极端恶劣环境下随机森林模型的岩心温度预测

Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES) Pub Date : 1900-01-01 DOI:10.1364/sensors.2022.sm1c.5

Jingyu Wu, Mohan Wang, Kehao Zhao, Rongtao Cao, D. Carpenter, G. Zheng, S. Rountree, Kevin P. Chen

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引用次数: 0

摘要

本文提出了基于光学测量的随机森林模型精确预测岩心内分布温度的方法。在极端恶劣的环境下，预测误差在温度波动的3.6%以内。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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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来源期刊

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

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