哈尔滨工程大学学报2026，Vol.47Issue(7)：1445-1456,12.DOI:10.11990/jheu.202602005
小样本下基于Transformer与树模型的临界热流密度预测及误差行为分析
Critical heat flux small sample prediction and error behavior analysis based on transformer and tree models
摘要
Abstract
To address the uneven distribution,inconsistent physical dimensions,and small-sample sensitivity of critical heat flux(CHF)experimental data under actual reactor operating conditions,this study establishes a research workflow integrating unified preprocessing,Transformer-based prediction,tree-model benchmarking,and error mechanism analysis.Based on a curated comprehensive CHF database,tube inner diameter,heated length,system pressure,mass flux,and outlet quality are selected as input features.Pressure units are unified,and samples with out-of-range outlet quality are marked for tracking.The results show that when the sample size is N<150,tree-based models,including Categorical Boosting,eXtreme Gradient Boosting,and Random Forest,exhibit more stable perfor-mance in terms of root mean square error and mean absolute error.When N increases to approximately 200-300,the Transformer model rapidly approaches and partially surpasses the tree-based models,demonstrating its potential for capturing high-dimensional nonlinear relationships.Residual analysis indicates that pressure and outlet quality are the dominant factors affecting prediction errors,while high-pressure and near-dryout conditions remain challenging for data-driven CHF prediction.By explicitly characterizing the small-sample performance curves,this study provides an alternative pathway for advancing data-driven CHF modeling toward engineering applications.关键词
小样本学习/临界热流密度/Transformer/树模型/样本效率/残差分析/物理一致性Key words
few-shot learning/critical heat flux/transformer/tree models/sample efficiency/residual analysis/physical consistency分类
能源科技引用本文复制引用
王一家,马文慧,徐乐,章静,王明军,巫英伟,田文喜,苏光辉,秋穗正..小样本下基于Transformer与树模型的临界热流密度预测及误差行为分析[J].哈尔滨工程大学学报,2026,47(7):1445-1456,12.基金项目
国家自然科学基金面上项目(12175173). （12175173）
