Investigation and improvement of the Mini-Max Polynomial Approximation method for solving burnup equations. (January 2023)

• Record Type: Journal Article
• Authors: Zhang, Cong; Zhang, Binhang; Bi, Yanzhao; Yuan, Xianbao; Zhang, Yonghong; Tang, Haibo
• Is Part Of: Annals of nuclear energy. Volume 180(2023)
• Publication Date: 2023-01
• Subjects: Burnup calculations -- MMPA method -- Combined Matrix Iterative Algorithm -- Benchmark; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2022.109482
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: The Combined Matrix Iterative Algorithm (MMPA-CMIA) based on the MMPA method is proposed for solving burnup equations. The numerical precision of three algorithms based on the MMPA method is compared with that of 16th-order CRAM in detail. The accuracy and efficiency of MMPA-CMIA are verified by the benchmarks. Abstract: In this work, a new efficient solution algorithm named Combined Matrix Iterative Algorithm based on the Mini-Max Polynomial Approximation (MMPA) method is proposed for burnup calculations. As a new matrix exponential method in recent years, MMPA method has good numerical stability and computational accuracy, and all calculation are real number operations. As the same with Chebyshev Rational Approximation Method (CRAM) which is widely and successfully used in solving burnup equations, the method can also directly deal with rigid and sparse burnup matrices. Compared with the traditional solution algorithms based on MMPA method, the Combined Matrix Iterative Algorithm (MMPA-CMIA) proposed in this paper can effectively reduce the computational complexity and improve the computational efficiency after equivalently transforming the expression of the MMPA method. Then the MMPA-CMIA is used to theoretically demonstrate and analyze the solution efficiency of random matrices with different orders which has the similar distribution characteristics to burnup matrices. Compared with the 16-order CRAM method and the other two traditional solution algorithms based on MMPA method, the algorithm shows good acceleration effect and computational precision. Finally, the algorithm is used in the actual burnup benchmarks to verify and validate the accuracy and efficiency. The burnup matrices of different orders are constructed through detailed burnup chain and simplified burnup chain. The results show that the algorithm has good accuracy and numerical stability. And comparing with 16-order CRAM, the algorithm can save about 30% of computing time for solving burnup equations. It shows that the algorithm is a potentially powerful method for accelerating the solution of large-scale burnup calculations.