*主持人：朱升峰 教授

*讲座内容简介：

In this talk, we introduce a machine learning approach that combines reduced-order models data assimilation in order to create a operation digital twin to predict the power distribution over the core in the operation stage. The operational digital twin is designed to solve forward problems given input operation parameters, as well as to solve inverse problems given some observations of the power field. The forward model is a non-intrusive reduced order model realized with different machine learning methods. For parameter estimation, different inverse models are introduced. The effectiveness in the sense of accuracy and real-time solver of the operation digital twin is illustrated through a real engineering problem in nuclear reactor physics — reactor core simulation in the life cycle of HPR1000 affected by input parameters, i.e., control rod inserting step, burnup, power level and inlet temperature of the coolant, which shows potential applications for on-line monitoring purpose.

*主讲人简介：

龚禾林，本科毕业于清华大学核工程与核技术专业、博士毕业于法国索邦大学数学专业，中国核动力研究设计院高级工程师，现为上海交通大学巴黎卓越工程师学院长聘教轨副教授。长期从事核反应堆工程科学计算理论研究和在线监测前沿技术探索及工程应用研究，在模型降阶、数据同化、在线监测、数字孪生等前沿技术方面取得创造性成绩，有力地支撑了我国先进核动力装置和自主三代核电技术“华龙一号”在线监测、数字孪生系统开发与应用。主持国家自然科学基金项目1项、上海市自然科学基金项目1项、核反应堆系统设计技术国家级重点实验室项目1项、国防科技工业核动力技术创新中心基金项目2项；作为技术负责人主研国家自然科学基金面上项目1项；作为核心成员参研总装、科工局及中核集团重点专项等重大项目4项；在国内外核科学与工程及计算数学类高水平期刊发表学术论文20余篇；撰写国防科技报告4篇；获得国家发明专利授权9项；获四川省科技进步二等奖1项（第一完成人），中国核能行业协会科学技术二等奖1项，中国专利优秀奖1项。