Scientific computation is pivotal throughout the nuclear industry's technical framework, encompassing everything from the creation of nuclear databases to the design, analysis, validation, and operation of nuclear power engineering, as well as the reprocessing of nuclear fuel and the decommissioning of reactors. Historically, the scientific computing paradigm in the industrial field is mainly based on statistical methods for modeling experimental measurement data, as well as numerical computing methods represented by solving differential/integral equations. As artificial intelligence (AI) technology advances, leveraging AI for scientific computation is emerging as a novel paradigm. This paper introduces the basic principles and main features of this emerging technology field, focusing on the characteristics of the nuclear industry. It summarizes the current research work and analyzes the advantages and disadvantages of artificial intelligence scientific computing methods compared to traditional methods. The paper concludes with a prospective look at the future development trends of intelligent computation in the nuclear field, along with potential application scenarios, offering insights to foster the evolution of AI in scientific computation within the nuclear industry.

The advantages of artificial intelligence (AI) algorithms in rapid prediction, self-learning, and strong generalizability have been applied to address the complexities of thermal-hydraulic phenomena and mechanisms in nuclear reactors. These applications include predictions of thermal-hydraulic parameters, optimization of thermal safety analysis codes, and enhancements in computational fluid dynamics (CFD) efficiency. This paper reviews the current state of research on AI algorithms in predicting thermal-hydraulic parameters such as flow regimes, boiling heat transfer, and critical flow. To address challenges such as unknown mechanisms and limited prediction ranges under extreme operating conditions, this study leverages the nonlinear rapid prediction capabilities of AI to expand the scope and accuracy of analyses. For thermal analysis codes constrained by parameter models, the self-learning, adaptive, and highly generalizable features of AI are utilized to improve the identification and prediction of complex phenomenon parameters through model calibration and data assimilation techniques. By employing model reduction and fast prediction methods, AI enhances the computational efficiency and the multidimensional reconstruction of complex thermal-hydraulic physical fields. Furthermore, the study highlights the future prospects of AI algorithms in accurately predicting the full lifecycle performance of key components in large-scale reactor systems, accelerating design iterations for advanced reactors such as liquid-metal fast reactors, and optimizing cross-scale, multiphysics interactions in a more efficient manner.

The development of digital twin of nuclear reactor has the potential to enhance the safety and economic efficiency of nuclear power plants by achieving a cyber-physical fusion, while the key challenge of cyber-physical fusion is data fusion. Therefore, this paper focuses on the field of digital twin of nuclear reactor, starting from the definition of data fusion, fusion objects, fusion levels, fusion methods, and the relationship between digital twins and data fusion. Subsequently, the application and research status of data fusion methods in the entire life cycle of digital twin in nuclear reactor are discussed from eight perspectives: the construction of digital twin model of nuclear reactor, the optimization issues in the design and construction of nuclear reactor, the inversion and reconstruction of nuclear reactor operating parameters, the prediction of nuclear reactor operating parameters and remaining service life, the calibration of nuclear reactor operating parameters, the feedback and control of nuclear reactor operation, the fault detection, identification and diagnosis of nuclear reactor, and the data fusion of other aspects of digital twin of nuclear reactor. In conclusion, the challenges existing in current research has been identified from the aspects of data and fusion methods, providing references for addressing key data fusion issues in the future development of digital twin for nuclear reactor.

For statistical learning algorithms that are related to various core physical calculations with nuclear data as the analysis input, providing stochastic disturbance samples that are consistent with the known statistical moment information and physical constraints of nuclear data is fundamental. Reasonably perturbed nuclear data samples are one of the important factors to ensure the prediction accuracy of data-driven artificial intelligence models such as core physical response feature extraction and reduced-order modeling. Selecting the probability density distribution that can meet the physical constraints of nuclear data itself is the key to ensure the rationality of the above stochastic sampling of nuclear data. This work focuses on two types of physical constraints that are commonly seen in the evaluated nuclear data library, namely, non-negativity constraints (e.g. fission product yield, nuclear reaction cross section) and normalization constraints (e.g. decay branch ratio), studies the corresponding probability density distribution selection methods and provides the corresponding sampling algorithms. Combined with the uncertainty information of nuclear data provided in the evaluated nuclear data library, this work compares the stochastic sampling effects of nuclear data with different probability density distributions, and gives some suggestions on the selection of probability density distributions.

Amid the global surge in scientific intelligence research and applications, artificial intelligence (AI) technology has been applied in various aspects of reactor neutron analysis to enhance its intelligence, precision and efficiency. This paper provides a comprehensive review of the research progress in the application of AI technologies within reactor neutron analysis, aiming to provide insights for advancing the digitalization of reactors and supporting future developments in this field. We first introduce AI methods' basic classifications and characteristics, and then investigate the four key aspects of neutron analysis: nuclear data evaluation, problem modeling, numerical solution of equations, and transport result application. Also critical technologies are reviewed and summarized for each aspect. Finally, the paper discusses challenges in AI-based neutron analysis, particularly in model, data, and application security. In response to these challenges, the paper proposes research recommendations.

In order to solve the problem of numerical heat transfer computation for extremely low-Prandtl-number fluids and to improve the computational accuracy of the numerical simulation of liquid lead-bismuth eutectic (LBE), a four-parameter SST k-ω-k_θ-ε_θ model for the Reynolds stress and turbulent heat flux is developed under the framework of OpenFOAM. Based on benchmark flow and heat transfer experiments of LBE in a vertical pipe and wire-wrapped 19-rod bundle, a comparison and thermal-hydraulic analysis were conducted against the turbulent Prandtl number (Pr_t) model using relevant empirical Nusselt number and friction factor correlations. The results show that the temperature predicted by the four-parameter SST k-ω-k_θ-ε_θ model agrees well with the experimental data, and the heat transfer prediction performance is better than that of the Pr_t model. The model is suitable for the numerical computation of LBE flow and heat transfer.