Annals of Nuclear Energy最新文献

Burnup analysis in nuclear reactors requires iterative computation of neutron transport and fuel depletion, which is computationally intensive, particularly for large-scale scenarios. This study introduces an innovative approach leveraging the Dynamic Mode Decomposition (DMD) algorithm to predict the temporal evolution of nuclide densities. By identifying and utilizing the DMD modes and eigenvalues from snapshots of nuclide density, this method aims to alleviate the computational demands of the coupled transport and burnup calculations. Firstly, the methodology selects the key reactivity-contributing nuclides to evaluate the correlation between the complexity of the reduced-order model and the precision of predictions. Subsequently, an optimized reduced-order model is employed for forecasting nuclide densities in a pin-cell. In most cases, DMD predicts more accurately than traditional quadratic extrapolation methods. Moreover, the DMD algorithm demonstrates commendable accuracy in predicting the nuclide density distribution within a PWR fuel assembly, suggesting its promising potential for reactor burnup analysis applications.

Cross sections of the ²⁷Al(n,$\alpha$)²⁴Na, ⁹⁶Zr(n,2n)⁹⁵Zr, ¹¹⁵In(n,p)^115gCd, ¹¹⁵In(n, 2n)^114mIn and ¹⁹⁷Au(n,2n)^196/196m2Au reactions induced by D-T neutrons are presented with the activation method and off-line $\gamma$-ray spectrometry technique. Uncertainty propagation and correlation of the cross sections were estimated using covariance analysis. It shows that, our results are consistent with most of the previous literature data of EXFOR library. Experimental values, including previous literature data, are compared with evaluated nuclear data of the CENDL-3.2, BROND-3.1, ENDF/B-VIII.0, IRDFF-II, JENDL-5 and JEFF-3.3 libraries. Besides, these excitation functions were theoretically calculated by using the TALYS-2.0 code up to the neutron energy of 20 MeV. It shows that significant discrepancies were found between experiment data and those of calculated results and evaluated data.

During the operational lifespan of uranium dioxide (UO₂) fuel, the emergence of a specific process termed recrystallization may transpire. The influence of recrystallization on the thermal conductivity of the fuel holds paramount significance, bearing direct implications for both safety and economic considerations. In the current investigation, a phase-field model incorporating an explicit nucleation model for recrystallized grains was formulated to study the formation and growth of recrystallized grains within polycrystalline UO₂. The simulations conducted in this study revealed that the kinetics of recrystallization adhered to the empirical equation, and the observed variation in grain size during recrystallization exhibited concordance with experimental data. To elucidate the variation in thermal conductivity during recrystallization, a thermal conductivity model based on the microstructure generated through phase-field simulations was employed. The relationship between grain boundary (GB) thermal resistance and phase-field simulation parameters has been determined through empirical formulas. The simulated values of thermal conductivity during recrystallization demonstrated a commendable agreement with empirical functions. By comparing the computational results of thermal conductivity with or without recrystallization, it is proven that recrystallization is beneficial to the effective thermal conductivity because the increase in thermal conductivity due to the elimination of defects by recrystallization exceeds the decrease in thermal conductivity due to the introduction of large area GBs.

A multi-physics coupling system has been developed in this work based on the MOOSE framework for the steady-state analysis of XAPR (Xi’an Pulse Reactor). It consists of three physical models including neutronics, thermo-mechanics model of fuel element and fluid flow model. These models have been coupled by Picard iteration through the MultiApp and Transfer system based on MOOSE framework. The core state parameters of XAPR under steady-state operation condition are analyzed and the 3-dimensional space-dependent power density, fuel element temperature as well as the coolant temperature are provided by the multi-physics model. The multi-physics model successfully reproduced the experimental results of the monitored fuel element temperature in XAPR under different power level, and the deviation was less than 20 K. Future work would be to study the dynamics behavior of XAPR to further validate the multi-physics model and simulate other advanced micro reactors.

Decay heat measurements on spent nuclear fuel (SNF) provide a basis for code validation. Their applicability domain (AD) and gaps, which are the focus of this study, are not commonly discussed in the literature. The analyzed validation data are based on measurements at the Clab facility and on calculations using the Polaris and ORIGEN codes of the SCALE code system. Bias-predicting machine learning (ML) models are applied: random forest and weighted k-nearest neighbors. The models weigh the similarity between the cases, expressed using correlations. The learning curves are studied by examining the prediction error versus the sample size and the similarity coefficient. The obtained error reduction at higher similarity coefficients supports the argument that the similarity or correlation is informative. However, a marginal error reduction is expected from increasing the validation data size from its current status. Following this, a validation AD is proposed as a range of SNF characteristics within which the validation data and the ML models are observed and tested. Within the AD, different levels of error, i.e., safety margins and conservatism, were evaluated. Beyond the AD, validation gaps exist. Examination of light-water reactor SNF applications indicates that the validation coverage is absent in both MOX fuel and short cooling, diminishes rapidly at higher burnup for low-enrichment fuel, and extends with burnup for high-enrichment cases. Additional measurements are justified to reduce conservatism or achieve validation coverage in applications. A case study of typical UO₂ and MOX SNF applications is analyzed. It is shown that a few tens of optimally selected measurements from both SNF types are necessary to complete validation coverage in numerous applications.

The coupled code LETHAC-Oxide is developed for analysis of thermal–hydraulic and safety characteristics in lead-cooled fast reactors, considering the impact of oxidation corrosion during prolonged operation. Based on experimental data from CORRIDA, Tsu-2M, and SM-1 facility, the oxidation model is well verified. The reactor concepts LESMOR and BREST-OD-300 are modeled, and the results show that the oxide layer significantly influences heat transfer, particularly at higher temperatures. A comparison between LESMOR and BREST-OD-300 demonstrates that a 95 °C difference in average system temperature will cause 14 times increase in oxide layer thickness and 7 times decrease in steam generator heat exchange capability. Conclusively, LESMOR forms a protective oxide film after a refueling cycle, offering structural material protection without major heat transfer impact. In contrast, BREST-OD-300 shows a substantial increase in cladding temperature and decrease in heat transfer capacity. This result underscores the necessity of oxygen control technology to mitigate risks associated with oxidation corrosion, providing valuable insights for optimal reactor performance and safety.

Steam generator (SG) replacements in South Korea began with the Kori No. 1 unit in 1998 due to performance degradation. Currently, 20 steam generators have been replaced in total. While additional decommissioning of dozens of steam generator will be required soon due to life-expiration of several nuclear power plants, there has been no actual dismantling performance of steam generators yet, and the replaced decommissioned steam generators are currently stored in intermediate storage facilities. To minimize waste volume and facilitate site reuse, it’s necessary to proactively dismantle steam generators. These components are less radioactively contaminated and easier to dismantle compared to primary equipment like reactors. Additionally, securing related dismantling technology is essential for managing future replacements or equipment that has been stored. Establishing a process scenario about where and how the steam generator will be safely dismantled is important. It is necessary to analyze the advantages and disadvantages of each scenario to study the timing, location, and method of dismantling, and to develop an optimal process scenario through analysis of worker radiation exposure and dismantling costs. For this purpose, simulations were conducted on the radiation dose to workers according to the timing and method of dismantling, using 3D dismantling simulation software developed by Cyclife Digital Solutions, a subsidiary of French EDF, and the results were reviewed by mathematically modeling and analyzing the radiation doses exposed to workers over the years using an exponential decay model.

Considerable research has been undertaken to explore the use of nanofluids for augmenting the critical heat flux in the in-vessel retention (IVR) strategy deployed in reactors, demonstrating significant improvements in CHF. However, it is important to consider the potential bias in previous studies on surface CHF due to the oxidation of low carbon steel, which is commonly used in reactor vessels, in both air and water under real-life conditions. This study represents the initial investigation into the oxidation behavior of low carbon steel in an air environment, followed by subsequent boiling in water. The results indicate that when the mild steel surface is pre-oxidized in air, the CHF value in deionized water decreases. However, this effect is not readily apparent in nanofluids. Consequently, it suggests that CHF under real operational conditions could be lower than anticipated. Additionally, nanofluids significantly increase the CHF of surface, however, the enhancement of CHF for oxidized surfaces in water is not as pronounced, a point which has never been mentioned by researchers. The mechanisms of surface oxidation and nanofluid-induced CHF enhancement are explained. Consequently, this paper provides important reference value for studying the application of nanofluids in IVR accidents.

This research presents advanced methods of data processing as tools applicable in nuclear engineering. Three methods-autoencoder, random forest and multilayer perceptron were considered for the testing. The multi-layer perceptron (MLP) method preserved best the structure of the data. A better generative adversarial network (GAN) based on the Wasserstein distance was implemented for data generation, which overcomes the issues of gradient vanishing and mode collapse prevailing in common GANs. While examining the generated data, advanced statistical and machine learning techniques were applied to minutely compare the generated and original data. Data forecasting applied a combined model of MLP, convolutional neural network (CNN), and recurrent neural networks (RNN). With limited-memory broyden-fletcher-goldfarb-shanno (LBFGS) optimization algorithm being combined with bayesian optimization, there was significant improved prediction of core thermal hydraulic parameters. Meanwhile, this research provides important techniques to deal with challenges of nuclear engineering data which further impacts the field of nuclear engineering.