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During the normal operation of cooling towers in nuclear power plants, water vapor with a certain temperature is discharged through the outlet of the cooling tower, which mixes with the surrounding air. A portion of this vapor condenses and forms visible plumes. This paper uses Computational fluid dynamics (CFD) numerical simulation software to simulate six nuclear power units and their supporting cooling towers at a nuclear power plant site, and studies the impact of plumes emitted from the six cooling towers under different wind directions on the radioactive diffusion of the nuclear power plant discharge. The research results indicate that when the arrangement direction of the cooling towers is consistent with the wind direction，the influence of the cooling towers on the environmental flow field extends to a distance of approximately 1 500 m in the downwind direction. When the cooling towers are located in the downwind direction of the reactor unit, the emission from the cooling towers increases the pollution diffusion factor within a 1 000 m range of the chimney. As the environmental wind speed increases, the impact of the cooling tower emissions on the diffusion of pollutants from the chimney decreases.

Nuclear plant modeling and control is an important subject in nuclear power engineering, giving the dynamic model from process mechanics and/or operational data as well as guaranteeing satisfactory transient and steady-state operational performance by well-designed plant control laws. With the fast development of small modular reactors (SMRs) and in the context of massive integration of intermittent renewables, it is required to operate the nuclear plants more reliably, efficiently, flexibly and smartly, motivating the recent exciting progress in nuclear plant modeling and control. In this paper, the main progress during the last several years in dynamical modeling and control of nuclear plants is reviewed. The requirement of nuclear plant operation to the subject of modeling and control is first given. By categorizing the results to the aspects of mechanism-based, data-based and hybrid modeling methods, the advances in dynamical modeling are then given, where the modeling of SMR plants, learning-based modeling and state-observers are typical hot topics. In addition, from the directions of intelligent control, nonlinear control, online control optimization and multimodular coordinated control, the advanced results in nuclear plant control methods are introduced, where the hot topics include fuzzy logic inference, neural-network control, reinforcement learning, sliding mode, feedback linearization, passivation and decoupling. Based upon the review of recent progress, the future directions in nuclear plant modeling and control are finally given. [ABSTRACT FROM AUTHOR]

The lack of plant-side energy storage analysis to support nuclear power plants (NPP), has setup this research endeavor to understand the characteristics and role of specific storage technologies and the integration to an NPP. The paper provides a qualitative review of a wide range of configurations for integrating the energy storage system (ESS) to an operating NPP with pressurized water reactor (PWR). The role of ESS technologies most suitable for large-scale storage are evaluated, including thermal energy storage, compressed gas energy storage, and liquid air energy storage. The methods of integration to the NPP steam cycle are introduced and categorized as electrical, mechanical, and thermal, with a review on developments in the integration of ESS with an operating PWR. By adopting simplified off-design modeling for the steam turbines and heat exchangers, the results show the performance of the PWR steam cycle changes with respect to steam bypass rate for thermal and mechanical storage integration options. Analysis of the integrated system characteristics of proposed concepts for three different ESS suggests that certain storage technologies could support steady operation of an NPP. After having reviewed what have been accomplished through the years, the research team presents a list of possible future works.

Emergency management in industrial plants is a fundamental issue to ensure the safety of operators. The emergency management analyses two fundamental aspects: the system reliability and the human reliability. System reliability is the capability of ensuring the functional properties within a variability of work conditions, considering the possible deviations due to unexpected events. However, system reliability is strongly related to the reliability of its weakest component. The complexity of the processes could generate incidental situations and the worker appears (human reliability) to be the weakest part of the whole system. The complexity of systems influences operator’s ability to take decisions during emergencies. The aim of the present research is to develop a new approach to evaluate human error probability (HEP), called Systematic Human Reliability Analysis (SHRA). The proposed approach considers internal and external factors that affect operator’s ability. The new approach is based on Nuclear Action Reliability Assessment (NARA), Simplified Plant Analysis Risk Human Reliability (SPAR-H) and on the Performance Shaping Factors (PSFs) relationship. The present paper analysed some shortcomings related to literature approaches, especially the limitations of the working time. We estimated HEP, after 8 hours (work standard) during emergency conditions. The correlations between the advantages of these three methodologies allows proposing a HEP analysis during accident scenarios emergencies. SHRA can be used to estimate human reliability during emergencies. SHRA has been applied in a nuclear accident scenario, considering 24 hours of working time. The SHRA results highlight the most important internal and external factors that affect operator’s ability.

Nuclear plant modeling and control is an important subject in nuclear power engineering, giving the dynamic model from process mechanics and/or operational data as well as guaranteeing satisfactory transient and steady-state operational performance by well-designed plant control laws. With the fast development of small modular reactors (SMRs) and in the context of massive integration of intermittent renewables, it is required to operate the nuclear plants more reliably, efficiently, flexibly and smartly, motivating the recent exciting progress in nuclear plant modeling and control. In this paper, the main progress during the last several years in dynamical modeling and control of nuclear plants is reviewed. The requirement of nuclear plant operation to the subject of modeling and control is first given. By categorizing the results to the aspects of mechanism-based, data-based and hybrid modeling methods, the advances in dynamical modeling are then given, where the modeling of SMR plants, learning-based modeling and state-observers are typical hot topics. In addition, from the directions of intelligent control, nonlinear control, online control optimization and multimodular coordinated control, the advanced results in nuclear plant control methods are introduced, where the hot topics include fuzzy logic inference, neural-network control, reinforcement learning, sliding mode, feedback linearization, passivation and decoupling. Based upon the review of recent progress, the future directions in nuclear plant modeling and control are finally given.

This work proposes adaptive control-based design strategies to control a pressurized water reactor (PWR) nuclear power plant (NPP). An L1-adaptive-based state-feedback control technique is proposed using the linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A robust L1-adaptive control technique is also proposed by combining the L1-adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives the strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in this article is defined by five inputs, five outputs, and 38 state variables. A linear model for controller design is obtained by linearizing the nonlinear NPP model at operating conditions. Various simulations are carried out on subsystems of the NPP to verify the effectiveness of the proposed scheme. Numerical and statistical measures are computed for quantitative analysis of the controllers’ performance. Several classical control design techniques are also implemented, and their performance is compared with the proposed adaptive control techniques. [ABSTRACT FROM AUTHOR]

• A model for evaluating in-plane shear strength of SC wall is proposed. • The upper bound theorem is used to develop the model. • The derived equation has a simple form. • The model has good computational accuracy. The steel-plate composite (SC) wall is considered an attractive structural type for the next generation nuclear power plants because of its efficient construction schedule, radiation shielding function, and excellent mechanical properties. During seismic action, the SC walls sustain large in-plane and out-of-plane loadings. So, it is essential for the evaluation of the in-plane and out-of-plane load carrying capacity of SC walls in design. Up to date, several equations that are developed based on elastic theory are proposed and adopted by relevant design codes. In the previous study by the authors, a lower bound equation was present for evaluation of the in-plane shear strength of SC elements based on the lower limit theorem of plastic limit theory. As a counterpart to that paper, an upper bound equation for evaluation of in-plane shear strength of SC elements based on the upper limit theorem of plastic limit theory was developed, and its minimum value solution which is the closest one to the exact solution was derived. Meanwhile, considering that concrete is not a perfectly plastic material, another equation is present to prevent the concrete between the steel faceplates from crushing prior to the steel yielding. A comparison between the results predicted by the proposed equation in this study and the test results of the SC panel specimens suggested the adequacy and accuracy of the proposed equation. In addition, comparisons of results evaluated by the proposed equations and the equations in codes JEAG 4618 and AISC N690 were also performed. [ABSTRACT FROM AUTHOR]

The causes and mechanism of metal degradation in damaged regions of heat-exchange pipes (coil) of the sodium–air heat exchanger of the secondary circuit in the IBR-2 research nuclear facility are found due to experimental investigations. The defects that do not lead to the initiation and development of cracks in contact with a sodium coolant are studied. As follows from the experimental results, it is necessary to pay attention to an involuntary change in the heat removal and to the local loads that cause plastic deformation of the pipe metal during erecting work. [ABSTRACT FROM AUTHOR]

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• A novel approach to nuclear condition monitoring using an R-vine copula is presented. • The developed framework can detect and diagnose the fault without any prior or detailed information. • Comparisons with other multivariate statistical tools highlight the superiority of the proposed method. • It provides new insight into multivariate probabilistic safety assessment in nuclear plants. Safety assessment of nuclear plants is pivotal for preventing an accident. The fault detection and diagnosis (FDD) module is crucial for ensuring plant safety since it is helpful in detecting any abnormal deviation from the normal operating condition and diagnosing the root cause(s) or faulty variables to restore the operation in the normal mode. The current work proposes a multivariate probabilistic framework using an R-vine copula for nuclear plants' FDD. Instead of feature extraction, it uses variables' dependence descriptions for monitoring. This tool does not require detailed fault information to provide diagnosis reports for distinct fault types – a common requirement for machine learning models. At the same time, unlike most multivariate statistical tools, it can capture data nonlinearity and non-Gaussianity and generate useful diagnosis results without the help of other tools. The proposed framework is tested and validated to four fault scenarios generated in the China Lead-based Reactor (CLEAR) platform. A detailed comparison with the other multivariate statistical tools: principal component analysis (PCA), independent component analysis (ICA), and kernel principal component analysis (KPCA), suggests that the R-vine model can provide a better monitoring mechanism. The current work will provide a new insight into multivariate probabilistic safety assessment in nuclear plants using vine copula models. [ABSTRACT FROM AUTHOR]

Probabilistic safety analysis (PSA) normally requires the development of a sophisticated computer model of the nuclear plant with a comprehensive reliability and safety study based on respective software. In 2013–2015, CRISS 5.3, a PSA software system, was developed and verified by Afrikantov OKBM as part of the effort, entitled New Generation Codes, under the Proryv (Breakthrough) project. The paper presents a review of the software tools used in the industry for the PSA of nuclear units and analyzes the capabilities of these tools. It also describes in brief the CRISS 5.3 software system intended to model and analyze safety systems and the nuclear plant as the whole as part of probabilistic safety analyses at all nuclear plant lifecycle stages. The paper presents results of the CRISS 5.3 code verification through the comparison of the analysis results obtained using the CRISS 5.3 system against analytical formulas and results of a qualitative and quantitative analysis based on certified nuclear plant PSA software tools.

All the nuclear facility structures must be analyzed for tornado loads. This analysis was per-formed when designing the New Safe Confinement over the ruined Chernobyl power unit (NSC ChNPP). The standard methods of tornado analysis could not be applied due to its large size and geometric shape. Therefore, it was necessary to develop new calculation methods. The paper provides detailed information on the performed calculations and describes the conservative assumptions made when there was not enough information. The purpose of this paper is to analyze the gained experience of performing such an analysis of this unique structure, which may be of some interest. The following two complex problems are considered: • establishing the design tornado parameters; • developing an engineering methodology for the tornado analysis. Moreover, questions are formulated that should be clarified when carrying out similar designs.