In a significant advancement for the nuclear energy sector, researchers from the School of Nuclear Science and Technology at Xi’an Jiaotong University have unveiled a groundbreaking method for optimizing core physical models in pressurized water reactors (PWRs). This innovative approach addresses the critical issue of fuel assembly bowing, a phenomenon that can jeopardize the safety and efficiency of nuclear power generation.
Fuel assembly bowing occurs due to various operational factors, including axial irradiation growth and the high-speed impact of coolant. This bowing can lead to power distribution discrepancies within the reactor core, complicating the safe operation of nuclear facilities. “To ensure the reliable performance of PWRs, it is essential to accurately simulate the effects of fuel assembly bowing on core parameters,” said Guo Lin, one of the lead authors of the study.
The research, published in the journal ‘Yuanzineng kexue jishu’ (which translates to ‘Journal of Energy Science and Technology’), introduces a sophisticated method that fuses measured data with advanced simulation techniques. The process begins with generating a comprehensive dataset, where various core physical models are tested against real-world measurements. By employing artificial neural networks (ANNs), the team was able to establish a functional relationship between these models and the actual performance data.
Building on this foundation, the researchers utilized a three-dimensional variational (3DVAR) assimilation algorithm to refine their simulations. This algorithm helps quantify the differences between the simulated and actual core physical models, leading to a more accurate representation of fuel assembly behavior. “Our method significantly reduces the relative error of power distribution, which is crucial for meeting safety standards in nuclear power operations,” added Zhang Kai, another lead author.
The implications of this research are profound. By enhancing the accuracy of numerical simulations, the optimized models can lead to improved operational safety and efficiency, ultimately benefiting the commercial viability of nuclear energy. The study demonstrated a reduction in the maximum relative error of power distribution from 13.4% to 7.7%, with all optimized model results meeting industrial criteria. This level of precision not only assures regulatory compliance but can also facilitate more effective management of nuclear resources.
As the global energy landscape evolves, the integration of such advanced modeling techniques could play a pivotal role in the development of digital twins for nuclear cores. These digital replicas can enhance predictive maintenance and operational planning, ensuring that nuclear power remains a competitive and safe energy source.
The research conducted by Guo Lin and his colleagues at Xi’an Jiaotong University represents a significant step forward in nuclear technology, emphasizing the importance of innovation in maintaining the safety and reliability of energy systems. For more information about their work, visit lead_author_affiliation.