Robust automatic tracking of individual triso-fueled pebbles through a novel application of x-ray imaging and machine learning

Patent metadata

Field	Value
Publication number	US-12580089-B2
Application number	US-202117799202-A
Country	US
Kind code	B2
Filing date	Mar 10, 2021
Priority date	Mar 10, 2020
Publication date	Mar 17, 2026
Grant date	Mar 17, 2026

Abstract

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.

First claim

Therefore, at least the following is claimed:

1 . A method for tagging a plurality of TRISO-fueled pebbles flowing throughout a pebble bed reactor core, the method comprising:
before the plurality of TRISO-fueled pebbles exit the pebble bed reactor core, for each of the plurality of TRISO-fueled pebbles:
acquiring a first ionizing radiation image of the TRISO-fueled pebble;
analyzing, using a machine learning algorithm, the acquired first ionizing radiation image of the TRISO-fueled pebble to identify a unique pattern of TRISO-fuel particle distributions within a solid graphite matrix of the TRISO-fueled pebble that is visible in the acquired first ionizing radiation image of the TRISO-fueled pebble;
deriving a TRISO-particle distribution fingerprint for the TRISO-fueled pebble that corresponds to the unique pattern of TRISO-fuel particle distributions within the solid graphite matrix of the TRISO-fueled pebble;
assigning an individual identifier to the TRISO-fueled pebble that corresponds to the TRISO-particle distribution fingerprint; and
storing the TRISO-particle distribution fingerprint and the individual identifier for the TRISO-fueled pebble in an image database;
acquiring a second ionizing radiation image of one of the plurality of TRISO-fueled pebbles exiting the pebble bed reactor core; analyzing the acquired second ionizing radiation image of the exiting TRISO-fueled pebble to identify, using the machine learning algorithm, a unique pattern of TRISO-fueled particle distributions within the solid graphite matrix of the exiting TRISO-fueled pebble that is visible in the acquired second ionizing radiation image of the exiting TRISO-fueled pebble; deriving a TRISO-particle distribution fingerprint for the exiting TRISO-fueled pebble that corresponds to the unique pattern of TRISO-fuel particle distributions within the solid graphite matrix of the exiting TRISO-fueled pebble; querying the image database for the individual identifier associated with the TRISO-particle distribution fingerprint of the exiting TRISO-fueled pebble; obtaining the individual identifier associated with the TRISO-particle distribution fingerprint of the exiting TRISO-fueled pebble from the image database; obtaining a current measurement of a burnup value for the exiting TRISO-fueled pebble; obtaining a stored burnup value associated with the individual identifier associated with the TRISO-particle distribution fingerprint for the exiting TRISO-fueled pebble; and validating that the individual identifier was successfully found for the exiting TRISO-fueled pebble by comparing the stored burnup value with the current measurement of the burnup value.

2 . The method of claim 1 , wherein the acquired first ionizing radiation image of each of the plurality of TRISO-fueled pebbles comprises an X-ray image.

3 . The method of claim 1 , wherein the acquired first ionizing radiation image of each of the plurality of TRISO-fueled pebbles comprises a neutron tomography image.

4 . The method of claim 1 , further comprising:
after storing the TRISO-particle distribution fingerprint and the individual identifier for each of the plurality of TRISO-fueled pebbles in the image database, introducing the plurality of TRISO-fueled pebbles into the pebble bed reactor core.

5 . The method of claim 1 , wherein querying the image database comprises:
searching the image database for the individual identifier associated with the TRISO-particle distribution fingerprint of the exiting TRISO-fueled pebble; and finding the individual identifier associated with the TRISO-particle distribution fingerprint of the exiting TRISO-fueled pebble.

6 . The method of claim 5 , further comprising tracking the flow of the plurality of TRISO-fueled pebbles throughout the pebble bed reactor core based on the individual identifiers for the plurality of TRISO-fueled pebbles.

7 . The method of claim 1 , wherein comparing the stored burnup value with the current measurement of the burnup value comprises determining whether the current measurement of the burnup value is within a set range of the stored burnup value.

8 . The method of claim 1 , further comprising:
obtaining a measurement of a burnup value for each of the plurality of TRISO-fueled pebbles before the plurality of TRISO-fueled pebbles exit the pebble bed reactor core; and storing the measurement of the burnup value for each of the plurality of TRISO-fueled pebbles, wherein the measurements of the burnup value are associated with the respective individual identifier for the respective TRISO-fueled pebble, wherein the obtained stored burnup value is one of the stored measurements of the burnup value.

9 . A system for tagging a plurality of TRISO-fueled pebbles flowing throughout a pebble bed reactor core, the system comprising:
an imaging system that is configured to capture ionizing radiation images of the plurality of TRISO-fueled pebbles; and a computing device having a memory and a processor, wherein, before the plurality of TRISO-fueled pebbles exit the pebble bed reactor core, for each of the plurality of TRISO-fueled pebbles, the processor is configured to:
analyze, using a machine learning algorithm, a first captured image of the TRISO-fueled pebble to identify a unique pattern of TRISO-fuel particle distributions within a solid graphite matrix of the TRISO-fueled pebble that is visible in the first captured image of the TRISO-fueled pebble, wherein the first captured image is a first ionizing radiation image obtained from the imaging system;
derive a TRISO-particle distribution fingerprint for the TRISO-fueled pebble that corresponds to the unique pattern of TRISO-fuel particle distributions within the solid graphite matrix of the TRISO-fueled pebble;
assign an individual identifier to the TRISO-fueled pebble that corresponds to the TRISO-particle distribution fingerprint; and
store the TRISO-particle distribution fingerprint and the individual identifier for the TRISO-fueled pebble in an image database;
wherein the processor is further configured to:
acquire a second image of one of the plurality of TRISO-fueled pebbles exiting the from a pebble bed reactor core;
analyze the acquired second image of the exiting TRISO-fueled pebble to identify, using the machine learning algorithm, a unique pattern of TRISO-fueled particle distributions within the solid graphite matrix of the exiting TRISO-fueled pebble that is visible in the acquired second image of the exiting TRISO-fueled pebble, wherein the acquired second image is a second ionizing radiation image obtained from the imaging system;
derive a TRISO-particle distribution fingerprint for the exiting TRISO-fueled pebble that corresponds to the unique pattern of TRISO-fuel particle distributions within the solid graphite matrix of the exiting TRISO-fueled pebble;
query the image database for the individual identifier associated with the TRISO-particle distribution fingerprint of the exiting TRISO-fueled pebble;
obtain the individual identifier associated with the TRISO-particle distribution fingerprint of the exiting TRISO-fueled pebble from the image database;
obtain a current measurement of a burnup value for the exiting TRISO-fueled pebble;
obtain a stored burnup value associated with the individual identifier associated with the TRISO-particle distribution fingerprint for the exiting TRISO-fueled pebble; and
validate that the individual identifier was successfully found for the exiting TRISO-fueled pebble by comparing the stored burnup value with the current measurement of the burnup value.

10 . The system of claim 9 , wherein the first captured image of each of the plurality of TRISO-fueled pebbles co

Assignees

• Univ Florida

Inventors

• Hartig Kyle
• Liu Hongcheng
• Kwapis Emily

Classifications

• G21C17/066 Primary

  Control of spherical elements · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V20/693

  Acquisition · CPC title

• G06V10/10

  Image acquisition (document image scanning and transmission H04N1/00; control of digital cameras H04N23/60) · CPC title

• G06F16/55

  Clustering; Classification · CPC title