title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð½Ð°Ð±Ð¾ÑÐ° ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¹ Ð´Ð»Ñ Ð¼Ð°ÐºÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð¹ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ ÑÐ¾Ð¿Ð»Ð¸Ð²Ð° Ð² ÑÐµÐ°ÐºÑÐ¾ÑÐµ ÐÐÐ­Ð -1000 Ð¿ÑÐ¸ ÑÑÐ°ÑÐ¸Ð¾Ð½Ð°ÑÐ½ÑÑ ÑÐµÐ¶Ð¸Ð¼Ð°Ñ ÑÐ°Ð±Ð¾ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐºÑÐ¿ÑÐµÑÑÐ¸Ð¸ Ð³ÐµÐ½Ð¾Ð² Ð¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ COBRA
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: Developing a set of steady-state correlations for maximum fuel temperature in VVER 1000 reactor using gene expression programming and COBRA
Creators: name: ÐÐ°Ð»Ð°Ð½ÐºÐµ, ÐÐ°ÑÐ°ÑÐ°
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: 2023
Subjects: Ð¯Ð´ÐµÑÐ½ÑÐµ ÑÐµÐ°ÐºÑÐ¾ÑÑ Ð²Ð¾Ð´Ð¾-Ð²Ð¾Ð´ÑÐ½ÑÐµ
ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð¼Ð°ÐºÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð°Ñ ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÐ° ÑÐ¾Ð¿Ð»Ð¸Ð²Ð°
Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÐ°Ð½Ð¾Ð²Ð¸Ð²ÑÐµÐ³Ð¾ÑÑ ÑÐµÐ¶Ð¸Ð¼Ð°
ÐºÐ¾Ð´Ð¾Ð²Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð°
ÑÐµÐ¿Ð»Ð¾Ð³Ð¸Ð´ÑÐ°Ð²Ð»Ð¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð¿ÐµÑÐµÑ Ð¾Ð´Ð½ÑÐµ Ð¿ÑÐ¾ÑÐµÑÑÑ
ÑÐ¾Ð¿Ð»Ð¸Ð²Ð½ÑÐµ ÑÐ±Ð¾ÑÐºÐ¸
Ð°ÐºÑÐ¸Ð²Ð½ÑÐµ Ð·Ð¾Ð½Ñ Ð»ÐµÐ³ÐºÐ¾Ð²Ð¾Ð´Ð½ÑÑ ÑÐµÐ°ÐºÑÐ¾ÑÐ¾Ð²
ÑÐ±Ð¾ÑÐºÐ° Ð¸ Ð°ÐºÑÐ¸Ð²Ð½ÑÐµ Ð·Ð¾Ð½Ñ Ð»ÐµÐ³ÐºÐ¾Ð²Ð¾Ð´Ð½ÑÑ ÑÐµÐ°ÐºÑÐ¾ÑÐ¾Ð²
Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐºÑÐ¿ÑÐµÑÑÐ¸Ð¸ Ð³ÐµÐ½Ð¾Ð²
maximum fuel temperature
steady-state modeling
code system
thermal-hydraulic analysis
transients
fuel assemblies
light-water reactor cores
light-water reactor assembly and cores
gene expression programming

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description: Fuel maximum temperature is a crucial factor regarding thermal behavior assessment as well as to ensure fuel integrity. This paper aims to show that a correlation to obtain the maximum temperature using artificial intelligence and steady state simulation can be achieved. In this paper the steady state simulation of VVER-1000 carried out using Code System for Thermal-Hydraulic Transient Analysis of Light Water Reactor Fuel Assemblies and Cores (COBRA-EN) was achieved to ascertain the relation between input parameters and their significance of effect on the centerline temperature of the fuel rod. This was fully investigated by performing sensitivity analysis by deviating the input values and comparison with the reference values. Based on the results of the sensitivity analysis it was evident that mass flux, inlet temperature, power, fuel &amp; clad thermal conductivity as well as gap heat transfer coefficient had the most significant impact on the centerline temperature. By creating a dataset of 59 samples based on Wilkâs method that states that with confidence of 95%; it is possible to cover 95% of all uncertainties in the correlation and therefore this was the dataset used for gene expression programming to ascertain if the centerline temperature could be predicted using machine learning thereby laying a foundation for the application of artificial intelligence in nuclear engineering. A correlation was achieved, showing similar maximum temperatures by gene expression programming and COBRA with the mean square error of 2.03163e-004 and the coefficient of determination at 0.92159936, consequently showing the high accuracy of the correlations obtained.