The development of next-generation autonomous control of fission systems, such as nuclear power plants, will require leveraging advancements in machine learning. For fission systems, accurate prediction of nuclear transport is important to quantify the safety margin and optimize performance. The state-of-the-art approach to this problem is costly Monte Carlo (MC) simulations to approximate solutions of the neutron transport equation. Such an approach is feasible for offline calculations e.g., for design or licensing, but is precluded from use as a model-based controller. In this work, we explore the use of Artificial Neural Networks (ANN), Gradient Boosting Regression (GBR), Gaussian Process Regression (GPR) and Support Vector Regression (SVR) to generate empirical models. The empirical model can then be deployed, e.g., in a model predictive controller. Two fission systems are explored: the subcritical MIT Graphite Exponential Pile (MGEP), and the critical MIT Research Reactor (MITR). Findings from this work establish guidelines for developing empirical models for multidimensional regression of neutron transport. An assessment of the accuracy and precision finds that the SVR, followed closely by ANN, performs the best. For both MGEP and MITR, the optimized SVR model exhibited a domain-averaged, test, mean absolute percentage error of 0.17 %. A spatial distribution of performance metrics indicates that physical regions of poor performance coincide with locations of largest neutron flux perturbation -- this outcome is mitigated by ANN and SVR. Even at local maxima, ANN and SVR bias is within experimental uncertainty bounds. A comparison of the performance vs. training dataset size found that SVR is more data-efficient than ANN. Both ANN and SVR achieve a greater than 7 order reduction in evaluation time vs. a MC simulation.
翻译:开发下一代裂变系统自主控制,例如核电厂,将需要在机器学习中利用杠杆优势。对于裂变系统来说,准确预测核运输对于量化安全比值和优化性能十分重要。最先进的方法是成本昂贵的蒙特卡洛(MC)模拟,以近似中子运输方程式的解决办法。这种方法对于离线计算是可行的,例如设计或许可,但不能用作基于模型的控制器。在这项工作中,我们探索人工神经网络(ANN)、渐进推进回归(GBR)、高斯进程回归(GPR)和支持矢量回归(SVR)对于量化实验模型来说非常重要。这个实验模型可以被部署,例如用于模型预测控制器。两个裂变系统被探索:亚临界的MIT图表比模型模型7级指数Pile(MGEP) (MIT) 和关键最大神经网络反应堆(MITR) (MITR) (VNEW) (V) (VR) (VG) (VA-RB) (G) (GR) (G) (G) (G) (GL) (G) (G) (G) (G) (后退后回归回归(G) (G) (G) (G) (G) (R) (G) (R) (的精确回归(G) (G) (P) (P) (的精确回归(G) (G) (G) (G) (G) (G) (G) (P) (P) (P) (的精确回归(G) (P) (P) (S) (P) (P) (的) (的) (的) (S) (S) (的) (的) (S) (S) (S) (S) (和(S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (O(S) (O) (O) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S) (S)