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The pressurized water reactor operates in the nucleate boiling regime. The transition from nucleate boiling to the film boiling accompanied by severe reduction of the heat transfer capability can result, however, in a boiling crisis that in the long run can cause fuel cladding melting. Therefore, it is very important to predict and monitor the departure from nucleate boiling (DNB) to prevent fuel clad melting and control the boiling crisis. In this study, the minimum DNB ratio (MDNBR) is predicted based on support vector regression (SVR) model using a number of measured signals from the reactor coolant system. SVR models are trained using a training data set and verified against test data set, which does not include training data. The SVR models have been applied to the first cycle of the Yonggwang 3 nuclear power plant. The estimation accuracy of the MDNBR was high enough to be used in DNB monitoring. Also, SVR model provides larger MDNBR values as compared to the existing core operation limit supervisory system, which allows greater operation margin. Index Terms--Departure from nucleate boiling ratio (DNBR), DNB monitoring, subtractive clustering (SC), support vector regression (SVR).

It is very difficult for operators to predict the progression of a loss of coolant accident (LOCA) because nuclear plant operators are provided with only partial information during the accident or they may have insufficient time to analyze the data despite being provided with considerable information. Therefore, its break location should be identified and the break size should be predicted accurately in order to provide the operators and technical support personnel with important and valuable information needed to successfully manage the accident. In this paper, support vector machines (SVMs) are used to identify the break location of a LOCA and predict the break size using the support vector classification (SVC) and support vector regression (SVR), which are well-known application areas of SVMs. The SVR models to predict the break size were optimized using a genetic algorithm. The inputs to the SVMs are the time-integrated values obtained by integrating the measurement signals in a short time interval after a reactor scram. The results showed that the proposed algorithm identified the break locations of LOCAs without fault and predicted the break size accurately. Index Terms--Genetic algorithm, loss of coolant accident (LOCA), support vector classification (SVC), support vector machine (SVM), support vector regression (SVR).

A support vector regression (SVR) model has been presented for reconstructing fuel rod powers from Canada deuterium uranium core calculations performed with a coarse-mesh finite difference diffusion approximation and single-assembly lattice calculations. The SVR is to nonlinearly map the original data into a higher dimensional feature space. Parameters related to the SVR are optimized by a genetic algorithm using the partial core calculation results of two 6 x 6 fuel bundle models (for training data). Verification has been conducted for two other partial core benchmark problems composed of 6 x 6 and 3 x 3 fuel bundles (for test data). The reconstructed fuel rod powers are compared with the reference solutions obtained with the detailed collision probability calculations using the HELIOS lattice analysis code. It is known from simulation results that the proposed rod power reconstruction algorithm is accurate, yielding the error due to the reconstruction scheme of less than 0.35 %. Index Terms--Canada deuterium uranium (CANDU) reactor, fuel rod power reconstruction, genetic algorithm, support vector regression (SVR).

The feedwater flowrate that is measured by Venturi flow meters in most pressurized water reactors can be overmeasured because of the fouling phenomena that make corrosion products accumulate in the Venturi meters. Therefore, in this paper, support vector machines combined with a sequential probability ratio test are used in order to accurately estimate online the feedwater flowrate, and also to monitor the status of the existing hardware sensors. Also, the data for training the support vector machines are selected by using a subtractive clustering scheme to select informative data from among all acquired data. The proposed inferential sensing and monitoring algorithm is verified by using the acquired real plant data of Yonggwang Nuclear Power Plant Unit 3. In the simulations, since the root mean squared error and the relative maximum error are so small and the proposed method early detects the degradation of an existing hardware sensor, it can be applied successfully to validate and monitor the existing hardware feedwater flow meters. Index Terms--Feedwater flowrate measurement, genetic algorithm, inferential sensing, sequential probability ratio test, subtractive clustering, support vector machines.

In this work, a model predictive control method combined with support vector regression and genetic optimization is applied to the design of the thermoelectric (TE) power control in the SP-100 space reactor. The future TE power is predicted by using the support vector regression. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted TE power and the desired power, and the variation of control drum angle that adjusts the control reactivity. Also, the objectives are constrained by maximum and minimum control drum angle and maximum drum angle variation speed. The genetic algorithm that is effective in accomplishing multiple objectives is used to optimize the model predictive controller. A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed controller. The results of numerical simulations to check the performance of the proposed controller show that the TE generator power level controlled by the proposed controller could track the target power level effectively, satisfying all control constraints. Index Terms--Genetic algorithm, model predictive control, reactor power control, SP-100 space reactor, support vector machines.

Venturi flow meters are currently used to measure the feedwater flowrate in most pressurized water reactors. The feedwater flowrate can be overmeasured because of the fouling phenomena that make corrosion products accumulate in the Venturi meters. Therefore, in this work, a smart software sensor using a fuzzy model is developed in order to accurately estimate online the feedwater flowrate, and also to monitor the status of the existing hardware sensors. A subtractive clustering method is used as the basis of a fast and robust algorithm for identifying the fuzzy model. The fuzzy model is optimized by a genetic algorithm combined with a least squares method. The proposed smart software sensor is verified by using the acquired real plant data of Yonggwang Nuclear Power Plant Unit 3. In the simulations, since the root mean squared error and the relative maximum error are so small and the proposed smart software sensor early detects the degradation of an existing hardware sensor, it can be applied successfully to validate and monitor the existing hardware feedwater flow meters. Index Terms--Feedwater measurement, fuzzy model, measurement monitoring, smart software sensor, subtractive clustering.

The basic concept of a model predictive control method is to solve on-line, at each time step, an optimization problem for a finite future interval and to implement only the first optimal control input as the current control input. It is a suitable control strategy for time-varying systems, in particular, because the parameter estimator identifies a controller design model recursively at each time step, and also the model predictive controller recalculates an optimal control input at each time step by using newly measured signals. The proposed controller is applied to the integrated power level and axial power distribution controls for a Korea Standard Nuclear Power Plant (KSNP). The power level and the axial shape index are controlled by two kinds of the five regulating control rod banks and the two part-strength control rod banks together with the automatic adjustment of boric acid concentration. The three-dimensional reactor analysis code, Multipurpose Analyzer for Static and Transient Effects of Reactor, which models the KSNP, is interfaced to the proposed controller to verify the proposed controller for controlling the reactor power level and the axial shape index. It is known from numerical simulations that the proposed controller exhibits very fast tracking responses. Index Terms--Load-following operation, model predictive control, parameter estimation.

It is very difficult for nuclear power plant operators to predict and identify the major severe accident scenarios following an initiating event by staring at temporal trends of important parameters. In this regard, a probabilistic neural network (PNN) that has been applied well to the classification problems is used in order to classify accidents into groups of initiating events such as loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR). Also, a fuzzy neural network (FNN) is designed to identify their major severe accident scenarios after the initiating events. The inputs to PNN and FNN are initial time-integrated values obtained by integrating measurement signals during a short time interval after reactor scram. An automatic structure constructor for the fuzzy neural network automatically selects the input variables from the time-integrated values of many measured signals, and optimizes the number of rules and its related parameters. In cases that an initiating event develops into a severe accident, this may happen when plant operators do not follow the appropriate accident management guidance or plant safety systems do not work, the proposed algorithm showed accurate classification of initiating events. Also, it well predicted timings for important occurrences during severe accident progression scenarios, which is very helpful to perform severe accident management. Index Terms--Event classification, neural network, scenario identification, severe accident.

The performance of fuzzy neural networks applied to sensor monitoring strongly depends on the selection of input signals. A large number of input signals may be involved to estimate an output signal for failure detection. However, as the number of input signals increases, the required training time increases exponentially and the uncertainty of the model increases significantly due to the irrelevant and/or the redundant inputs. In this paper, a fuzzy neural network with an optimal structure constructor has been successfully developed to achieve a reliable and efficient sensor monitoring system. A fuzzy neural network is used to estimate an output signal from the selected input signals. Correlation analysis and genetic algorithm (GA) are combined for automatic input selection. In addition, the optimal number of fuzzy rules is accomplished automatically by the GA integrated along with the automatic input selection. The status of sensor health is determined by applying sequential probability ratio test to the residuals between the measured signals and the estimated signals. The proposed sensor monitoring system has been validated by using a variety of sensor signals acquired from Yonggwang units 3 and 4 pressurized water reactors. Index Terms--Fuzzy neural network, genetic algorithm (GA), input selection, rule generation, sensor failure detection, sequential probability ratio test.

The water level control of a nuclear steam generator is very important to secure the sufficient cooling inventory for the nuclear reactor and, at the same time, to prevent the damage of turbine blades. The dynamics of steam generators is very different according to power levels and changes as time goes on. The generalized predictive control method is to solve an optimization problem for the finite future time steps at current time and to implement only the first control input among the solved optimal control inputs of several time steps. A recursive parameter estimation algorithm estimates on-line the mathematical model of steam generator every time step to generate the linear controller design model. In this work, by combining these generalized predictive control method and recursive parameter estimation algorithm, a new controller is designed to control the water level of nuclear steam generators. It is shown through application to a linear model and a nonlinear model of steam generators that the proposed controller has good performances. Index Terms--Generalized prediction control, parameter estimation and water level control, steam generator.

The nuclear steam generator is a nonminimum-phase system, which is caused by the swell and shrink effects. Since its inverse system has unstable dynamics, it is difficult to train the fuzzy controller via the conventional backpropagation of the system output errors. In this paper, a genetic algorithm is applied for the simultaneous design of membership functions and rule sets for a fuzzy control method of the steam generator water level. The genetic fuzzy controller for the steam generator is a fuzzy logic controller which is tuned offline by the genetic algorithm using the water level, feedwater flowrate, and steam flowrate signals of the steam generator. The symmetric Gaussian membership functions based on the flowrate and water level errors are applied. The proposed genetic fuzzy controller has a generalized and simplified rule base. The same genetic algorithm that is used to optimize the genetic fuzzy controller tunes a conventional proportional-integral (P-I) controller, and the performance of two controllers is compared. The genetic fuzzy controller shows good response that its swell and shrink phenomena are smaller and its response is faster than those of a well-tuned P-I controller are. Index Terms - Fuzzy controller, genetic algorithm, steam generator, water level control.

A neuro-fuzzy control algorithm is applied for the core power distribution in a pressurized water reactor. The inputs of the neural fuzzy system are composed of data from each region of the reactor core. Rule outputs consist of linear combinations of their inputs (first-order Sugeno-Takagi type). The consequent and antecedent parameters of the fuzzy rules are updated by the back-propagation method. The reactor model used for computer simulations is a two-point xenon oscillation model based on the nonlinear xenon and iodine balance equations and the one-group, one-dimensional neutron diffusion equation having nonlinear power reactivity feedback. The reactor core is axially divided into two regions, and each region has one input and one output and is coupled with the other region. The interaction between the regions of the reactor core is treated by a decoupling scheme. This proposed control method exhibits very fast response to a step or a ramp change of target axial offset without any residual flux oscillations between the upper and lower halves of the reactor core. Index Terms - Axial power distribution, neuro-fuzzy controller, xenon spatial oscillations.

A neuro-fuzzy inference system has been developed for reconstructing fuel pin powers from Canada deuterium uranium (CANDU) core calculations performed with a coarse-mesh finite difference diffusion approximation and single-assembly lattice calculations. The neuro-fuzzy inference system is trained by a genetic algorithm and a least-squares method using the partial core calculation results of two 6 x 6 fuel bundle models. Verification tests have been performed for two partial core benchmark problems composed of other 6 x 6 and 3 x 3 fuel bundles. The reconstructed pin powers are compared with the reference solutions obtained with the detailed collision probability calculations using the HELIOS lattice analysis code. The results indicate that the proposed reconstruction algorithm is accurate, yielding the error due to the reconstruction scheme of less than 0.5%. Index Terms--Canada deuterium uranium (CANDU), genetic algorithm, least-squares method, neuro-fuzzy logic, pin power reconstruction.

In this paper, proportional-integral-derivative (PID) control gains are automatically tuned by using a model predictive control (MPC) method. The MPC has received much attention as a powerful tool for the control of industrial process systems. An MPC-based PID controller can be derived from the second-order linear model of a process. The steam generator is usually described by the well-known fourth-order linear model, which consists of the mass capacity, reverse dynamics, and mechanical oscillation terms. But the important terms in this linear model are the mass capacity and reverse dynamics terms, both of which can be described by a second-order linear system. The proposed auto-tuned PID controller was applied to a linear model of steam generators. The parameters of a linear model for steam generators are very different according to the power levels. The PID gains of the proposed controller are tuned automatically. Also, the proposed controller showed fast water level tracking and small shrink and swell performance by changing only the input-weighting factor according to the power level for the water-level deviation and sudden steam flow disturbances supposed to investigate the tracking performance and swell and shrink characteristics. Index Terms--Model predictive control, nuclear steam generator, proportional-integral-derivative (PID) control, water-level control.