Yoon Joon Lee

Design of a fuzzy model predictive power controller for pressurized water reactors

In this paper, a fuzzy model predictive control method is applied to design an automatic controller for thermal power control in pressurized water reactors. The future reactor power is predicted by using the fuzzy model identified by a subtractive clustering method of a fast and robust algorithm. The objectives of the proposed fuzzy model predictive controller are to minimize both the difference between the predicted reactor power and the desired one, and the variation of the control rod positions. Also, the objectives are subject to maximum and minimum control rod positions and maximum control rod speed. The genetic algorithm that is useful to accomplish multiple objectives is used to optimize the fuzzy model predictive controller. A three-dimensional nuclear reactor analysis code is used to verify the proposed controller for a nuclear reactor. From results of numerical simulation to check the performance of the proposed controller at the 5%/min ramp increase or decrease of a desired load and its 10% step increase or decrease which are design requirements, it was found that the nuclear power level controlled by the proposed fuzzy model predictive controller could track the desired power level very well.

Design of an adaptive predictive controller for steam generators

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 used 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 performance.

A model predictive controller for load-following operation of PWR reactors

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.

Impedance imaging of two-phase flow field with mesh grouping method

Abstract There have been many attempts to measure the bubble distribution in two-phase flow fields and various techniques have been devised. However, the existing techniques require much improvement for imaging two-phase flow fields. In this study, the EIT (Electrical Impedance Tomography) technique is introduced for two-phase flow visualization. In the EIT, a static image reconstruction algorithm providing a higher spatial resolution is required. Using the conventional iterative static image reconstruction algorithms, however, the processing time increases rapidly with poor convergence characteristics as we try to obtain a higher spatial resolution. In order to overcome this problem, we propose an adaptive mesh grouping method utilizing the genetic algorithm and the fuzzy set theory. Computer simulations using the improved Newton–Raphson method with the proposed method show promising results indicating that we can significantly reduce the image reconstruction time without sacrificing spatial resolution.

A fast EIT image reconstruction method for the two-phase flow visualization

Abstract A preliminary investigation is introduced to demonstrate the feasible potentials of the application of the EIT (Electrical Impedance Tomography) to visualize the bubble distribution in two-phase flow field. We expect the required experimental apparatus for the EIT bubble distribution measurement to be rather simple thus much cheaper than the other bubble motion monitoring devices like LDV (Laser Doppler Velocimetry), PIV (Particle Image Velocimetry) and optical probes. At the present stage, however, the EIT visualization of the bubble distribution takes too long time to be implemented. In this paper, an adaptive mesh grouping method based on fuzzy-GA (Genetic Algorithm) is introduced to reduce the image reconstruction time significantly. Sample reconstructed images by the proposed method are presented with discussion for several ‘artificial’ bubble distributions.

Inferential Sensing and Monitoring for Feedwater Flowrate in Pressurized Water Reactors

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

Design of a Nuclear Reactor Controller Using a Model Predictive Control Method

A model predictive controller is designed to control thermal power in a nuclear reactor. The basic concept of the model predictive control is to solve an optimization problem for finite future time steps at current time, to implement only the first optimal control input among the solved control inputs, and to repeat the procedure at each subsequent instant. A controller design model used for designing the model predictive controller is estimated every time step by applying a recursive parameter estimation algorithm. A 3-dimensional nuclear reactor analysis code, MASTER that was developed by Korea Atomic Energy Research Institute (KAERI), was used to verify the proposed controller for a nuclear reactor. It was known that the nuclear power controlled by the proposed controller well tracks the desired power level and the desired axial power distribution.

Estimation of Axial DNBR Distribution at the Hot Pin Position of a Reactor Core Using Fuzzy Neural Networks

The pressurized water reactor (PWR) generally operates in the forced convection or nucleate boiling regime. However, if the fuel rod is operating at a high power density, the nucleate boiling that is characterized by extremely high heat transfer rates becomes film boiling with severely reduced heat transfer capability, which is called Departure from Nucleate Boiling (DNB). In this work, the axial DNB Ratio (DNBR) distribution at the hot pin position is predicted by the fuzzy neural networks using the measured signals of the reactor coolant system. The fuzzy neural network is a fuzzy inference system equipped with a training algorithm. The fuzzy neural network is trained by a hybrid method combined with a back-propagation algorithm and a least-squares algorithm. The proposed method is applied to the first cycle of the Yonggwang 3 nuclear power plant. The relative 2-sigma error averaged for 13 axial locations of the hot rod is 1.97%. The fuzzy neural networks estimate DNBRs more accurately at central parts that have relatively lower DNBR values which are more important in safety aspects. From these simulation results, it is known that this algorithm can provide reliable protection and monitoring information for the nuclear power plant operation and diagnosis by accurately predicting the DNBR each time step.

ESTIMATION OF THE POWER PEAKING FACTOR IN A NUCLEAR REACTOR USING SUPPORT VECTOR MACHINES AND UNCERTAINTY ANALYSIS

Knowing more about the Local Power Density (LPD) at the hottest part of a nuclear reactor core can provide more important information than knowledge of the LPD at any other position. The LPD at the hottest part needs to be estimated accurately in order to prevent the fuel rod from melting in a nuclear reactor. Support Vector Machines (SVMs) have successfully been applied in classification and regression problems. Therefore, in this paper, the power peaking factor, which is defined as the highest LPD to the average power density in a reactor core, was estimated by SVMs which use numerous measured signals of the reactor coolant system. The SVM models were developed by using a training data set and validated by an independent test data set. The SVM models’ uncertainty was analyzed by using 100 sampled training data sets and verification data sets. The prediction intervals were very small, which means that the predicted values were very accurate. The predicted values were then applied to the first fuel cycle of the Yonggwang Nuclear Power Plant Unit 3. The root mean squared error was approximately 0.15%, which is accurate enough for use in LPD monitoring and for core protection that uses LPD estimation.

Application of the thermal diode concept for the utilization of solar energy

This paper introduces a number of numerical and experimental investigations to harness the Suns energy using the thermal diode concept. Three different types of thermal diodes are constructed and tested following the numerical simulation to verify their actual performance and adaptability (feasibility). Of these, the first model is a simple thermal diode consisting of a number of rectangular loops whose direction of heat transfer can be easily manipulated either manually or automatically. The second model consists of a rectangular fluid reservoir, a vertical cavity and an inclined channel connecting the reservoir and cavity. This design is easy to construct and very effective in transporting and storing heat for space heating in winter. The concept employed in the second model could be further extended to build a solar water heating system with a tubeless flat plate collector. An antifreeze solution is circulated through the system by natural convection extracting heat from the solar energy to heat the water contained in a separate storage tank.