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moshkbar-bakhshayesh--khalil
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Development of a Validation and Calibration Algorithm for Thermohydraulic Sensors of Bushehr NPP First Circuit Using Neural Networks
, M.Sc. Thesis Sharif University of Technology ; Ghaffari, Mohsen (Supervisor) ; Moshkbar-Bakhshayesh, Khalil (Co-Supervisor)
Abstract
Sensors are one of the most vital instruments in Nuclear Power Plants (NPP), and operators and safety systems monitor various parts of the NPP and control transients by analyzing the values reported by the sensors. Failure to detect malfunctions or anomalies in them would lead to catastrophic consequences. A new approach based on thermo-hydraulic simulation by RELAP5 code and Feed-Forward Neural Networks (FFNN) is given to detect faulty sensors and estimate their correct value which are two main objectives of the current study. This approach consists of two main parts; The first part, Fault Detection Hyper Block (FDHB), responsible for detecting faulty sensors, and the second part,...
Estimation Influential Parameters in Operation of the Bushehr Nuclear Power Plant using Neural Network
, M.Sc. Thesis Sharif University of Technology ; Ghofrani, Mohammad Bagher (Supervisor) ; Moshkbar Bakhshayesh, Khalil (Co-Advisor)
Abstract
Given many computing errors in current systems, a method appears necessary for predicting the nuclear parameter quickly and accurately. In this thesis, a neural network was used to predict safety in a nuclear power plant in order to develop an operating aid tool for preventive measures.First, some studies were conducted on appropriate feature selection for training neural networks. Some case studies have also been carried out on parameter prediction through soft computing in a power plant. In the next section, an expert judgment was taken into account to select DNBR (Departure from Nucleate Boiling Ratio) as a criterion for safety evaluation in the exploitation of a nuclear power plant (PWR)...
Estimation of Power Peaking Factor (PPF)Parameter in VVER Reactor Using Soft Computing, Case Study: Bushehr Nuclear Power Plant
, M.Sc. Thesis Sharif University of Technology ; Ghofrani, Mohammad Bagher (Supervisor) ; Moshkbar Bakhshayesh, Khalil (Supervisor)
Abstract
operation of a nuclear power plant. Therefore, constant monitoring of the reactor core with reliable methods is important. To monitor the reactor heart, it is necessary to estimate and calculate some parameters, with high speed and accuracy, such as power distribution inside the heart, reactivity feedback coefficients, PPF, DNBR, etc. Analytical methods are often used to calculate these parameters, which in case of failure of the sensors, the calculations will be practically disrupted, and the method used in this research can solve these problems by losing a small amount of accuracy.In this study using real data of Bushehr nuclear power plant (BNPP) and by soft computing methods and...
PIDEC Batteries Simulation Accompany with Comparative Study of Key Parameters on its Efficiency
, M.Sc. Thesis Sharif University of Technology ; Moshkbar Bakhshayesh, Khalil (Supervisor) ; Mohtashami, Soroush (Co-Supervisor)
Abstract
Nuclear batteries have been attractive since the beginning of the twentieth century due to their longer life in comparison with other types of batteries. These batteries are produced in different types with different efficiencies depending on the method used to convert their energy. In this study, due to the lack of the resources in the country to study the types of nuclear batteries, first we did an overview of the classification and operation of different types of nuclear batteries, especially on the latest type of nuclear batteries, PIDEC, that has not been studied in the country so far. The most important feature of a PIDEC is that it has an intermediate stage to transmit the maximum...
Design and Development of Gamma Wireless Detector with the Use of Soft Computing to Identify the Type of Radioisotope for Environmental Monitoring
, M.Sc. Thesis Sharif University of Technology ; Vosoughi, Naser (Supervisor) ; Moshkbar Bakhshayesh, Khalil (Co-Supervisor)
Abstract
The inability to directly observe radioactive rays, from the beginning of the discovery of these rays, led various people to seek a suitable solution to detect and recognize their properties. To this end, many detectors with different capabilities were built. On the other hand, in the electronic world, it was possible to analyze electrical pulses and extract information based on them. Therefore, some detectors were designed to convert the traces left by the entry and interaction of radioactive rays into measurable electrical pulses proportional to the properties of the incident radiation. Analysis of these pulses provides information about the properties of the incident rays. So far, a...
Comparative study of application of different supervised learning methods in forecasting future states of NPPs operating parameters
, Article Annals of Nuclear Energy ; Volume 132 , 2019 , Pages 87-99 ; 03064549 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
In this paper, some important operating parameters of nuclear power plants (NPPs) transients are forecasted using different supervised learning methods including feed-forward back propagation (FFBP) neural networks such as cascade feed-forward neural network (CFFNN), statistical methods such as support vector regression (SVR), and localized networks such as radial basis network (RBN). Different learning algorithms, including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are used in CFFNN method. SVR method is used with different kernel functions including Gaussian, polynomial, and...
Development of a novel analytical method for calculating the dose equivalent rate as a case study of fields which obey the inverse square law
, Article Journal of Instrumentation ; Volume 14, Issue 9 , 2019 ; 17480221 (ISSN) ; Sharif University of Technology
Institute of Physics Publishing
2019
Abstract
The field of any point source which is broadened equally in all directions without any limitation to its range is within category of the inverse square law (ISL). As a case study, the dose equivalent (DE) rate is calculated. For calculating the DE rate, the radiation source can be divided into multiple layers and each layer is fractionated to multiple rectangular surfaces. Each rectangular surface can be replaced with three types of sectors. The DE rate of a source on a target is then sum of DE rates of sectors. The developed method is independent of the target position relative to the source and is used for the dose calculation of any arbitrary arrangement of source and target. As an...
Calculating the dose equivalent of coordinate surfaces of the Cartesian geometry: A new analytical method compared with Monte Carlo method
, Article Journal of Instrumentation ; Volume 14, Issue 8 , 2019 ; 17480221 (ISSN) ; Sharif University of Technology
Institute of Physics Publishing
2019
Abstract
In this paper, an analytical method for calculation of the dose equivalent (DE) of coordinate surfaces of the Cartesian geometry is presented. DE of rectangular surfaces of gamma radiation emitters is calculated. The developed analytical method changes rectangular surface to multiple polar regions by dividing its surface into four types of sectors. By this method, the calculation of the dose is converted into calculation of simple mathematical series. The dose of rectangular shape sources for different gamma radiation emitters at different distances to target is calculated and the results are compared with MCNP code. Results show very good agreement. Advantages of the developed method are:...
Bayesian regularization of multilayer perceptron neural network for estimation of mass attenuation coefficient of gamma radiation in comparison with different supervised model-free methods
, Article Journal of Instrumentation ; Volume 15, Issue 11 , November , 2020 ; Sharif University of Technology
IOP Publishing Ltd
2020
Abstract
Multilayer perceptron (MLP) neural networks have been used extensively for estimation/regression of parameters. Moreover, recent studies have shown that learning algorithms of MLP which are based on Gaussian function are more accurate. In this paper, the mass attenuation coefficient (MAC) of gamma radiation for light-weight materials (e.g. O-8), mid-weight materials (e.g. Al-13), and heavy-weight materials (e.g. Pb-82) is modelled using Gaussian function based regularization of MLP (i.e. Bayesian regularization (BR)) and by a modular estimator. The results are compared with the Reference results. To show better performance of the utilized algorithm, the results of the different supervised...
Constructing energy spectrum of inorganic scintillator based on plastic scintillator by different kernel functions of SVM learning algorithm and TSC data mapping
, Article Journal of Instrumentation ; Volume 15, Issue 1 , January , 2020 ; Sharif University of Technology
Institute of Physics Publishing
2020
Abstract
In this paper, a novel idea is developed to construct energy spectrum of inorganic scintillator detector (e.g. NaI(Tl)) using energy spectrum of organic scintillator detector (e.g. NE102) by means of a model-free method. For this purpose, support vector machine (SVM) accompanied with different kernel functions (i.e. linear, polynomial, and Gaussian) is applied. The spectra of NE102 and NaI(Tl) detectors of the single radioisotopes (i.e. Co60, Cs137, Na22, and Am241) are utilized for training of SVM. In other words, data of NE102 detector are input spectrums of training patterns and data of NaI(Tl) detector are target spectrums of training patterns. To construct an appropriate mapping...
Development of an efficient technique for constructing energy spectrum of NaI(Tl) detector using spectrum of NE102 detector based on supervised model-free methods
, Article Radiation Physics and Chemistry ; Volume 176 , November , 2020 ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
The motivation of this study is development of a technique to construct energy spectrum of higher price/high resolution detectors (e.g. NaI (Tl)) using spectrum of lower price/low resolution detectors (e.g. NE102). Since there is no explicit mathematical model between these type of detectors (i.e. organic and inorganic scintillator detectors), it is necessary to utilize model-free methods. Construction of mapping function to generate spectrum of inorganic scintillator using spectrum of organic scintillator can be done by supervised model-free methods. Different supervised learning methods including localized neural networks, statistical methods, feed-forward neural networks, and conditional...
Prediction of unmeasurable parameters of NPPs using different model-free methods based on cross-correlation detection of measurable/unmeasurable parameters: a comparative study
, Article Annals of Nuclear Energy ; Volume 139 , May , 2020 ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
In this paper cross-correlation of measurable/unmeasurable parameters of nuclear power plants (NPPs) are detected. Correlation techniques including Pearson's, Spearman's, and Kendall-tau give appropriate input parameters for training/prediction of the target unmeasurable parameters. Fuel and clad maximum temperatures of uncontrolled withdrawal of control rods (UWCR) transient of Bushehr nuclear power plant (BNPP) are used as the case study target parameters. Different model-free methods including decision tree (DT), feed-forward back propagation neural network (FFBPNN) accompany with different learning algorithms (i.e. gradient descent with momentum (GDM), scaled conjugate gradient (SCG),...
Performance study of bayesian regularization based multilayer feed-forward neural network for estimation of the uranium price in comparison with the different supervised learning algorithms
, Article Progress in Nuclear Energy ; Volume 127 , September , 2020 ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
In this study, the estimation of the uranium price as one of the most important factors affecting the fuel cost of nuclear power plants (NPPs) is investigated. Supervised learning algorithms, especially, multilayer feed-forward neural network (FFNN) are used extensively for parameters estimation. Similar to other supervised methods, FFNN can suffer from overfitting (i.e. imbalance between memorization and generalization). In this study, different regularization techniques of FFNN are discussed and the most appropriate regularization technique (i.e. Bayesian regularization) is selected for estimation of the uranium price. The different methods including different learning algorithms of FFNN,...
Identification of the appropriate architecture of multilayer feed-forward neural network for estimation of NPPs parameters using the GA in combination with the LM and the BR learning algorithms
, Article Annals of Nuclear Energy ; Volume 156 , 2021 ; 03064549 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
In this study, accurate estimation of nuclear power plant (NPP) parameters is done using the new and simple technique. The proposed technique using the genetic algorithm (GA) in combination with the Bayesian regularization (BR) and Levenberg- Marquardt (LM) learning algorithms identifies the appropriate architecture for estimation of the target parameters. In the first step, the input patterns features are selected using the features selection (FS) technique. In the second step, the appropriate number of hidden neurons and hidden layers are investigated to provide a more efficient initial population of the architectures. In the third step, the estimation of the target parameter is done using...
Investigating the performance of the supervised learning algorithms for estimating NPPs parameters in combination with the different feature selection techniques
, Article Annals of Nuclear Energy ; Volume 158 , 2021 ; 03064549 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
Several reasons such as no free lunch theorem indicates that any learning algorithm in combination with a specific feature selection (FS) technique may give more accurate estimation than other learning algorithms. Therefore, there is not a universal approach that outperforms other algorithms. Moreover, due to the large number of FS techniques, some recommended solutions such as using synthetic dataset or combining different FS techniques are very tedious and time consuming. In this study to tackle the issue of more accurate estimation of NPPs parameters, the performance of the major supervised learning algorithms in combination with the different FS techniques which are appropriate for...
The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network
, Article Nuclear Engineering and Technology ; Volume 53, Issue 12 , 2021 , Pages 3944-3951 ; 17385733 (ISSN) ; Sharif University of Technology
Korean Nuclear Society
2021
Abstract
Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and...
Developing a new approach for material discrimination using modular radial basis neural networks based on dual-energy X-ray radiography
, Article Annals of Nuclear Energy ; Volume 188 , 2023 ; 03064549 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2023
Abstract
Challenges, especially, identification of correlation of attenuation coefficients at high/low energy X-ray, stochastic noise on images, source energy fluctuation, and angular drop-off of X-ray energy/dose rate affect the quality of the material discrimination using dual-energy X-ray radiography. In this study, a new approach using radial basis network (RBN) is applied for the identification of materials. A modular structure, in which each module is responsible for the identification of a single material-thickness is developed. Each module with its specific RBN not only is trained to identify its own material-thickness but also is trained to reject the other ones. The activation/radial...
Classification of NPPs transients using change of representation technique: A hybrid of unsupervised MSOM and supervised SVM
, Article Progress in Nuclear Energy ; Volume 117 , 2019 ; 01491970 (ISSN) ; Mohtashami, S ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
This study introduces a new identifier for nuclear power plants (NPPs) transients. The proposed identifier changes the representation of input patterns. Change of representation is a semi-supervised learning algorithm which employs both of labeled and unlabeled input data. In the first step, modified self-organizing map (MSOM) carries out an unsupervised learning algorithm on labeled and unlabeled patterns and generates a new metric for input data. In the second step, support vector machine (SVM) as a supervised learning algorithm classifies the input patterns using the generated metric of the first step. In contrast to unsupervised learning algorithms, the proposed identifier does not...
Unsupervised classification of NPPs transients based on online dynamic quantum clustering
, Article European Physical Journal Plus ; Volume 134, Issue 10 , 2019 ; 21905444 (ISSN) ; Pourjafarabadi, E ; Sharif University of Technology
Springer Verlag
2019
Abstract
In this study, we propose a new method for identification of nuclear power plants (NPPs) transients based on online dynamic quantum clustering (DQC). In this unsupervised learning algorithm, the Gaussian kernel is the eigen-state of the Schrödinger equation and the minimums of the Schrödinger potential are the cluster centers of patterns. For clustering of transients, data of each event are given to the DQC and form a cluster independent of other transients. This process is done for all target plant conditions. The formed clusters are labeled according to the name of their related transients. Afterwards, to test the proposed identifier, as time goes by, new data points move toward the formed...
Developing an approach for fast estimation of range of ion in interaction with material using the Geant4 toolkit in combination with the neural network
, Article Nuclear Engineering and Technology ; Volume 54, Issue 11 , 2022 , Pages 4209-4214 ; 17385733 (ISSN) ; Mohtashami, S ; Sharif University of Technology
Korean Nuclear Society
2022
Abstract
Precise modelling of the interaction of ions with materials is important for many applications including material characterization, ion implantation in devices, thermonuclear fusion, hadron therapy, secondary particle production (e.g. neutron), etc. In this study, a new approach using the Geant4 toolkit in combination with the Bayesian regularization (BR) learning algorithm of the feed-forward neural network (FFNN) is developed to estimate the range of ions in materials accurately and quickly. The different incident ions at different energies are interacted with the target materials. The Geant4 is utilized to model the interactions and to calculate the range of the ions. Afterward, the...