旋转条件下超临界压力碳氢燃料传热替代模型研究
Study on Heat Transfer Surrogate Model of Supercritical Pressure Hydrocarbon Fuel Under Rotating Condition
翁庆言 1姜培学 1胥蕊娜1
作者信息
- 1. 清华大学能源与动力工程系热科学与动力工程教育部重点实验室,北京 100084
- 折叠
摘要
为了预测旋转条件下超临界压力碳氢燃料的对流换热性能,基于LightGBM算法建立了Nu数预测的传热替代模型.针对U型通道中不同的实验段,分别建立了单一管段模型以及基于所有数据的统一预测模型,研究了模型在数据集上的预测性能,同时利用特征重要性进一步认识了换热过程的物理规律.结果表明,离心段、水平段、向心段的预测模型误差分别为2.47％、5.09％、4.48％,三个模型在所有数据上的平均误差为4.06％,而误差为3.32％的统一预测模型表现更优.说明,即便不同实验段换热规律存在差异,但数据中存在的相似性有助于提高模型的性能.
Abstract
In order to predict the convective heat transfer performance of supercritical pressure hydrocarbon fuel under rotating condition,a heat transfer surrogate model for Nu number predic-tion was established based on the LightGBM algorithm.For different experimental sections in the U-shaped channel,single-section models and a unified prediction model based on all data were es-tablished,and the prediction performance of the models on the data set was studied,while feature importance was used to further understand the physical laws of the heat transfer process.The re-sults show that the prediction model errors for the centrifugal section,the horizontal section,and the centripetal section are 2.47％,5.09％,and 4.48％,respectively,and the average error of the three models on all data is 4.06％,while the unified prediction model with error of 3.32％performs better.It shows that even if there are differences in the heat transfer laws of different experimental sections,the similarity in the data helps to improve the performance of the model.
关键词
机器学习/LightGBM/旋转条件/超临界压力碳氢燃料/湍流换热Key words
machine learning/LightGBM/rotating condition/supercritical pressure hydrocarbon fuel/turbulent heat transfer引用本文复制引用
出版年
2024
NETL
NSTL
万方数据