发表机构
Colorado School of Mines; Los Alamos National Laboratory（科罗拉多矿业学院; 洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用定向人工神经网络，仅以燃料核素原子浓度为输入，在固定几何和温度下准确预测二氧化铀栅元中的自屏蔽多群微观截面，并在轻水反应堆富集度和燃耗范围内保持高精度，验证了其有效性。
AI 中文摘要
多群中子输运模型因其相对于连续能量方法在内存和计算效率上的优势，被广泛应用于模拟代码中。然而，多群代码依赖于使用近似方法准备的核数据，这些方法需要大量的人工工作和专业知识来处理自屏蔽效应。此外，以这种方式准备的多群核数据库在模拟各种反应堆和运行条件方面的适用性可能有限。在此，我们展示了经过训练的人工神经网络能够在包含固定几何和温度的二氧化铀燃料的栅元模拟中准确预测自屏蔽微观截面。该模型的预测精度在轻水反应堆中遇到的燃料富集度和燃耗范围内保持较高。关键的是，神经网络的预测仅需要燃料组成核素的原子浓度作为输入。网络使用通过连续能量OpenMC栅元模拟生成的8,704个训练和验证数据样本进行训练。训练后的神经网络预测CASMO-8能群结构中90种核素组合的自屏蔽总截面、裂变截面、吸收截面、弹性散射截面和总散射截面。通过将神经网络的预测与训练期间未见过的1,500个参考截面样本进行比较，并使用OpenMC模拟进行统计，验证了自屏蔽截面的预测。测试数据集中预测截面与真实截面平均一致在0.119%以内。当预测截面用于多群特征值计算时，keff的平均绝对误差为200 pcm。
英文摘要
Multigroup neutron transport models are used widely in simulation codes due to their memory and compute efficiency compared to continuous energy methods. However, multigroup codes rely on nuclear data prepared using approximate methods that require significant manual effort and expertise to account for self-shielding effects. In addition, multigroup nuclear data libraries prepared in this way can be limited in their applicability for modeling diverse reactors and operating conditions. Here we show that trained artificial neural networks can predict self-shielded microscopic cross sections accurately in pincell simulations containing uranium dioxide fuel with a fixed geometry and temperature. The model's predictive accuracy remains high across fuel enrichment and burnup ranges encountered in light water reactors. Critically, the neural networks' predictions require only the atomic concentration of the fuel's constituent nuclides as inputs. The networks are trained using 8,704 samples of training and validation data generated using continuous energy OpenMC pincell simulations. The trained neural networks predict self-shielded total, fission, absorption, elastic, and total scattering cross sections for combinations of 90 nuclides in the CASMO-8 energy group structure. The neural networks' predictions of self-shielded cross sections are validated by comparison with 1,500 samples of reference cross sections unseen during training and tallied using OpenMC simulations. The predicted and ground truth cross sections in the test data set agree to within 0.119% on average. When the predicted cross sections are used in a multigroup eigenvalue run, they achieve a mean absolute error in keff of 200 pcm.
Comments28 pages, 7 figures
