Conference Agenda

Conventional analysis methods for nuclear reactors are inadequate for fulfilling high-fidelity computational demands, and many of them face challenges in achieving compatibility with a variety of distinct models. Furthermore, cross-platform computational approaches may encounter problems such as low computational efficiency, data transfer distortion, and incompatibility in analysis scales. In recent years, physics-informed neural networks (PINNs) and operator learning have emerged as an effective tool for solving partial differential equations (PDEs) governing physical fields. Their potential for application in multi-physics coupling computations within nuclear reactors is particularly noteworthy. This paper introduces deep reactor multi-physics network (DeepRMNet), a novel nuclear reactor multi-physics coupling computational framework combining operator learning and other deep learning methods. DeepRMNet decreases the computational deficiencies inherent in traditional numerical calculation programs, facilitating independent, coupled, and rapid predictions of physical fields such as material temperature field, neutron flux field and coolant flow field based on their physical constraints. The framework addresses challenges including variable material parameters, integral calculations, boundary conditions, eigenvalue functions and so on. In our test model, DeepRMNet has demonstrated favorable computational outcomes and coupling efficiency compared to conventional multi-physics coupling calculations. We argue that DeepRMNet offers a promising tool for reactor multi-physics coupling calculations and digital twin construction, with advantages over numerical calculation-based digital twins.

Introducing new parameters into neural networks often requires significant time, even when existing networks based on experimental data are available. This study proposes a novel approach for the reliability assessment of the Emergency Heat Removal System (EHRS), utilizing Physics-Informed Neural Networks (PINNs), which integrate new parameters into the neural network structure. By encoding new parameters directly into an existing network, this approach avoids the need to rebuild the network from scratch, significantly improving computational efficiency. As a result, PINNs deliver faster response times and enhanced accuracy, demonstrating superior performance compared to conventional neural networks. The method was validated through practical simulations under accident conditions, showing that PINNs outperform traditional models in terms of accuracy and computational efficiency.

The main coolant pump (MCP) is a critical component in transferring the liquid metal in the primary system and removing the decay heat of the core of lead-cooled fast reactors (LFRs), suffering a corrosion possibility from the liquid metal especially in the high-temperature and high-velocity working conditions. Accordingly, the flow field in MCP should be paid more attention in the optimal designing process besides the head and efficiency which are concerned by the traditional design approach. The present study addresses the complexities of multiple, interdependent design parameters for MCPs in LFRs, aiming to develop a surrogate model to obtain real-time solutions of the flow field in the MCPs under various structural and operation conditions. Firstly, numerical simulations were carried out to simulate the flow field structure of MCP under different design parameters, as well as provide some training data. Subsequently, a surrogate model was developed based on the physics-based modeling and the small-sample data from simulations, to enable real-time internal flow field obtaining under varying operating conditions. The model employs proper orthogonal decomposition to identify the primary modes of the flow field within MCP and utilizes a physics-informed neural network to compute the modal coefficients under specific parameters, thereby achieving order reduction and reconstruction of the flow field. Future work will primarily focus on the validation of the surrogate model using the experimental platform of the LFR main pump. Based on this model, a digital twin of the MCP will be constructed to facilitate rapid intelligent design and operational control.

This study addresses the inverse problem of identifying a sharp, two-dimensional heat source within a conjugate forced convection system. The problem is formulated as a linear under-determined matrix equation, which requires a strong sparsity-promoting regularization. A deterministic-Bayesian hybrid solution framework was employed. The deterministic solution process utilized an iterative reweighted norm (IRN) algorithm to solve the Lp-Lq minimization problem. The enhanced sparsity-promoting capability is obtained through a small q value. An adaptive hyperparameter selection strategy is used to ensure solution convergence and minimize the user influence on the solution process. As a next step, Bayesian inference, with a discontinuity-adaptive Markov random field (DAMRF) prior, quantified solution uncertainty by providing a statistical distribution on the identified heat source magnitude. While the deterministic approach effectively reconstructed coarse geometries, resolving fine features was limited by sensor spacing and noise magnitude. The Bayesian method added uncertainty information on heat source strength but did not modify the (non-perfect) reconstruction shape. The combined features of automated hyperparameter tuning and uncertainty quantification enhance the robustness and reliability of the methodology, offering the potential for application in monitoring thermal-hydraulic systems.

The high-fidelity, high-order spectral element code NekRS developed at Argonne National Laboratory is designed to take full advantage of modern high-performance GPU computing architectures. It has been actively used during the past decade to perform low-to-high fidelity Computational Fluid Dynamics (CFD) simulations in both simple and complex geometries. However, due to its open-source nature and the focus on flexibility and performance over user-friendliness, the case set up in nekRS is not as intuitive or accessible as commercial CFD codes. This complexity often results in a steep learning curve for new users, and setting up cases can consume a significant amount of time. Users must manually define various simulation parameters, which can be error-prone and challenging. This work introduces a specialized chatbot designed to assist users in setting up and managing simulation cases in NekRS. By leveraging a curated database of sample cases, the chatbot guides users through problem domain definition, boundary condition setup, and solver parameter configuration. Leveraging machine learning and natural language processing techniques, the chatbot is designed to interpret user queries, deliver context-aware responses, and recommend relevant case examples customized to individual requirements. Additionally, it can troubleshoot common setup errors and recommend optimization strategies for high-performance computing platforms. The chatbot’s integration into the NekRS workflow aims to improve efficiency, reduce setup errors, and enhance accessibility, ultimately accelerating case preparation and enabling broader adoption of NekRS.

Developing a digital twin model for the reactor upper plenum and hot leg is critical for accurate and real-time monitoring of flow and temperature distributions. However, the complex internal structures and flow fields make traditional CFD methods unsuitable for real-time computation. Furthermore, the significant three-dimensional transient mixing effects cause the flow field to be highly sensitive to inlet boundary changes, while models based on limited CFD data fail to respond effectively to transient dynamics. This study proposes an efficient and high-fidelity transient digital twin modeling method combining non-intrusive model reduction with Fourier transform. CFD simulations generate full-order transient flow data under varing inlet temperature distributions. Fourier transform is used to extract mean flow fields, spatially averaged amplitudes and frequencies as key descriptors for the model. Proper orthogonal decomposition (POD) and artificial neural networks (ANNs) construct a rapid prediction model, and genetic algorithms enable inversion of inlet temperature distributions and high-fidelity reconstruction of spatial flow fields using measurement data. The results show that the digital twin model can rapidly predict the full mean flow field, average amplitude, and frequency under given inlet temperature distributions, capturing complex transient flow behaviors. It also enables inversion of inlet temperature distributions and high-fidelity reconstruction of full spatial flow fields, providing a novel pathway for reactor digitalization.