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Dual power supply cascaded-type power electronic transformers (DPSC-PET) is connected to two power supplies with high operational reliability and flexible operation mode, and it can be widely used in medium and low voltage distribution networks. It is of great importance to conduct in-depth research on its voltage sag tolerance and regulation method for maintaining efficient energy transmission of DPSC-PET as well as high quality power supply during voltage sag. Firstly, the topological structure and control strategy of DPSC-PET are analyzed. Secondly, the influence factors of voltage sag tolerance of DPSC-PET are analyzed for three-phase symmetrical voltage sag which has the most serious power shortage. Then, from the perspective of power balance, a real-time analysis method of voltage sag immunity of DPSC-PET as well as power coordination method between dual input ports when voltage sag occurs on different power supply are proposed to achieve a perfect recovery of low-voltage DC bus voltage, which means the significant improvement of DPSC-PET to cope with transient disturbances. Finally, the simulation model of DPSC-PET is established and the simulations for voltage sag occurred with different magnitudes on different power supply are cited out. The results show that the voltage sag tolerance of DPSC-PET is significantly improved with the proposed method.

In the present study, a data-driven neural network methodology was proposed for the rapid prediction of multi-physics interactions in heat pipe reactors, and grounded in a comprehensive framework of multi-physics coupling. By leveraging neural networks, certain numerical computation modules within the framework were effectively replaced, which facilitated an efficient resolution of the iterative process inherent to the nuclear-thermal-mechanical multi-physics coupling. In the process of multi-physics coupling, there exists a sophisticated iterative mechanism, wherein the computational analyses of distinct physical domains largely function autonomously. When contemplating the integration of neural network paradigms as surrogates within the multi-physics coupling framework, it is pivotal to target the numerical computations intrinsic to individual physical domains. By deploying one or multiple neural network models as substitutes, this methodology not only sustains the scalability inherent to rapid predictive schemes but also attenuates the complexity associated with the neural network’s surrogate functionalities. Concurrently, this approach necessitates a reduced dataset for training, and through the amalgamation of diverse neural network models, markedly augments the adaptability of the overarching rapid predictive framework. Furthermore, multiple neural networks, operating autonomously, can be dynamically calibrated with respect to their training data volumes based on their predictive performance. This not only optimizes their predictive accuracy but also facilitates the cost-effective expansion of training datasets in accordance with specific model requirements. Taking “Megapower” as the subject of analysis, pertinent neural network models were constructed and employed for rapid predictive parameter estimation, with the results juxtaposed against traditional numerical computations. The discrepancies between the key parameters yielded by rapid predictions and those from numerical simulations were minimal. Specifically, the maximum stress differential does not exceed 2 MPa, and the average fuel temperature difference is less than 3 K. Notably, while numerical computations demand six hours of processing time, the rapid prediction on the same platform requires a mere 4 minutes, marking a reduction in computational time by over 95%. Given these outcomes, it can be posited that neural network-based rapid prediction schemes exhibit high precision and accelerated processing speeds. Coupled with their inherent flexibility and modest training data requirements, and the capacity for targeted model optimization, it is advocated that neural network-based rapid prediction serves as a preferred methodology for addressing challenges such as core optimization in scenarios that demand large-scale computations. Concurrently, the neural network models utilized in rapid predictions have less stringent computational resource requirements and offer greater adaptability to varied environments. Capitalizing on these attributes, in conjunction with contemporary compact mobile reactors, one can facilitate on-site deployments with relative ease. Such integrations pave the way for prompt alerts in accident scenarios, thereby enhancing the safety of the reactor core.

The deposition of corrosion products in the core causes CRUD (corrosion related unidentified deposit) induced power shift and CRUD induced localized corrosion, threatening the safety and economy of the reactor. Zinc addition is regarded as a widely used method to effectively alleviate corrosion product deposition in reactor core. Despite the wide spread use of zinc, the understanding of mass balance, thermodynamic equilibrium and kinetic substitution of zinc in the primary circuit is still not clear. This paper focuses on the balance and thermodynamic kinetic behaviors of zinc in the primary circuit from the perspectives of theoretical mechanism and numerical calculation analysis. A mass balance model of zinc in the primary circuit was established to evaluate the addition, removal, deposition, precipitation and substitution of zinc in the primary circuit. The thermodynamic equilibrium and substitution kinetics of zinc in coolant were also evaluated. Theoretical analysis shows that zinc in the coolant is involved in hydrolysis and chemical reactions with iron, nickel, silicon and other elements. Zinc mainly exists in the form of zinc hydroxide under the prototypical conditions of coolant water chemistry. When zinc replaces the iron and nickel elements in the oxide corrosion layer, it tends to replace the divalent ions at the tetrahedral positions in the normal spinel, and the ability of zinc to replace the divalent ions in the inverse spinel needs to be evaluated in combination with the hydrolysis and chemical equilibrium of zinc. Numerical analysis shows that the mass balance prediction results of the model are in good agreement with the observation value from Diablo Canyon 1 reactor plant. Gradually reducing zinc addition amount is an effective means to maintain the zinc concentration in the specific optimal range. Zinc addition can effectively alleviate corrosion release up to about 50% and the thickness of CRUD deposition after zinc addition is 42% of that before zinc addition. This study can provide theoretical support to develop the optimal zinc addition technique.