Inverse method study based on Characteristic Statistic Algorithm (CSA) with application in estimating core states parameters

In reactor-related scenarios, inverse methods utilizing monitoring data are frequently employed to address issues such as state estimation, accident analysis, and power reconfiguration. These problem-solving approaches can entail converting the original problem into a parameter estimation problem. This paper presents a versatile and scalable framework for solving inverse problems by iterative optimization. To address intricate core parameter problems effectively, we incorporate the CPACT program, a physical thermal engineering solution program. Additionally, we employ the global optimization algorithm known as CSA (Characteristic Statistic Algorithm) to tackle specific challenges associated with nonlinear global optimization. To evaluate the performance of our framework, a numerical simulation model based on the Daya Bay Nuclear Power Plant was used as the test object design as our test subject.