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人工智能在核燃料及材料领域的应用现状和发展趋势
作者:
作者单位:

中国核动力研究设计院先进核能技术全国重点实验室

基金项目:

国家自然科学基金重点项目资助(项目号U2067221),国家自然科学基金项目资助(项目号12205285),四川省杰青项目资助(24NSFJQ0248)


The Current Status and Development Trends of Artificial Intelligence in the Field of Nuclear Fuel and Materials
Author:
Affiliation:

State Key Laboratory of Advanced Nuclear Energy Technology, Nuclear Power Institute of China

Fund Project:

The National Natural Science Foundation of China (Key Program, U2067221), The National Natural Science Foundation of China(12205285), Excellent Youth Foundation of Sichuan Scientific Committee

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    摘要:

    随着人工智能(Artificial intelligence, AI)的飞速发展,其在核燃料及材料领域的应用正逐渐成为推动核能科技进步的新动力。本文全面回顾了AI在核燃料及材料领域的研究现状,并对未来的发展趋势进行了深入分析。首先介绍了应用于科学研究的AI方法,分别从网络架构和学习范式两个方面展开论述。其次系统总结了AI在核燃料材料级和整体级的性能预测、材料和结构的设计优化和燃料生产运行过程的视觉任务三个方面的应用现状。随后展望了AI与核燃料及材料结合的未来发展趋势,在算法层面,讨论了提升机器学习模型的可解释性、量化不确定性的方法,以及主动学习在减少数据需求方面的重要性;在应用层面,讨论了多尺度多物理场仿真加速、拓扑优化与生成式设计、核材料性质通用预训练模型,以及自动化实验室等关键技术。最后为进一步促进AI在核燃料及材料领域的应用提出了几点建议。

    Abstract:

    With the rapid development of Artificial Intelligence (AI), its application in the field of nuclear fuel and materials is gradually becoming a new driving force for the advancement of nuclear energy technology. This article comprehensively reviews the current state of AI research in the field of nuclear fuel and materials and conducts an in-depth analysis of future development trends. It first introduces AI methods applied to scientific research, discussing from two aspects: network architecture and learning paradigms. Secondly, it systematically summarizes the current state of AI applications in performance prediction at the material and structure levels of nuclear fuel materials, design optimization of materials and structures, and computer vision in fuel production and operation. It then looks forward to the future development trends of the combination of AI with nuclear fuel and materials. At the algorithm level, it discusses methods to enhance the interpretability of machine learning models, quantify uncertainty, and the importance of active learning in reducing data requirements. At the application level, it discusses key technologies such as acceleration of multi-scale multi-physics field simulations, topological optimization and generative design, universal pre-trained models for nuclear material properties, and automated laboratories. Finally, several suggestions are proposed to further promote the application of AI in the field of nuclear fuel and materials.

    参考文献
    [1] ZHANG X, WANG L, HELWIG J, et al. arXiv preprint arXiv:230708423[J], 2023.
    [2] DENG Z, WANG J, LIU H, et al. Physics of Fluids[J], 2023, 35(7).
    [3] BI K, XIE L, ZHANG H, et al. Nature[J], 2023.
    [4] WU T, WANG Q, ZHANG Y, et al. proceedings of the Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining[C], F, 2022.
    [5] JUMPER J, EVANS R, PRITZEL A, et al. Nature[J], 2021, 596(7873): 583-9.
    [6] ZENG J, ZHANG D, LU D, et al. The Journal of Chemical Physics[J], 2023, 159(5).
    [7] WANG H, FU T, DU Y, et al. Nature[J], 2023, 620(7972): 47-60.
    [8] SUMAN S. Journal of Cleaner Production[J], 2021, 278.
    [9] HUANG Q, PENG S, DENG J, et al. Heliyon[J], 2023, 9(3): e13883.
    [10] MORGAN D, PILANIA G, COUET A, et al. Current Opinion in Solid State and Materials Science[J], 2022, 26(2).
    [11] RUSSELL S J, NORVIG P. Artificial intelligence: a modern approach[M]. Pearson, 2016.
    [12] ANDERSEN B, KROPACZEK D J. Progress in Nuclear Energy[J], 2023, 155.
    [13] STEWART R H, PALMER T S, DUPONT B. Progress in Nuclear Energy[J], 2021, 138.
    [14] ERICKSON N, MUELLER J, SHIRKOV A, et al. arXiv preprint arXiv:200306505[J], 2020.
    [15] ZHANG A, LIPTON Z C, LI M, et al. Dive into deep learning[M]. Cambridge University Press, 2023.
    [16] LU L, JIN P, PANG G, et al. Nature Machine Intelligence[J], 2021, 3(3): 218-29.
    [17] LI Z, KOVACHKI N B, AZIZZADENESHELI K, et al. proceedings of the International Conference on Learning Representations[C], F, 2021.
    [18] XIONG W, HUANG X, ZHANG Z, et al. Journal of Computational Physics[J], 2024: 113194.
    [19] KARNIADAKIS G E, KEVREKIDIS I G, LU L, et al. Nature Reviews Physics[J], 2021, 3(6): 422-40.
    [20] ZHU S P, WANG L, LUO C, et al. Philos Trans A Math Phys Eng Sci[J], 2023, 381(2260): 20220406.
    [21] RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Journal of Computational Physics[J], 2019, 378: 686-707.
    [22] SATORRAS V G, HOOGEBOOM E, WELLING M. proceedings of the International conference on machine learning[C], F, 2021.
    [23] DEGRAVE J, FELICI F, BUCHLI J, et al. Nature[J], 2022, 602(7897): 414-9.
    [24] RADAIDEH M I, DU K, SEURIN P, et al. Nuclear Engineering and Design[J], 2023, 412.
    [25] HO J, JAIN A, ABBEEL P. Advances in neural information processing systems[J], 2020, 33: 6840-51.
    [26] WU T, MARUYAMA T, WEI L, et al. proceedings of the The Twelfth International Conference on Learning Representations[C], F, 2024.
    [27] HAGRMAN D L, REYMANN G A. MATPRO-Version 11: a handbook of materials properties for use in the analysis of light water reactor fuel rod behavior[R], 1979.
    [28] LU Y, HUANG X, REN Z, et al. Journal of Nuclear Materials[J], 2023, 583.
    [29] KAUTZ E J, HAGEN A R, JOHNS J M, et al. Computational Materials Science[J], 2019, 161: 107-18.
    [30] XU F, CAI L, SALVATO D, et al. Sci Rep[J], 2023, 13(1): 10616.
    [31] YANG T-X, DOU P. Materials Characterization[J], 2024, 211.
    [32] DUTTA S, ROBI P S. Metals and Materials International[J], 2022, 28(12): 2884-97.
    [33] DUTTA S, ROBI P S. Mechanics of Time-Dependent Materials[J], 2024.
    [34] ZHANG X-C, GONG J-G, XUAN F-Z. International Journal of Fatigue[J], 2021, 148.
    [35] SAI N J, SRIDHARAN K, CHAUHAN A. Progress in Nuclear Energy[J], 2024, 177.
    [36] KOROTAEV P, YANILKIN A. Computational Materials Science[J], 2025, 246.
    [37] JIN M, CAO P, SHORT M P. Journal of Nuclear Materials[J], 2019, 523: 189-97.
    [38] BAYDOUN S, FARTAS M, FOUVRY S. Tribology International[J], 2023, 177: 107936.
    [39] PARK M J, SHIN Y G, ALMOMANI B, et al. Nuclear Engineering and Design[J], 2024, 421.
    [40] ZHANG W, WANG X, AI Y, et al. Materials Today Communications[J], 2024, 38.
    [41] ZHANG T, JIAO Y, LIU Z, et al. Nuclear Engineering and Technology[J], 2024.
    [42] CRAVEN G T, CHEN R, COOPER M W D, et al. Computational Materials Science[J], 2023, 230.
    [43] HASAN T, CAPOLUNGO L, ZIKRY M. npj Materials Degradation[J], 2023, 7(1): 22.
    [44] CHE Y, WU X, PASTORE G, et al. Annals of Nuclear Energy[J], 2021, 153.
    [45] DHULIPALA S L, SHIELDS M D, CHAKROBORTY P, et al. Reliability Engineering & System Safety[J], 2022, 226: 108693.
    [46] ZHAO Y, CHEN Z, DONG Y, et al. Materials Today Communications[J], 2023, 37.
    [47] DUBOIS E T, TRANCHIDA J, BOUCHET J, et al. Physical Review Materials[J], 2024, 8(2).
    [48] STIPPELL E, ALZATE-VARGAS L, SUBEDI K N, et al. Artificial Intelligence Chemistry[J], 2024, 2(1).
    [49] WANG H, PAN X-L, WANG Y-F, et al. Journal of Nuclear Materials[J], 2022, 572.
    [50] CHEN H, YUAN D, GENG H, et al. Computational Materials Science[J], 2023, 229.
    [51] KRUGLOV I A, YANILKIN A, OGANOV A R, et al. Physical Review B[J], 2019, 100(17).
    [52] KOBAYASHI K, OKUMURA M, NAKAMURA H, et al. Sci Rep[J], 2022, 12(1): 9808.
    [53] HAO M , GUAN P. Chinese Physics B[J], 2023, 32(9).
    [54] WANG H, GUO X, ZHANG L, et al. Applied Physics Letters[J], 2019, 114(24).
    [55] LADYGIN V V, KOROTAEV P Y, YANILKIN A V, et al. Computational Materials Science[J], 2020, 172.
    [56] YANG W, YE J, BI P, et al. Materials Today Communications[J], 2024, 38.
    [57] KOSKENNIEMI M, BYGGM?STAR J, NORDLUND K, et al. Journal of Nuclear Materials[J], 2023, 577: 154325.
    [58] QI S-M, BO T, ZHANG L, et al. Artificial Intelligence Chemistry[J], 2024, 2(1).
    [59] FENG T, ZHAO J, LIANG W, et al. Computational Materials Science[J], 2022, 210.
    [60] WANG J, GHOSH D B, ZHANG Z. Materials (Basel)[J], 2023, 16(14).
    [61] Hong Liang(洪亮), Jin Xin(金鑫), Liu Xiaohan(刘虓瀚), et al. Journal of Shenzhen University(深圳大学学报)[J], 2022, 39(05): 515-20.
    [62] Wang Dongdong(王东东), Yang Hongyi(杨红义), Wang Duan(王端), et al. Atomic Energy Science and Technology(原子能科学技术)[J], 2020, 54(10): 1809-16.
    [63] WU H, LI R, ZHAO P, et al. Frontiers in Energy Research[J], 2022, 10: 852146.
    [64] Liu Zhenhai(刘振海), Qi Feipeng(齐飞鹏), Zhou Yi(周毅), et al. Nuclear Power Engineering(核动力工程)[J], 2023, 44(S2): 1-5.
    [65] WEI X, WAN J, ZHAO F. Science and Technology of Nuclear Installations[J], 2016, 2016: 1-6.
    [66] WANG Y, WEI J, WANG J, et al. Journal of Nuclear Science and Technology[J], 2020, 58(3): 333-46.
    [67] CHE Y, YURKO J, SEURIN P, et al. Annals of Nuclear Energy[J], 2022, 168.
    [68] ZHOU W, ROBERTSON G, SJ?STRAND H. Annals of Nuclear Energy[J], 2025, 211.
    [69] ZHANG T, JIAO Y, LIU Z, et al. Topfuel 2024, Light Water Reactor Fuel Performance Conference[C]. Franch. 2024.
    [70] ROSS M, LIN T Y, GOULD D, et al. Energies[J], 2022, 15(11).
    [71] YAN B, GAO R, LIU P, et al. International Journal of Heat and Mass Transfer[J], 2020, 159.
    [72] TAN F, JIANG Y, LEI Q, et al. Journal of Materials Research and Technology[J], 2024, 31: 1326-36.
    [73] HE J, LI Z, ZHAO P, et al. Journal of Materials Research and Technology[J], 2024, 33: 260-86.
    [74] WEN C, ZHANG Y, WANG C, et al. Acta Materialia[J], 2019, 170: 109-17.
    [75] BAI B, HAN X, ZHENG Q, et al. Fusion Engineering and Design[J], 2020, 161.
    [76] BAI B, HAN X, WU S, et al. Mechanical Engineering Journal[J], 2024, 11(2): 23-00483-23-.
    [77] Bao Jiaming(包佳明), Bai Bing(白冰), Ke Yixuan(柯艺璇), et al. Atomic Energy Science and Technology(原子能科学技术)[J], 2024, 58(S1): 121-30.
    [78] TAHERANPOUR N, TALEBI S. Progress in Nuclear Energy[J], 2021, 131.
    [79] GHASABIAN M, TALEBI S, SAFARZADEH O. Progress in Nuclear Energy[J], 2021, 142.
    [80] GHASABIAN M, TALEBI S, SAFARZADEH O. Progress in Nuclear Energy[J], 2023, 163.
    [81] TAN J, LI Q, ZHAO B, et al. Annals of Nuclear Energy[J], 2022, 169.
    [82] BARATI R. Annals of Nuclear Energy[J], 2014, 70: 56-63.
    [83] ZHANG C, LIU J, LI X, et al. Journal of Nuclear Materials[J], 2023, 585.
    [84] RAZA W, KIM K-Y. Nuclear Engineering and Design[J], 2008, 238(6): 1332-41.
    [85] LEITE V C, SCHIRRU R, NETO M M. Nuclear Technology[J], 2018, 205(5): 637-45.
    [86] WAN C, LI W, YANG B, et al. Progress in Nuclear Energy[J], 2024, 173.
    [87] SOBES V, HISCOX B, POPOV E, et al. Sci Rep[J], 2021, 11(1): 19646.
    [88] GU M, HUANG D, ZHOU X, et al. proceedings of the Proceedings of the Seventh Asia International Symposium on Mechatronics: Volume II[C], F, 2020 . Springer.
    [89] ZHANG B, MIAO Y, TIAN Y, et al. Journal of Nuclear Science and Technology[J], 2021, 58(7): 787-96.
    [90] LI F, ZHANG B, ZHANG B, et al. Journal of Nuclear Science and Technology[J], 2024: 1-12.
    [91] RAMOS A, CARRASCO A, FONTANET J, et al. Nuclear Engineering and Design[J], 2024, 417.
    [92] SUO X, LIU J, DONG L, et al. Journal of Intelligent Manufacturing[J], 2021, 33(6): 1649-63.
    [93] Zhang Qiyu(张器宇). Detection of Coated Particles Based on Machine Vision(基于机器视觉的包覆球燃料颗粒检测)[D], 2020.
    [94] Wang Yuanyuan(王媛媛). Research on the Detection Technology of the Pose for Nuclear Fuel Ball Based on Machine Vision(基于机器视觉的核燃料球姿态检测技术研究)[D], 2019.
    [95] Cheng Wei(程伟), Wang Lei(王磊), Hu Jianhua(胡建华), et al. Nuclear Power Engineering(核动力工程)[J], 2020, 41(06): 198-201.
    [96] Meng Yu(孟宇). Research on recognition and location algorithm of nuclear fuel Rod based on machine vision(基于机器视觉的核燃料棒识别与定位算法研究)[D], 2022.
    [97] Li Hao(李豪). Auxiliary Positioning System of Nuclear Power Plant Refueling Based on Machine Vision(基于机器视觉的核电站换料辅助定位系统研究)[D], 2021.
    [98] ABDAR M, POURPANAH F, HUSSAIN S, et al. Information Fusion[J], 2021, 76: 243-97.
    [99] LAKSHMINARAYANAN B, PRITZEL A, BLUNDELL C. Advances in neural information processing systems[J], 2017, 30.
    [100] WU T, NEISWANGER W, ZHENG H, et al. proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, F, 2024 [C].
    [101] ROMANO Y, PATTERSON E, CANDES E. Advances in neural information processing systems[J], 2019, 32.
    [102] AGGARWAL C C, KONG X, GU Q, et al. Active learning: A survey[M]. Data classification. Chapman and Hall/CRC. 2014: 599-634.
    [103] OWOYELE O, PAL P. Journal of Energy Resources Technology[J], 2021, 143(3): 032307.
    [104] ZENG J, ZHANG D, LU D, et al. The Journal of Chemical Physics[J], 2023, 159(5).
    [105] WEI H, WU C T, HU W, et al. Journal of Engineering Mechanics[J], 2023, 149(3).
    [106] BETZLER B R, ADE B J, JAIN P K, et al. Nuclear Science and Engineering[J], 2022: 1-26.
    [107] SHIN S, SHIN D, KANG N. Journal of Computational Design and Engineering[J], 2023, 10(4): 1736-66.
    [108] SENHORA F V, CHI H, ZHANG Y, et al. Computer Methods in Applied Mechanics and Engineering[J], 2022, 398.
    [109] MAZé F, AHMED F. proceedings of the Proceedings of the AAAI conference on artificial intelligence[C], F, 2023.
    [110] XIE T, FU X, GANEA O-E, et al. arXiv preprint arXiv:211006197[J], 2021.
    [111] YANG H, HU C, ZHOU Y, et al. arXiv preprint arXiv:240504967[J], 2024.
    [112] MERCHANT A, BATZNER S, SCHOENHOLZ S S, et al. Nature[J], 2023, 624(7990): 80-5.
    [113] SZYMANSKI N J, RENDY B, FEI Y, et al. Nature[J], 2023, 624(7990): 86-91.
    [114] ABOLHASANI M, KUMACHEVA E. Nature Synthesis[J], 2023, 2(6): 483-92.
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张涛,焦拥军,刘振海,邱玺,向羿龙,黄好越,兰峋,辛勇,李垣明.人工智能在核燃料及材料领域的应用现状和发展趋势[J].稀有金属材料与工程,,().[Zhang Tao, Jiao Yongjun, Liu Zhenhai, Qiu Xi, Xiang Yilong, Huang Haoyue, Lan Xun, Xin Yong, Li Yuanming. The Current Status and Development Trends of Artificial Intelligence in the Field of Nuclear Fuel and Materials[J]. Rare Metal Materials and Engineering,,().]
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  • 收稿日期:2024-10-25
  • 最后修改日期:2024-12-21
  • 录用日期:2025-01-03