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In-core Temperature Forecasting by Random Forest Modeling in Extreme Harsh Environment

Jingyu Wu,Mohan Wang,Kehao Zhao,Rongtao Cao,David Carpenter,Guiqiu Zheng,Steven Derek Rountree,Kevin P. Chen

Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES)（2022）

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Abstract
This paper proposed accurate in-core distributed temperature predictions by random forest modeling based on optical measurements. The prediction error is within 3.6% of the temperature swing in the extremely harsh environment.
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Key words
random forest modeling,extreme harsh environment,forecasting,temperature,in-core
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