{"title":"基于机器学习的CHF模型增强COBRA-TF预测性能的评价","authors":"Congshan Mao, Yue Jin","doi":"10.1016/j.pnucene.2025.106066","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigates the integration of machine learning (ML) techniques into the thermal-hydraulic code COBRA-TF as a foundational advancement for predictive modeling in complex multiphase energy systems. Accurate prediction of critical heat flux (CHF) is essential not only for nuclear reactor safety and performance, but also for a wide range of high-efficiency energy systems that rely on advanced heat transfer, including concentrated solar power, supercritical CO<sub>2</sub> cycles, fission and fusion energy systems, and next-generation thermal desalination and storage technologies. Four ML models were developed and assessed, including two pure data-driven artificial neural networks (ANN) with 3- and 4-layer architectures, as well as two physics-informed machine learning (PIML) variants that embed physical constraints derived from the classical Zuber correlation. The models were trained and validated using the NRC CHF dataset and subsequently integrated into the COBRA-TF at the source-code level, enabling efficient and real-time hybrid modeling. Testing across diverse operating conditions demonstrated that all ML-enhanced COBRA-TF models substantially outperformed the legacy tool in both accuracy and stability. The PIML-enhanced models, particularly the PIML-4L model, achieve the lowest mean absolute error (MAE) and mean absolute percentage error (MAPE) among all tested models. While PIML-3L yields the lowest RMSE, PIML-4L performs best in normalized metrics such as rRMSE, indicating better control of extreme deviations. Error-based analysis further reveals that at least 80 % of predictions from all models fall within a ±15 % relative error range. PIML-3L achieves the highest accuracy under strict error tolerances (≤5 %), while PIML-4L performs best in the moderate error range (5–15 %), demonstrating superior robustness. Overall, it was concluded that integrating ML—especially PIML—into COBRA-TF significantly improves its CHF prediction capabilities, with PIML-4L offering the most comprehensive performance gain.</div></div>","PeriodicalId":20617,"journal":{"name":"Progress in Nuclear Energy","volume":"191 ","pages":"Article 106066"},"PeriodicalIF":3.2000,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of COBRA-TF prediction performance enhanced by machine learning-based CHF models\",\"authors\":\"Congshan Mao, Yue Jin\",\"doi\":\"10.1016/j.pnucene.2025.106066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study investigates the integration of machine learning (ML) techniques into the thermal-hydraulic code COBRA-TF as a foundational advancement for predictive modeling in complex multiphase energy systems. Accurate prediction of critical heat flux (CHF) is essential not only for nuclear reactor safety and performance, but also for a wide range of high-efficiency energy systems that rely on advanced heat transfer, including concentrated solar power, supercritical CO<sub>2</sub> cycles, fission and fusion energy systems, and next-generation thermal desalination and storage technologies. Four ML models were developed and assessed, including two pure data-driven artificial neural networks (ANN) with 3- and 4-layer architectures, as well as two physics-informed machine learning (PIML) variants that embed physical constraints derived from the classical Zuber correlation. The models were trained and validated using the NRC CHF dataset and subsequently integrated into the COBRA-TF at the source-code level, enabling efficient and real-time hybrid modeling. Testing across diverse operating conditions demonstrated that all ML-enhanced COBRA-TF models substantially outperformed the legacy tool in both accuracy and stability. The PIML-enhanced models, particularly the PIML-4L model, achieve the lowest mean absolute error (MAE) and mean absolute percentage error (MAPE) among all tested models. While PIML-3L yields the lowest RMSE, PIML-4L performs best in normalized metrics such as rRMSE, indicating better control of extreme deviations. Error-based analysis further reveals that at least 80 % of predictions from all models fall within a ±15 % relative error range. PIML-3L achieves the highest accuracy under strict error tolerances (≤5 %), while PIML-4L performs best in the moderate error range (5–15 %), demonstrating superior robustness. Overall, it was concluded that integrating ML—especially PIML—into COBRA-TF significantly improves its CHF prediction capabilities, with PIML-4L offering the most comprehensive performance gain.</div></div>\",\"PeriodicalId\":20617,\"journal\":{\"name\":\"Progress in Nuclear Energy\",\"volume\":\"191 \",\"pages\":\"Article 106066\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Progress in Nuclear Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0149197025004640\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NUCLEAR SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Progress in Nuclear Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0149197025004640","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Evaluation of COBRA-TF prediction performance enhanced by machine learning-based CHF models
This study investigates the integration of machine learning (ML) techniques into the thermal-hydraulic code COBRA-TF as a foundational advancement for predictive modeling in complex multiphase energy systems. Accurate prediction of critical heat flux (CHF) is essential not only for nuclear reactor safety and performance, but also for a wide range of high-efficiency energy systems that rely on advanced heat transfer, including concentrated solar power, supercritical CO2 cycles, fission and fusion energy systems, and next-generation thermal desalination and storage technologies. Four ML models were developed and assessed, including two pure data-driven artificial neural networks (ANN) with 3- and 4-layer architectures, as well as two physics-informed machine learning (PIML) variants that embed physical constraints derived from the classical Zuber correlation. The models were trained and validated using the NRC CHF dataset and subsequently integrated into the COBRA-TF at the source-code level, enabling efficient and real-time hybrid modeling. Testing across diverse operating conditions demonstrated that all ML-enhanced COBRA-TF models substantially outperformed the legacy tool in both accuracy and stability. The PIML-enhanced models, particularly the PIML-4L model, achieve the lowest mean absolute error (MAE) and mean absolute percentage error (MAPE) among all tested models. While PIML-3L yields the lowest RMSE, PIML-4L performs best in normalized metrics such as rRMSE, indicating better control of extreme deviations. Error-based analysis further reveals that at least 80 % of predictions from all models fall within a ±15 % relative error range. PIML-3L achieves the highest accuracy under strict error tolerances (≤5 %), while PIML-4L performs best in the moderate error range (5–15 %), demonstrating superior robustness. Overall, it was concluded that integrating ML—especially PIML—into COBRA-TF significantly improves its CHF prediction capabilities, with PIML-4L offering the most comprehensive performance gain.
期刊介绍:
Progress in Nuclear Energy is an international review journal covering all aspects of nuclear science and engineering. In keeping with the maturity of nuclear power, articles on safety, siting and environmental problems are encouraged, as are those associated with economics and fuel management. However, basic physics and engineering will remain an important aspect of the editorial policy. Articles published are either of a review nature or present new material in more depth. They are aimed at researchers and technically-oriented managers working in the nuclear energy field.
Please note the following:
1) PNE seeks high quality research papers which are medium to long in length. Short research papers should be submitted to the journal Annals in Nuclear Energy.
2) PNE reserves the right to reject papers which are based solely on routine application of computer codes used to produce reactor designs or explain existing reactor phenomena. Such papers, although worthy, are best left as laboratory reports whereas Progress in Nuclear Energy seeks papers of originality, which are archival in nature, in the fields of mathematical and experimental nuclear technology, including fission, fusion (blanket physics, radiation damage), safety, materials aspects, economics, etc.
3) Review papers, which may occasionally be invited, are particularly sought by the journal in these fields.