Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan

Multi-objective Optimization of the Fast Neutron Source by Machine Learning

The design and optimization of nuclear systems can be a difficult task, often with prohibitively large design spaces, as well as both competing and complex objectives and constraints. When faced with such an optimization, the task of designing an algorithm for this optimization falls to engineers who must apply engineering knowledge and experience to reduce the scope of the optimization to a manageable size. When sufficient computational resources are available, unsupervised optimization can be used. The optimization of the Fast Neutron Source (FNS) at the University of Tennessee is presented as an example for the methodologies developed in this work. The FNS will be a platform for subcritical nuclear experiments that will reduce specific nuclear data uncertainties of next-generation reactor designs. It features a coupled fast-thermal design with interchangeable components around an experimental volume where a neutron spectrum, derived from a next-generation reactor design, will be produced. Two complete genetic algorithm optimizations of an FNS experiment targeting a sodium fast reactor neutron spectrum are presented. The first optimization is a standard implementation of a genetic algorithm. The second utilizes neural network based surrogate models to produce better FNS designs. In this second optimization, the surrogate models are trained during the execution of the algorithm and gradually learn to replace the expensive objective functions. The second optimization outperformed by increasing the total neutron flux 24\%, increased the maximum similarity of the neutron flux spectrum, as measured by representativity, from 0.978 to 0.995 and producing configurations which were more sensitive to material insertions by +124 pcm and -217 pcm. In addition to the genetic algorithm optimizations, a second optimization methodology using directly calculated derivatives is presented. The methods explored in this work show how complex nuclear systems can be optimized using both gradient informed and uninformed methods. These methods are augmented using both neural network surrogate models and directly calculated derivatives, which allow for better optimization outcomes. These methods are applied to the optimization of several variations of FNS experiments and are shown to produce a more robust suite of potential designs given similar computational resources

Nuclear plant diagnostics using neural networks with dynamic input selection

The work presented in this dissertation explores the design and development of a large scale nuclear power plant (NPP) fault diagnostic system based on artificial neural networks (ANNs). The viability of detecting a large number of transients in a NPP using ANNs is demonstrated. A new adviser design is subsequently presented where the diagnostic task is divided into component parts, and each part is solved by an individual ANN. This new design allows the expansion of the diagnostic capabilities of an existing adviser by modifying the existing ANNs and adding new ANNs to the adviser;This dissertation also presents an architecture optimization scheme called the dynamic input selection (DIS) scheme. DIS analyzes the training data for any problem and ranks the available input variables in order of their importance to the input-output relationship. Training is initiated with the most important input and one hidden node. As the network training progresses, input and hidden nodes are added as required until the networks have learned the problem. Any hidden or input nodes that were added during training but are unnecessary for subsequent recall are now removed from the network. The DIS scheme can be applied to any ANN learning paradigm;The DIS scheme is used to train the ANNs that form the NPP fault diagnostic adviser. DIS completely eliminates any guesswork related to architecture selection, thus decreasing the time taken to train each ANN. Each ANN uses only a small subset of the available input variables that is required to solve its particular task. This reduction in the dimensionality of the problem leads to a drastic reduction in training time;Data used in this work was collected during the simulation of transients on the operator training simulator at Duane Arnold Energy Center, a boiling water reactor nuclear power plant. An adviser was developed to detect and classify 30 distinct transients based on the simulation of 47 scenarios at different severities. This adviser was then expanded to detect and classify a total of 36 transients based on the simulation of 58 transient scenarios. The noise tolerant characteristics of the adviser are demonstrated

Nuclear Power

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the "system-thinking" approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners

Nuclear Power - System Simulations and Operation

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the system-thinking approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners

Machine Learning and Artificial Intelligence-Driven Multi-Scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications

The concept of small modular reactor has changed the outlook for tackling future energy crises. This new reactor technology is very promising considering its lower investment requirements, modularity, design simplicity, and enhanced safety features. The application of artificial intelligence-driven multi-scale modeling (neutronics, thermal hydraulics, fuel performance, etc.) incorporating Digital Twin and associated uncertainties in the research of small modular reactors is a recent concept. In this work, a comprehensive study is conducted on the multiscale modeling of accident-tolerant fuels. The application of these fuels in the light water-based small modular reactors is explored. This chapter also focuses on the application of machine learning and artificial intelligence in the design optimization, control, and monitoring of small modular reactors. Finally, a brief assessment of the research gap on the application of artificial intelligence to the development of high burnup composite accident-tolerant fuels is provided. Necessary actions to fulfill these gaps are also discussed

Surrogate Model Optimisation for PWR Fuel Management

Pressurised Water Reactor (PWR) fuel management is an operational problem for nuclear operators, requiring solutions on a regular basis throughout the life of the plant. A variety of conflicting factors and changing goals mean that fuel loading pattern design problems are multiobjective and, by design, have many input variables. This causes a combinatorial explosion, known as the ‘curse of dimensionality’, which makes these complex problems difficult to investigate. In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and simulated quantum annealing. The models are used to predict core parameters of reactors in simplified optimisation scenarios for a microcore, a small modular reactor, and a ‘standard’ PWR. The experiments with deep learning models show that competitive results can be obtained for training sets using a much lower number of simulations than direct optimisation. Fission matrix experiments demonstrate the method to predict core parameters for the first time, with interesting preliminary results. Novel experiments using simulated quantum annealing demonstrate the technique is able to generate loading patterns by following heuristic rules and is suitable for application to custom optimisation hardware. The principal contribution of this work is to show that surrogate model optimisation can be used to augment fuel loading pattern optimisation, generating competitive results and providing enormous computational cost reduction and thus permitting more investigation within a given computational budget. These methods can also make use of new computational hardware such as neural chips and quantum annealers. The promising methods developed in this thesis thus provide candidate implementations that can bring the benefits of these innovations to the sphere of nuclear engineering

Design of a deep learning surrogate model for the prediction of FHR design parameters

All rights reserved. Following previous work by Xing and Shwageraus, a large corpus of data has been collected for simulated AGR-style fuel assembly design in FHRs. The results exhibit a nonlinear system response, so a ‘deep’ multi-layer perceptron surrogate model is designed and tested for prediction of design parameters. This neuro-surrogate regression model could be useful for the fast optimization of the design parameters, for example in multiobjective optimization problems, due to the extremely fast evaluation time. Source code is made available for the audit and authentication of the scientific method

Development of triga mark ii research reactor core monitoring system using adaptive neuro-fuzzy inference system

Most of TRIGA research reactors has successfully converted the instrumentation and control (I&C) system from analog-based to digital-based. The digital I&C system is capable to monitor and control variables and parameters as well as to react to the design safety limits and conditions. In this study, the methodology on monitoring three of the core safety-related parameters was developed using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method at Reactor TRIGA PUSPATI (RTP). There were two parts involved which were parameter prediction and deviation calculation. Each parameter was generated with 12 -14 fuzzy inference system (FIS) models according to input-partitioning types. The generated model then underwent the training and testing phases to identify the good fit models which can be calculated based on three statistical calculations which are correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) to be further validated using a novel dataset. The second part of this study was carried out by constructing the algorithm to calculate the relative error between the predicted parameters and the design safety limit. For validation, the novel RTP dataset was used to select only one good fit model with an optimum input-partitioning method to represent the ANFIS model for parameter prediction in the monitoring system. In fuel temperature reactivity coefficient (FTC) validation, the results show that the Model 12 with fuzzy c-mean and the initial clusters centers of 3 had the lowest MAE and RMSE values which were 0.0110 and 0.1051 respectively however the R2 values are poor; R2 at 0.0795. For the fuel pin power (FPP) parameters at 12 fuel rods radial locations, Model 7 and Model 8 with subtractive clustering as the input-partitioning types and the optimal influenced radius values of 0.40 and 0.45 were selected to represent the FPP parameters at B04 and the rest of the fuel rods. The results show a good accuracy in predicting FPP parameters as the MAE and RMSE were calculated with the lowest values on each of fuel rod. The predicted FPP also shows a strong R2 values of 94% on the average. The validation of the power peaking factor (PPF) at the hot rods determined by the TRIGLAV code also demonstrates a good ANFIS model with 0.45 as the optimal influenced radius value in subtractive clustering input-partitioning types in Model 8. The model results in the lowest MAE and RSME with the R2 values at 0.1844, which is quite low. Although the calculated R2 for FTC and PPF parameters have weak R2 values, this statistical calculation was only used to present the relationship between the actual and prediction output and was not used as the primary model performance evaluation to conclude on the models’ accuracy and capability to predict the parameters. Thus, from these findings, the inclusion of FTC, FPP and PPF with specific optimal input-partitioning type on each ANFIS model can be implemented in the monitoring system for enhancing the reactor safety at TRIGA research reactors