A new Cross-section calculation method in HTGR engineering simulator system based on Machine learning methods. (15th September 2020)

• Record Type: Journal Article
• Authors: Li, Zeguang; Sun, Jun; Wei, Chunlin; Sui, Zhe; Qian, Xiaoye
• Is Part Of: Annals of nuclear energy. Volume 145(2020)
• Publication Date: 2020-09-15
• Subjects: Cross-section calculation -- Engineering simulator -- HTGR -- Machine learning methods; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2020.107553
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Proposed a cross-section calculation method for HTGR ESS based on machine learning. The method could achieved more accurate results than present method in HTGR ESS. The proposed method also meets the real-time requirement of HTGR ESS. Detailed comparisons have been made to prove the feasibility of proposed method. Abstract: To give better reactor physics results, the detailed 3-D diffusion module has been implemented in HTGR engineering simulator system (ESS), which needs more efficient cross-section calculations to meet real-time requirement of ESS. To calculated the HTGR's cross-sections, more reactor variables are needed with larger scope and more complex non-linear relationships, which makes the polynomial method used well in LWR simulators hard to meet full range accuracy. In this paper, a new cross-section calculation method based on machine learning is proposed. The proposed method uses deep neuron network to consider the complex non-linear relationships between reactor variables and uses tree regression to achieve accurate cross-sections in full range, which are trained from the scattered database generated by V.S.O.P.. With the numerical tests, the proposed cross-section calculation method is proved to achieve much more accurate cross-section results than present polynomial method and also fast enough to be used in the HTGR ESS.