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• Timely and correct identification of NPP transients could either prevent an accident or mitigate the consequences of an accident. • A hybrid deep learning technique is proposed for the online identification of nuclear power plant transients. • A CNN-LSTM network was used for the transient identification, which transformed the input matrices into vectors using a CNN and then LSTM layers predicted the probability of each transient by sequence processing. • The results were shown that the proposed technique performs better than common deep learning techniques in terms of accuracy, identification time, and computational cost. Identification of NPP transients plays an important role in the prevention of accidents and mitigation of their consequences. NPP parameters may follow different patterns during each transient. So the transients can be identified by monitoring the operating parameters. It has been shown in several studies that data-driven methods, especially deep learning approaches, have a desirable performance in NPP transient identification. A hybrid deep learning technique is proposed in the present paper, in which transient identification is done using a CNN-LSTM neural network. The training data set is taken from a VVER-1000 full-scope simulator and the most important operating parameters are determined by feature selection techniques. According to the results, the proposed technique has identified the NPP transients in a short time, with high accuracy, and with a reasonable computational cost. The effective performance of the technique makes it possible to use it as a practical tool for online transient identification. [ABSTRACT FROM AUTHOR]

• To determine the most important input parameters for NPP transient identification, the present paper has proposed a hybrid feature selection method. • Several filter methods are used to generate appropriate feature subsets by calculating the score of each feature. • A Long Short-Term Memory (LSTM) deep neural network is selected as the classifier to determine the best feature subset. • The Neighbourhood Components Analysis filter method has selected the best subset of features. • The effect of the number of used features on the accuracy of the model was investigated. Accidents that occur at NPPs must be correctly identified so quickly that mitigation actions can be taken in a timely manner. Depending on the type of transient, the operating parameters follow different patterns and it might be possible to identify the transient by monitoring these parameters. Due to the large number of parameters of an NPP, it is necessary to determine the parameters that play a vital role in transient identification. Data-driven methods have shown effective performance for NPP transient identification. To determine the most important input parameters for NPP transient identification, the present paper has utilized a hybrid feature selection method, in which feature subsets are created using several filter methods and then the best feature subset is determined by comparing the training results of a deep Long Short-Term Memory network. According to the results, the Neighbourhood Components Analysis method has selected the best subset of features. [ABSTRACT FROM AUTHOR]

In normal operation conditions of nuclear power plants, the distribution of primary coolant between fuel channels would be considered almost uniform. When different number of Reactor Circulation Pumps (RCPs) are switched off, known as an abnormal condition, this uniform distribution is disturbed and different conditions occur for each channel depending on its position in the core. In this research, the normal and abnormal condition (with one or two tripped RCPs) for a VVER-1000/446 is investigated. For evaluation of the core neutronic calculations and thermal power distribution, USNRC's PARCS system code is employed. Then a thermal-hydraulics module was developed for performing the T/H calculation of the core zone. The input velocity of each channel in abnormal condition was calculated based on developed CFD model in downcomer and lower plenum of Reactor Pressure Vessel (RPV) by ANSYS-CFX. The results show that, in normal operation, the hot channel is related to the central fuel assembly of the reactor core with the highest relative power equal to 1.29 and total power of 23.74 MW. In this case, the minimum inlet velocity, the maximum coolant outlet velocity, and the maximum fuel temperature are 5.6 (m/s), 330.96 (°C), and 1345.8 (°C), respectively. In the cases of operation with one and two tripped RCPs, the hot channel is related to the fuel assemblies with the lowest inlet velocities. The lowest velocities are 0.32 m/s, 0.24 m/s, and 0.22 m/s respectively for the condition with one tripped pump, two tripped pumps placed oppositely, and two tripped pumps placed contiguously. The hot channel numbers in these cases are 158, 102, and 90, respectively. In these channel, the condition of outlet flow would be superheated, but the fuel temperature (1006.3, 1050.9, and 987.9) do not reach the maximum allowable margin. The study confirms the necessity of the coolant distribution consideration in OLCs as well as events that may disturb the symmetry of the coolant flow. It also showed that the lateral fuel assemblies are more at risk in this situation because of a significant reduction in coolant flow. Likewise, the investigations proved the safe continuation of the operation in PLOFA conditions with the preventive algorithm of the emergency protection system and without any need for immediate mitigation actions or operator intervention. [Display omitted] [ABSTRACT FROM AUTHOR]

Highlights: [•] Lagrange method is proposed for on-line reactivity calculation in nuclear reactors. [•] The need for nuclear power history or the Laplace transform is vanished. [•] The three- and five-point formulas are presented and examined in different benchmark cases. [•] Computational time-steps of up to 1s lead to highly reliable reactivity calculations. [•] The main advantage of the proposed approach is its stability and convergence in large time-step calculations. [Copyright &y& Elsevier]

• The malfunction of boron injection system is one transients affecting the characteristics of cooling fluid. • A realistic approach based on the CFD and nodal expansion method is employed for simulation of this accident. • The utilization of CFD method depicted the boron heterogeneous distribution effects. • Due to the reduction of the absorber in a part of the reactor core DNBR is close to the critical value of 1. Nuclear power, as a low-carbon energy source, provides the steady and reliable electricity. In this power source, safe operation plays a significant role from the point of view of design and economic cost. While nuclear power plants are designed to be safe in their operation and safe in the event of any malfunction or accident, some accidents with considerable occurrence frequency should be considered to provide related safety system and operation. The malfunction of boron injection system is one of this accident affecting the chemical and neutronic characteristics of cooling fluid. The aim of this study is the simulation and analysis of the boron dilution accident as one of Design Basis Accident, by considering the heterogeneous distribution of boron. For this purpose, CFD methods were used for exact evaluation of the boron distribution in each fuel assembly. Also, cross-section correction module and core calculation module are respectively developed by a cell calculation code and nodal expansion method. These modules are employed to apply absorber effects including boron and control banks and calculate 3D relative power. The results reveal the effect of heterogeneous distribution of boron in reactor pressure vessel and non-uniform chemical characteristics of coolant across fuel channels. [ABSTRACT FROM AUTHOR]

Soft Computing (SC) is defined as a group of computational techniques that solve complex problems independent of mathematical models. SC techniques including artificial neural networks (ANNs), fuzzy systems (FSs), evolutionary algorithms (EAs), etc., can solve problems that either cannot be solved by the analytical/conventional methods or require a long computation time. Due to their features, SC techniques are nowadays widely used in scientific and industrial researches. SC techniques have also been included in many types of research related to nuclear power plants (NPPs). In this paper, the applications of SC techniques in NPPs, according to published articles, are presented. The applications include fault detection and diagnosis, reactor power control, solving flow and heat transfer problems, optimization of NPP operation and design, High-performance accident management support tools, NPP operating parameters forecasting, and reliability/safety assessment of NPP. Other miscellaneous applications of SC that are used indirectly in NPP have also been mentioned. In addition, the advantages/disadvantages of SC techniques are compared with conventional computational methods. The main advantages of SC techniques are high execution speed, model independence, and high efficiency; while the main disadvantage is the need for access to reliable data sets which should be provided by experiment or modeling. [ABSTRACT FROM AUTHOR]

In this paper an approach based on system identification is used for fault detection in a nuclear reactor. A continuous-time Extended Kalman Filter (EKF) is presented, which allows the parameters of the nonlinear system to be estimated. Also a fault tolerant control system is designed for the nuclear reactor during power changes operation. The proposed controller is an adaptive critic-based neuro-fuzzy controller. Performance of the controller in terms of transient response and robustness against failures is very good and considerable. [ABSTRACT FROM AUTHOR]

• A neutronic/thermal–hydraulic simulator has been developed based on coarse mesh methods to calculate AO for a Small Modular Reactor. • Average Current Nodal Expansion Method (ACNEM) and Single Heated Channel (SHC) method are considered as the neutronic and the TH simulator. • Macroscopic cross-sections were generated by cell calculation module corresponding to reactor operation conditions. • The results demonstrate that the AO variation remains in a safe and standard operating region for SMART reactor. Axial-Offset (AO), as an important operational parameter, should be monitored in the load-following procedure due to the safety limits related to xenon oscillation and axial peaking factor in a nuclear power plant. In this study, to calculate AO for a Small Modular Reactor, a neutronic/thermal–hydraulic simulator has been developed based on coarse mesh methods. In this simulator, the average current nodal expansion method and the single heated channel approach have been used for neutronic and thermal–hydraulic analysis, respectively. The developed simulator is verified by the IAEA-3D model and thermal–hydraulic benchmark model. As the iPWR small modular reactor case study, SMART reactor has been simulated, and the neutronic and thermal–hydraulic parameters have been calculated. For investigation of AO behavior during power changes, the regulating bank repositioning approach is employed, and the reactor core characteristics are estimated by a developed simulator. The results demonstrate that the AO variation remains in a safe and standard operating region for SMART reactor. [ABSTRACT FROM AUTHOR]