基于“全局-局部”搜索的核反应堆运行孪生反问题求解

Global-local Search Based Inverse Problem Solver for Reactor Operation Digital Twin

反应堆运行孪生在反应堆运行过程中为反应堆提供实时的参数和物理场估计,为后续相关安全参数计算提供输入。反问题求解是反应堆运行孪生的核心模块,是确保运行孪生参数和物理场估计的实时性和准确性的关键。当前的运行孪生反问题求解方法依赖于初始参数的估计,其估计精度直接决定数字孪生的精度。为了提高运行孪生反问题求解精度和计算效率,本文提出了“全局-局部”搜索(GLS)的反问题求解方法。对基于华龙一号构建的反应堆运行孪生进行了测试,考察了观测量无噪声和有噪声时反问题求解的精度和计算效率。结果表明,此方法可为反应堆运行孪生提供实时准确的参数和物理场估计,为工程实践打下了基础。

The reactor operation digital twin provides real-time parameter and state estimation for the reactor during operation,providing input for subsequent safety parameter calculations.As one of the core modules of the reactor operation digital twin,the inverse problem solver is crucial to ensure the real-time and accuracy of parameter and state estimation.In the previous work,the inverse problem is solved based on an initial guess of the input parameter,and the quality is highly depend on the initial guess.In order to improve the accuracy and computational efficiency,a global-local search(GLS)method was proposed in this work.Firstly,the reduced order method and KNN(K-nearest neighbor)method were used to build a reduced forward model,with which one can compute the physical field for a given input parameter online.The inverse problem,i.e.,the process of calculating from a set of observations the causal factors that produced them,was then solved using GLS.In the global search stage,the KNN method was used to predict an initial input parameter based on the observed values.In the local search stage,the Latin hypercube sampling(LHS)method was used to discrete the local neighborhood of the initial parameters.The theoretical observation values corresponding to the parameters were calculated based on the reduced forward model,which was also trained using KNN.The optimal input parameter was then determined with which the observations were closest to the actual observation values.Numerical tests were conducted on HPR1000 reactor operation digital twin.To simulate the power distribution of HPR1000,the burnup,the power rate,the control rod inserting steps and the inlet temperature of the coolant were selected as input parameters,and the neutronic code CORCA-3D was used to calculate the power distribution with the four-dimensional input parameters.The digital twin was constructed over a wide range of the four-dimensional input parameters.The synthetic observations,calculated with CORCA-3D,were used to simulate the real observations of the in-core self-powered neutron detectors(SPNDs).The inverse problem solvers with different methods were used to infer the four-dimensional input parameters using the synthetic observations.The accuracy and computational efficiency for parameter inference and the related physical field with and without noisy observations were investigated using GLS,KNN and LHS.Numerical results verify that the proposed GLS outperforms other methods,and provides real-time and accurate parameter and state estimation for reactor operation digital twin,laying the foundation for engineering practice.