Kobayashi Kazuma, Alam Syed Bahauddin
Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-Champaign, Suite 100 Talbot Laboratory, 104 South Wright Street, Urbana, IL, 61801, USA.
National Center for Supercomputing Application, 205 W Clark Street, Urbana, IL, 61801, USA.
Sci Rep. 2024 Jan 24;14(1):2101. doi: 10.1038/s41598-024-51984-x.
This paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) enabling technology for nuclear energy systems. Machine learning (ML)-based prediction algorithms that need extensive retraining for new reactor operational conditions may prohibit real-time inference for DT across varying scenarios. In this study, DeepONet is trained with possible operational conditions and that relaxes the requirement of continuous retraining - making it suitable for online and real-time prediction components for DT. Through benchmarking and evaluation, DeepONet exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference in solving a challenging particle transport problem. DeepONet also exhibits generalizability and computational efficiency as an efficient surrogate tool for DT component. However, the application of DeepONet reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world DT implementation. Addressing these challenges will further enhance the method's practicality and reliability. Overall, this study marks an important step towards harnessing the power of DeepONet surrogate modeling for real-time inference capability within the context of DT enabling technology for nuclear systems.
本文聚焦于深度神经算子网络（DeepONet）作为一种强大的代理建模方法在核能系统数字孪生（DT）赋能技术背景下的可行性。基于机器学习（ML）的预测算法需要针对新的反应堆运行条件进行大量重新训练，这可能会阻碍DT在不同场景下的实时推理。在本研究中，使用可能的运行条件对DeepONet进行训练，从而放宽了持续重新训练的要求，使其适用于DT的在线和实时预测组件。通过基准测试和评估，DeepONet展现出卓越的预测准确性和速度，优于传统的ML方法，使其成为解决具有挑战性的粒子输运问题时进行实时DT推理的合适算法。DeepONet作为DT组件的高效代理工具，还展现出通用性和计算效率。然而，DeepONet的应用揭示了与最优传感器布置和模型评估相关的挑战，这些是实际DT实施的关键方面。解决这些挑战将进一步提高该方法的实用性和可靠性。总体而言，本研究标志着在核能系统DT赋能技术背景下，利用DeepONet代理建模实现实时推理能力迈出了重要一步。
