In this work, a Nearly Autonomous Management and Control (NAMAC) system is designed to diagnose the reactor state and provide recommendations to the operator for maintaining the safety and performance of the reactor. A three layer-hierarchical workflow is suggested to guide the design and development of the NAMAC system. The three layers in this workflow corresponds to knowledge base, digital twin developmental layer (for different NAMAC functions), and NAMAC operational layer. Digital twin in NAMAC is described as knowledge acquisition system to support different autonomous control functions. Therefore, based on the knowledge base, a set of digital twin models is trained to determine the plant state, predict behavior of physical components or systems, and rank available control options. The trained digital twin models are assembled according to NAMAC operational workflow to support decision-making process in selecting the optimal control actions during an accident scenario.
To demonstrate the capability of the NAMAC system, a case study is designed, where a baseline NAMAC is implemented for operating a simulator of the Experimental Breeder Reactor II (EBR-II) during a single loss of flow accident. Training database for development of digital twin models is obtained by sampling the control parameters in the GOTHIC data generation engine. After the training and testing, the digital twins are assembled into a NAMAC system according to the operational workflow. This NAMAC system is coupled with the GOTHIC plant simulator, and a confusion matrix is generated to illustrate the accuracy and robustness of implemented NAMAC system. It is found that within the training databases, NAMAC can make reasonable recommendations with zero confusion rate. However, when the scenario is beyond the training cases, the confusion rate increases, especially when the scenarios are more severe. Therefore, a discrepancy checker is added to detect unexpected reactor states and alert operators for safety-minded actions.
Best Estimate Plus Uncertainty (BEPU) approaches for nuclear reactor applications have been extensively developed in recent years. The challenge for BEPU approaches is to achieve multi-physics modeling with an acceptable computational cost while preserving a reasonable fidelity of the physics modeled. In this work, we present the core multi-physics computational framework developed for the efficient computation of uncertainties in Light Water Reactor (LWR) simulations. The subchannel thermal-hydraulic code CTF and the nodal expansion neutronic code PARCS are coupled for the multi-physics modeling (CTF-PARCS). The computational framework is discussed in detail from the Polaris lattice calculations up to the CTF-PARCS coupling approaches. Sampler is used to perturb the multi-group microscopic cross-sections, fission yields and manufacturing parameters, while Dakota is used to sample the CTF input parameters and the boundary conditions. Python scripts were developed to automatize and modularize both pre- and post-processing. The current state of the framework allows the consistent perturbation of inputs across neutronics and thermal-hydraulics modeling. Improvements to the standard thermal-hydraulics modeling for such coupling approaches have been implemented in CTF to allow the usage of 3D burnup distribution, calculation of the radial power and the burnup profile, and the usage of Santamarina effective Doppler temperature. The uncertainty quantification approach allows the treatment of both scalar and functional quantities and can estimate correlation between the multi-physics outputs of interest and up to the originally perturbed microscopic cross-sections and yields. The computational framework is applied to three exercises of the LWR Uncertainty Analysis in Modeling Phase III benchmark. The exercises cover steady-state, depletion and transient calculations. The results show that the maximum fuel centerline temperature across all exercises is 2474K with 1.7% uncertainty and that the most correlated inputs are the 238U inelastic and elastic cross-sections above 1 MeV.
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