哈尔滨工程大学学报2026，Vol.47Issue(7)：1427-1435,9.DOI:10.11990/jheu.202602013
基于改进知识蒸馏的干涸后传热预测方法
An improved knowledge-distillation-based method for predicting post-dryout heat transfer
摘要
Abstract
Objective To address the issues of limited experimental data and insufficient cross-domain generalization in modeling post-dryout heat transfer coefficients,an improved knowledge distillation-based continual learning method is de-veloped.Method Based on two independent post-dryout heat transfer experimental datasets,the proposed approach first trains a model using the dataset from Institution Ⅰ and employs a Conditional Tabular Generative Adversarial Network(CTGAN)to generate synthetic feature samples for data augmentation and automatic labeling.Then,on the basis of the Institution Ⅰ model,a model for Institution Ⅱ is trained through joint optimization of logit distillation and feature distilla-tion,enabling collaborative retention and adaptive transfer of old and new knowledge.Results The proposed model achieves a mean absolute percentage error(MAPE)of 1.92%and a coefficient of determination(R2)of 0.999 6 on an in-dependent test set.Compared with fine-tuning and feature extraction methods,it demonstrates superior prediction accuracy and generalization capability.Conclusion The proposed method effectively enhances the predictive performance of post-dryout heat transfer models while ensuring data security,providing a feasible technical route for intelligent heat transfer modeling under complex conditions in nuclear energy systems.关键词
干涸后传热/知识蒸馏/记忆回放/条件表格生成对抗神经网络/努塞尔数/灾难性遗忘/数据增强Key words
post-dryout heat transfer/knowledge distillation/memory replay/CTGAN/Nusselt number/cata-strophic forgetting/data augmentation分类
能源科技引用本文复制引用
袁双楠,夏建华,宋美琪,许巍,刘晓晶..基于改进知识蒸馏的干涸后传热预测方法[J].哈尔滨工程大学学报,2026,47(7):1427-1435,9.基金项目
国家自然科学基金项目(12427811). （12427811）
