect variations in particle distribution patterns within pebbles, which act as unique visual fingerprints. Each pebble's internal structure creates a distinct radiographic pattern that can be identified and tracked, analogous to using color or visual markers for identification.
Solution Approach 2:
The system creates digital copies of the unique particle distribution patterns from X-ray images and stores them in a database. These digital fingerprints can be compared and matched to track individual pebbles through the reactor system without physically tagging or modifying the pebbles themselves.
2Productivity
If manual tracking methods are used for pebbles, then the equipment complexity is low, but the productivity and speed of pebble flow management is insufficient
Solution Approach 1:
The patent replaces manual mechanical tracking methods with an automated X-ray imaging and machine learning analysis system. The imaging system captures radiographic images, and machine learning algorithms automatically analyze particle distribution patterns to identify and track pebbles, eliminating the need for manual inspection and significantly increasing processing speed.
Solution Approach 2:
The system enables self-service tracking by using the pebbles' own internal particle distribution patterns as identification markers. No external tags, labels, or modifications are needed on the pebbles themselves - their natural heterogeneity provides the tracking information, and the system automatically extracts and uses this information for identification.
3Loss of information
If pebbles are randomly packed in the reactor core, then the manufacturing process is simple, but the ability to determine burnup and track pebble flow is compromised
Solution Approach 1:
The system performs preliminary X-ray imaging and fingerprinting of pebbles before they are randomly packed into the reactor core. The unique particle distribution patterns are captured and stored in advance, creating a database of identification markers that can be used later to track the pebbles even after random packing obscures their positions and identities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid, non-contact identification and tracking of pebbles, improving safety, fuel efficiency, and cost-effectiveness by validating burnup measurements and maintaining pebble integrity under extreme reactor conditions.
Implementation Method 1
acquiring an ionizing radiation image of a TRISO-fueled pebble
Implementation Method 2
the ionizing radiation image comprises a neutron tomography image
Data Source
AI summary
The present disclosure presents systems and methods of tagging TRISO-fueled pebbles. One such method comprises acquiring an ionizing radiation image of a TRISO-fueled pebble; analyzing, using a machine learning algorithm, the acquired image of the TRISO-fueled pebble to identify a unique pattern of particle distributions that is visible in the acquired image of the TRISO-fueled pebble; deriving a TRISO-particle distribution fingerprint for the TRISO-fueled pebble that corresponds to the unique pattern of particle distributions; assigning an individual identifier to the TRISO-fueled pebble that corresponds to a TRISO-particle distribution fingerprint; and storing the TRISO-particle distribution fingerprint and the individual identifier for the TRISO-fueled pebble in an image database, wherein the image database stores a plurality of TRISO-particle distribution fingerprints and individual identifiers for a plurality of TRISO-fueled pebbles. Other systems and methods are also presented.