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The Integral Inherently Safe Light Water Reactor (I 2 S-LWR) is a novel pressurized water reactor (PWR) concept being developed by a multi-institutional team led by Georgia Institute of Technology, under the Department of Energy’s Nuclear Energy University Programs Integrated Research Projects (DOE NEUP IRP). The I 2 S-LWR aims to deliver approximately 1 GWe power output, comparable to other large scale LWRs, while simultaneously delivering improved safety over other Generation III+ LWRs. This reactor concept aims to achieve these goals by incorporating inherent safety features in the design. These features include an integral design of the primary coolant and control rod drive systems which eliminate the possibility of large break loss of coolant and control rod ejection accidents. It also features a fully passive decay heat removal system (DHRS) designed to providing indefinite core cooling as long as the core remains submerged in coolant. These features impact the design of the instrumentation system in a variety of ways. The University of Tennessee is developing instrumentation and control design concepts for the I 2 S-LWR. The development of instrumentation systems in the I 2 S-LWR, and the challenges and technology gaps to be addressed, are presented. [ABSTRACT FROM AUTHOR]

Dynamic fluctuations of nuclear plant sensors contain information about their response characteristics and bandwidth features. The random fluctuations can be characterized by using auto-regression (AR) time-series models. These discrete-time models are then utilized to estimate time-domain and frequency-domain signatures. Prior to developing these models, the sensor measurements are enhanced by filtering both low-frequency and high-frequency components using wavelet transforms. The use of wavelet transform for signal conditioning results in minimum distortion of the signal bandwidth, and thus provides an effective approach for data pre-processing. This integrated approach is applied to plant data from a pressurized water reactor (PWR). Univariate AR models were established for several pressure transmitter data, and used to estimate response time parameters of sensors and their frequency spectra. The results of this integrated approach demonstrate the improvement in the sensor signature estimation compared to the direct use of plant measurements. [ABSTRACT FROM PUBLISHER]

Fault diagnosis is an important area in nuclear power industry for effective and continuous operation of power plants. Fault diagnosis approaches depend critically on the sensors that measure important process variables. Allocation of these sensors determines the effectiveness of fault diagnostic methods. However, the emphasis of most approaches is primarily on the procedure to perform fault detection and isolation (FDI) given a set of sensors. Little attention has been given to actual allocation of the sensors for achieving efficient FDI performance. This paper presents a graph-based approach as a solution for optimization of sensor selection to ensure fault observability, as well as fault resolution to a maximum possible extent. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships among the measurements and to characterize by a data hyper-plane. Fault directions for the different fault scenarios are obtained using singular value decomposition of the prediction errors, and fault isolation is then accomplished from new projections on these fault directions. Results of the helical coil steam generator (HCSG) system of the International Reactor Innovative and Secure (IRIS) nuclear reactor demonstrate the proposed FDI approach with optimized sensor selection, and its future application to large industrial systems. [ABSTRACT FROM AUTHOR]

Simulation and control of an integral pressurized water reactor, with multiple units that operate in parallel and feed steam to a single turbine, is presented. Such a configuration requires mixing steam from two or more units in a steam header, with the steam from all the units maintained at the same operating conditions, with efficient operation of this configuration requiring advanced control strategy. The objective of this research is to evaluate and quantify the performance of a nuclear power plant comprised of two IRIS reactor units. The steam from two reactors flow into a common header connected to a single turbine, resulting in a steam-mixing control problem with respect to “load-following” scenarios, such as reduced consumption during weekends. To solve this problem, a single-unit IRIS Simulink® model, previously developed at the University of Tennessee, was modified to include a second unit, and was used to develop the control strategies under various operation transients. [ABSTRACT FROM AUTHOR]

Fault diagnosis is an important area in the nuclear industry for effective and continuous operation of power plants. All the approaches for fault diagnosis depend critically on the sensors that measure important process variables in the system. The locations of these sensors determine the effectiveness of the diagnostic methods. However, the emphasis of most fault diagnosis approaches is primarily on procedures to perform fault detection and isolation (FDI) given a set of sensors. Little attention has been given to the actual allocation of sensors for achieving efficient FDI performance. A graph-based approach, the directed graph (DG), is proposed in this paper as a solution for the optimization of sensor locations for efficient fault identification. The application of the DG modeling in deciding the locations of sensors based on the concepts of observability and fault resolution is introduced. A reliability maximization-based optimization framework for sensor placement from a fault diagnosis perspective is described. The helical coil steam generator unit of the International Reactor Innovative and Secure system is outlined to underscore the utility of the algorithms for large systems. [ABSTRACT FROM AUTHOR]

Abstract: In this work, a model predictive control method combined with fuzzy identification, 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 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 TE power and the desired power, and the variation of control drum angle that adjusts the control reactivity. Also, the objectives are subject to 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 fuzzy 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. [Copyright &y& Elsevier]

A robust Principal Component Analysis (PCA) model-based approach to Fault Detection and Isolation (FDI) was developed for the Helical Coil Steam Generator (HCSG) systems of the International Reactor Innovative and Secure (IRIS) nuclear reactor. The PCA model was developed using the data generated from a simulation of the system dynamics. Because all the operation modes can be excited through well-designed simulations, a PCA model developed from simulation data is more robust to changes in operation conditions than a PCA model developed from historical data collected during routine operation conditions. In order to deal with model uncertainty that exists in a simulation model, the mismatch between PCA model predictions and online plant measurements was analyzed to characterize the model uncertainty. Based on the assumption that model uncertainty has a structured property, a complete algorithm for the PCA-based FDI was derived such that the FDI results are robust to model uncertainty. When this new method was applied to the IRIS HCSG systems, the results showed that this approach could avoid false alarms and fault misdiagnosis due to changes in operation conditions and model certainty. The approach would overcome the limitations of traditional data-driven model based FDI when routine operational data do not contain sufficient information, and the limitations of physical model based FDI when these models are too complicated for a direct use in FDI design and contain model uncertainty. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A design approach to sensor fault detection and isolation (FDI) of helical coil steam generator (HCSG) systems of the International Reactor Innovation Secure (IRIS) reactor is presented. In the design phase, a physical model is first developed to provide a realistic simulation and generate data characterizing the system dynamics. A subspace identification technique is then used to extract a low-order linear state-space model from the data. Finally, a robust dynamic parity space approach is utilized to design residual generators for FDI. This design approach is able to achieve fault isolation following a predetermined logic without the need to use data during fault conditions, which is an unrealistic assumption of many FDI approaches studied for nuclear power plants. The results of the HCSG application show that the approach is robust to not only measurement and process noises but also operation condition changes and has the capability of correct FDI during reactor power transients and during the propagation of sensor faults in a control loop. [ABSTRACT FROM AUTHOR]

The integrity diagnosis of tubular structures is studied in this research using guided acoustic waves. A smart sensor array is deployed to activate and collect Lamb wave signals that propagate along metallic tubes. Several advanced techniques have been explored to extract representative features from acoustic time series. Among them, the Hilbert-Huang Transform (HHT) is a recently developed technique for nonlinear nonstationary signal processing. A moving window is introduced to generate the local peak information from transient series, and a zooming window algorithm is developed to localize the structural flaws. The extracted features using moving windows are utilized for classifying structural defects in brass tubes. An experimental piezo-transducer system has been developed for testing several types of flaws in brass tubes in air and in water, and to demonstrate the implementation of HHT for flaw detection, localization, and classification. The techniques are also shown to be effective under background/process noise. This approach has the potential of on-line integrity monitoring of tubes in steam generators, boilers, and heat exchangers, used in process and power plants. [ABSTRACT FROM AUTHOR]

Examine the effectiveness of the application of the neuro-fuzzy control algorithm in a pressurized water reactor. How the inputs of the neural fuzzy system are composed; Division of the reactor core; What the proposed control method exhibits.

This study is aimed at showing the applicability of mutual information, namely auto-mutual information function for condition monitoring in electrical motors, through age detection in accelerated motor aging. Vibration data collected in artificial induction motor experiment is used for verification of both the original auto-mutual information function algorithm and its hardware implementation in Verilog, produced from an initial version made with MATLAB HDL (Hardware Description Language) Coder. A conceptual model for industry and education based on a field programmable logic array development board is developed and demonstrated on the auto-mutual information function example while suggesting other applications as well. It has also been shown that attractor reconstruction for the vibration data cannot be straightforward. [ABSTRACT FROM AUTHOR]

Nuclear power plants (NPPs) require accurate measurement of mass flow rates. Advanced flowmeters have been widely applied in several current industries; however, the operating environment in NPPs is especially harsh because of high temperature, high radiation, and extremely corrosive conditions. Several of the advanced reactor designs, such as liquid sodium pool reactors and integral small modular reactors, do not have conventional primary piping systems. These designs require an alternative method to accurately measure primary flow. Cross-correlation function (CCF) flow estimation can estimate the flow velocity indirectly without any specific instruments for flow measurement. The target flow rate is derived by the delay time between two sensors located near each other along the flow direction. Temperature sensors are a common choice for this function because they are reliable, economical, and widely used in various industries. The delay time is inferred by applying the CCF to the signals collected from two or more sensors. CCF flow estimation can be performed in any structure of the flow region, not limited to pipes. One challenge for the CCF flow estimation is that the accuracy of the flow measurement is mainly determined by the inherent local process variation, which is small compared to the uncorrelated noise. To differentiate the process variations from the uncorrelated noise, this paper demonstrates periodic fluid injection of a different temperature before the sensors to amplify common process variation. The feasibility and accuracy of this method have been investigated through a physical flow loop experiment designed to verify the CCF flow estimation using fluid injection. Several parameters must be selected when designing the fluid injection CCF measurement system, such as the distance between the fluid injection site and the sensors, the injection period, and the injection flow rate. A series of tests was conducted to investigate whether these parameters were related to the accuracy of the CCF flow estimation and to identify appropriate values for these parameters for different flow regimes. The results show that the fluid injection method improves the flow measurement performance, and the appropriate design of flow injection and measurement geometry produces better flow characterization performance over a range of flow rates. [ABSTRACT FROM AUTHOR]

Abstract: The necessity of improvements in monitoring and diagnosis methods started to be of extreme relevance in the predictive maintenance field, establishing the reliability and readiness of system components as an achievable goal. Taking into account these reasons, this paper presents an approach for incipient fault detection of motor-operated valves (MOVs) using wavelet transforms. The technique applied in this paper is the wavelet transform analysis using wavelet toolbox, where the main goal is to obtain more detailed information contained in the measured data, identifying and characterizing the transient phenomena in the time and frequency domains, correlating them to failure situations in the incipient stage. The wavelet analysis has provided good results establishing a new qualitative methodology for monitoring and diagnostics of motor-operated valves. [Copyright &y& Elsevier]

• Open-source plant-level dynamic model of commercial-scale molten-salt reactor. • Primary side coupled with OTSG and a reheated-regenerative Rankine cycle BOP. Xenon poison effect accounted. • Frequency response with different boundary conditions on the model shows need for accounting secondary-side dynamics for I&C related studies. • Results suggest that self-regulating characteristics emanating from feedbacks can be leveraged to engineer control systems offering a great deal of autonomy. Ongoing research by the authors has led to a dynamic modeling approach that was verified against experimental data from the Molten-Salt Reactor Experiment (MSRE). These lumped-parameter models, characterizing the reactor dynamics, are nonlinear and represent changes in mass, energy, and temperature in all parts of the reactor plant. The reactivity feedback effects due to changes in temperature and Xe-135 concentration are taken into account. These models can be used to study both the time and frequency response to perturbations caused during normal operating conditions and during anomalies resulting from failure of certain sub-systems. A plant-level dynamic model of a representative molten salt reactor system developed based on the same methodology is presented here. A once-through steam generator providing superheated steam and reheated-regenerative Rankine cycle balance-of-plant system are coupled to the plant model. Results from simulation are presented for various transients and the resulting response of the plant is analyzed. Frequency response of the plant is also presented for various boundary conditions on the secondary side. Modeling results suggest that the inherent temperature-related feedbacks result in load-following characteristics that can be leveraged to engineer control systems offering a great deal of autonomy. [ABSTRACT FROM AUTHOR]

A nonlinear dynamic model for the two-fluid molten-salt breeder reactor (MSBR) system is presented. This work is partly inspired by a preliminary dynamic model of the concept studied at Oak Ridge National Laboratory (ORNL). The core heat transfer model has been revised to accurately reflect the design exemplified in ORNL-4528—the last report on the two-fluid design. A brief description of the reactor system and the effects of reactor poisons and a discussion of temperature feedback mechanisms are presented. This background information is followed by an overview of the modeling approach and a discussion of the revised lumped parametrization, along with detailed descriptions of the modeling methodology and model limitations. All equations and parameters used in the model are presented to aid in model reproduction and adaptation for other molten-salt reactor designs. Model stability is analyzed by observing the uncontrolled response to reactivity perturbations. Simulations illustrate stable behavior at all power levels investigated. Temperature-induced feedback effects lead to stable dynamics for both large and small reactivity transients. Stable and smooth changes in the various nodal temperatures are also observed. The frequency response of the system indicates no dynamics problems at all operating power levels and is consistent with the transient response. Characteristic features in the frequency response plots due to feedback effects are also discussed. Finally, the load-following capability of the MSBR system is studied for various ramp rates of the power demand in the final heat sink. The temperatures in all salt-containing parts of the system are observed to vary about an average during the load-following maneuver. It is observed that the MSBR system exhibits a self-regulating behavior, minimizing the need for external controller action for load-following operations. [ABSTRACT FROM AUTHOR]

Several molten-salt reactor (MSR) concepts are currently being developed by private vendors and national organizations. Molten-Salt Reactor Experiment (MSRE) is the only MSR that operated for several years, for which the performance was well documented in the literature. Experimental data from MSRE serve as the standard for developing models and tools for MSR analysis. MSRE underwent several design changes from conception to construction to operation. Model validation requires the use of parameters consistent with those that existed during the various measurements. The paper therefore presents a brief discussion of the parameters that evolved over time, and details of sources for all the parameters used in the modeling. This report is part of a series of papers on modeling MSR dynamics. The goal here is to validate the modeling approach by comparing simulation results with experimental data. For this purpose, two nonlinear dynamic models are developed. The first model uses a one-region one-dimensional representation of the core. The second reproduces the nine-region core model developed by Oak Ridge National Laboratory for MSRE studies. The dynamics are analyzed by simulating transients during normal power operation. The frequency response of the models is also evaluated. Both models show excellent agreement with experimental data for both U-235 and U-233 fuels. Some apparent discrepancies in experimental data are reported and their consequences discussed. The one-region model is modified to simulate reactivity excursions resulting from operational anomalies, and the results are presented. This modeling approach can be applied to other MSR systems provided valid system parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a multiple-input multiple-output (MIMO) model predictive control (MPC) design with constraints and Laguerre functions using the simplified model of the primary system (reactor core and intermediate heat exchanger (IHX)) of a prototypical sodium fast reactor (SFR). The control aims at tracking the reactor power, sodium tank temperature and the primary inlet plenum temperature when the reactor operates at 100% capacity by controlling the reactivity and the secondary sodium flow rate. For comparison criteria, this paper simulates the insertion of disturbances related to the reactivity and the sodium inlet temperature. The analysis includes the simulation of the insertion of disturbances in the reactivity and the sodium inlet temperature. The controller design was developed in MATLAB and Simulink, and was tested under a variety of reactivity disturbances (−1 and −5 cents) and a sodium inlet temperature disturbance of +10 °F. In each case studied, the MPC tracked the outputs effectively. The proposed MPC-SFR obtained a faster and more robust disturbance rejection regarding insertion of reactivity and sodium inlet temperature when compared with SFR controlled by digital PI controllers (PI-SFR). Finally, the controller was embedded in a Field Programmable Gate Array (FPGA) device in order to demonstrate the FPGA-in-the-loop (FIL) feature and to compare it with the results using MATLAB-Simulink. • An MPC with constraints and Laguerre functions was designed for an SFR. • The MPC-SFR was embedded in FPGA for hardware-in-loop demonstration. • The sodium flow rate and reactivity control the reactor temperature and power. • The MPC rejects external disturbances in reactivity and sodium inlet temperature. [ABSTRACT FROM AUTHOR]

This research describes the monitoring of the fundamental spectral features of stator insulation damage through accelerated aging studies for induction motors with a power rating of 5 HP. In order to accomplish this goal, even-harmonic values of the line frequency defined between the 4th and the 16th harmonics, which are computed by the coherence approach between the stator currents and vibration signals, are determined as indicators of stator insulation damage. After this determination, a neuro-detector based on the auto-associative neural structure is trained in the frequency domain. This uses coherence variations and even-harmonic values as indicators of the insulation damage of an induction motor by observing the changes in the errors (residuals) generated by the neural net. [ABSTRACT FROM AUTHOR]

This paper presents a reactor-monitoring algorithm using the group method of data handling (GMDH) that creates nonlinear algebraic models for system characterization. The monitoring system was applied to the IEA-R1 experimental reactor at the Instituto de Pesquisas Energéticas e Nucleares (IPEN). The IEA-R1 is a 5-MW pool-type research reactor that uses light water as coolant and moderator and graphite as reflector. The GMDH provides a general framework for characterizing the relationships among a set of state variables of a process system and is used for generating estimates of critical variables in an optimal data-driven model form. The monitoring system developed in this work was used to predict the IEA-R1 reactor environment, using nuclear power, rod position, and coolant temperatures, by combining two variables at a time. The results obtained using the GMDH models agreed very well with the dose rate measurements, with prediction errors of less than 5%. The error was minimal when the dose rate prediction was made using reactor power and coolant temperature. [ABSTRACT FROM AUTHOR]

Presents a study that developed a fuzzy neural network with an optimal structure constructor to achieve a reliable and efficient sensor monitoring system. Description of a fuzzy neural network with an automatic structure contractor; Information failure decision; Applications of real nuclear plant sensor signals.