Several applications such as nuclear forensics, nuclear fuel cycle simulations and sensitivity analysis require methods to quickly compute spent fuel nuclide compositions for various irradiation histories. Traditionally, this has been done by interpolating between one-group cross-sections that have been pre-computed from nuclear reactor simulations for a grid of input parameters, using fits such as Cubic Spline. We propose the use of Gaussian Processes (GP) to create surrogate models, which not only provide nuclide compositions, but also the gradient and estimates of their prediction uncertainty. The former is useful for applications such as forward and inverse optimization problems, the latter for uncertainty quantification applications. For this purpose, we compare GP-based surrogate model performance with Cubic- Spline-based interpolators based on infinite lattice simulations of a CANDU 6 nuclear reactor using the SERPENT 2 code, considering burnup and temperature as input parameters. Additionally, we compare the performance of various grid sampling schemes to quasirandom sampling based on the Sobol sequence. We find that GP-based models perform significantly better in predicting spent fuel compositions than Cubic-Spline-based models, though requiring longer computational runtime. Furthermore, we show that the predicted nuclide uncertainties are reasonably accurate. While in the studied two-dimensional case, grid- and quasirandom sampling provide similar results, quasirandom sampling will be a more effective strategy in higher dimensional cases.

翻译：核法证、核燃料循环模拟和敏感度分析等若干应用方法要求迅速计算各种辐照历史的乏燃料核素成分。 传统上,这是通过从核反应堆模拟中为输入参数网格预先计算出来的一组交叉剖面之间的相互交错而完成的。 我们提议使用高森进程(GP)来创建代金模型,不仅提供核素成分,而且提供其预测不确定性的梯度和估计值。 前者对前方和反面优化问题等应用有用,而后者则用于不确定性量化应用。 为此,我们用SERPENT 2 代码来将基于核反应堆6 的无穷不粘度模拟的GP-S-Spline 模型性能与基于CUbic-Spline的内切分解剖面模型进行对比,将燃烧和温度作为输入参数。 此外,我们将各种电网取样方法的性能与基于索伯尔序列的准兰取样方法进行对比。 我们发现,基于GP-NBO的基取样模型在预测的精确度模型中,虽然需要更精确的精确的精确度模型,但我们的精确性模型在预测中进行更精确的计算。