Verification of Triso Fuel Burnup Using Machine Learning Algorithms

Technical Report · Sun Aug 01 04:00:00 EDT 2021

DOI:https://doi.org/10.2172/1813329· OSTI ID:1813329

Dim, Odera ^[1]; Soto, Carlos ^[1]; Cui, Yonggang ^[1]; Cheng, Lap-Yan ^[1]; Gemmill, Maia ^[1]; Grice, Thomas ^[1]; Rivers, Joseph ^[1]; Stern, Warren ^[1]; Todosow, Michael ^[1]

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1. Brookhaven National Lab. (BNL), Upton, NY (United States)

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as ¹³⁴Cs, ¹³⁷Cs, ¹⁵⁴Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development; USDOE Office of Nuclear Energy (NE)
• DOE Contract Number: SC0012704
• OSTI ID: 1813329
• Report Number(s): BNL-221947-2021-INRE
• Country of Publication: United States
• Language: English

Related Subjects

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS
97 MATHEMATICS AND COMPUTING
Triso fuel
algorithms
burnup
machine learning
pebble bed reactors