High-fidelity computational fluid dynamics (CFD) simulations are widely used to analyze nuclear reactor transients, but are computationally expensive when exploring large parameter spaces. Multifidelity surrogate models offer an approach to reduce cost by combining information from simulations of varying resolution. In this work, several multifidelity machine learning methods were evaluated for predicting the time to onset of natural circulation (ONC) and the temperature after ONC for a high-temperature gas reactor (HTGR) depressurized loss of forced cooling transient. A CFD model was developed in Ansys Fluent to generate 1000 simulation samples at each fidelity level, with low and medium-fidelity datasets produced by systematically coarsening the high-fidelity mesh. Multiple surrogate approaches were investigated, including multifidelity Gaussian processes and several neural network architectures, and validated on analytical benchmark functions before application to the ONC dataset. The results show that performance depends strongly on the informativeness of the input variables and the relationship between fidelity levels. Models trained using dominant inputs identified through prior sensitivity analysis consistently outperformed models trained on the full input set. The low- and high-fidelity pairing produced stronger performance than configurations involving medium-fidelity data, and two-fidelity configurations generally matched or exceeded three-fidelity counterparts at equivalent computational cost. Among the methods evaluated, multifidelity GP provided the most robust performance across input configurations, achieving excellent metrics for both time to ONC and temperature after ONC, while neural network approaches achieved comparable accuracy with substantially lower training times.

翻译：高保真计算流体动力学（CFD）模拟被广泛用于分析核反应堆瞬态过程，但在探索大参数空间时计算成本高昂。多保真度代理建模通过融合不同分辨率模拟的信息，提供了一种降低成本的途径。本研究评估了多种多保真度机器学习方法，用于预测高温气冷堆（HTGR）失压强迫冷却丧失瞬态过程中自然循环起始时间（ONC）及ONC后的温度。我们在Ansys Fluent中开发了一个CFD模型，以在每个保真度级别生成1000个模拟样本，其中低、中保真度数据集通过对高保真度网格进行系统粗化来生成。我们研究了多种代理建模方法，包括多保真度高斯过程及多种神经网络架构，并在应用于ONC数据集前，通过解析基准函数进行了验证。结果表明，模型性能在很大程度上取决于输入变量的信息量以及各保真度级别之间的关系。使用通过先验敏感性分析确定的主导输入变量训练的模型，其性能始终优于使用完整输入集训练的模型。低-高保真度配对产生的性能优于涉及中保真度数据的配置，并且双保真度配置在同等计算成本下通常匹配或优于三保真度配置。在所评估的方法中，多保真度高斯过程（GP）在所有输入配置下均表现出最稳健的性能，在ONC时间和ONC后温度两个指标上均取得了优异的度量结果，而神经网络方法在显著降低训练时间的同时，达到了相当的精度。