title: Developing a set of reference-based correlations for prediction of fission gas fractions and hydrogen concentration in VVER-1000 using GEP trained by FRAPCON-3
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð°Ð±Ð¾ÑÐ° ÑÑÐ°Ð»Ð¾Ð½Ð½ÑÑ ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¹ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð¾Ð»ÐµÐ¹ Ð³Ð°Ð·Ð° Ð´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¸ ÐºÐ¾Ð½ÑÐµÐ½ÑÑÐ°ÑÐ¸Ð¸ Ð²Ð¾Ð´Ð¾ÑÐ¾Ð´Ð° Ð² ÐÐÐ­Ð -1000 Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ GEP, Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ FRAPCON-3
Creators: name: Ð¯Ð»ÑÑÐ½, ÐÐ»ÑÐµÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¯Ð´ÐµÑÐ½ÑÐµ ÑÐµÐ°ÐºÑÐ¾ÑÑ Ð²Ð¾Ð´Ð¾-Ð²Ð¾Ð´ÑÐ½ÑÐµ
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
fission gas fractions
Gene Expression Programming
machine learning
hydrogen concentration
fuel behavior analysis

description: Fission gas fractions and hydrogen concentrations are of significant importance regarding safety and efficiency due to their direct impact on the thermal conductivity of the fuel. Therefore, modeling the behavior of fission gas releases and being able to measure or predict their quantities is crucial. In line with this objective, this study aims to produce correlations that predict the fraction of fission gases and concentration of hydrogen. In this process, a sensitivity analysis was first performed using numerical solutions, and a dataset was created. Subsequently, correlations were generated using machine learning-based Gene Expression Programming (GEP). The results obtained from the correlations were compared with the numerical solution results in terms of Mean Square Error (MSE), Square Error (R^2), and Average Absolute Error (AAE), demonstrating a high degree of accuracy. In future studies, the application of artificial intelligence in related fields holds the potential to accelerate the process and eliminate human error.
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