Sharif Digital Repository

The elastic behavior of a screw dislocation which is positioned inside the shell domain of an eccentric core-shell nanowire is addressed with taking into account the surface/interface stress effect. The complex potential function method in combination with the conformal mapping function is applied to solve the governing non-classical equations. The dislocation stress field and the image force acting on the dislocation are studied in detail and compared with those obtained within the classical theory of elasticity. It is shown that near the free outer surface and the inner core-shell interface, the non-classical solution for the stress field considerably differs from the classical one, while... 

Sulfur doped and pure micro-nanoporous TiO2 film were synthesized with PEO method to produce a film with a high surface area for photocatalysis applications. The effect of applied voltage and electrolyte concentration on the microstructure and photocatalytic properties of the prepared layer were investigated via SEM, XRD, EIS and DRS studies. Electrochemical Impedance Spectroscopy (EIS) was carried out in order to determine the corrosion and electrochemical properties of the produced layer. It was found that although the barrier layer resistance decreases with the voltage, the layers porosity and consequently the surface area increases. Finally the XRD and DRS spectrums were correlated with... 

We propose an inexact nonmonotone successive quadratic programming (SQP) algorithm for solving nonlinear programming problems with equality constraints and bounded variables. Regarding the value of the current feasibility violation and the minimum value of its linear approximation over a trust region, several scenarios are envisaged. In one scenario, a possible infeasible stationary point is detected. In other scenarios, the search direction is computed using an inexact (truncated) solution of a feasible strictly convex quadratic program (QP). The search direction is shown to be a descent direction for the objective function or the feasibility violation in the feasible or infeasible... 

An inexact nonmonotone filter sequential quadratic programming algorithm is presented for solving general constrained nonlinear programming problems. At every iteration, a steering direction is computed as a minimizer of a linear model of the constraint violation over a trust region. A possible infeasible stationary point can be detected using the steering direction. If the current iterate is not an infeasible stationary point, a strongly convex feasible quadratic programming subproblem is defined to compute a search direction as an inexact solution satisfying some loose and achievable conditions. We prove that the search direction is a descent direction for the constraint violation or the... 

We propose an inexact nonmonotone successive quadratic programming (SQP) algorithm for solving nonlinear programming problems with equality constraints and bounded variables. Regarding the value of the current feasibility violation and the minimum value of its linear approximation over a trust region, several scenarios are envisaged. In one scenario, a possible infeasible stationary point is detected. In other scenarios, the search direction is computed using an inexact (truncated) solution of a feasible strictly convex quadratic program (QP). The search direction is shown to be a descent direction for the objective function or the feasibility violation in the feasible or infeasible... 

An inexact nonmonotone filter sequential quadratic programming algorithm is presented for solving general constrained nonlinear programming problems. At every iteration, a steering direction is computed as a minimizer of a linear model of the constraint violation over a trust region. A possible infeasible stationary point can be detected using the steering direction. If the current iterate is not an infeasible stationary point, a strongly convex feasible quadratic programming subproblem is defined to compute a search direction as an inexact solution satisfying some loose and achievable conditions. We prove that the search direction is a descent direction for the constraint violation or the... 

We propose an inexact nonmonotone successive quadratic programming (SQP) algorithm for solving nonlinear programming problems with equality constraints and bounded variables. Regarding the value of the current feasibility violation and the minimum value of its linear approximation over a trust region, several scenarios are envisaged. In one scenario, a possible infeasible stationary point is detected. In other scenarios, the search direction is computed using an inexact (truncated) solution of a feasible strictly convex quadratic program (QP). The search direction is shown to be a descent direction for the objective function or the feasibility violation in the feasible or infeasible... 

Quantum computing is an emerging and promising computational paradigm that provides substantial speedup for a variety of tasks such as integer factorization, database search, and machine learning. One of the quantum computation features is the possibility of developing quantum algorithms, which could be faster than algorithms developed for classic computers. However, we are still unable to fully realize a physical quantum computer and depend on traditional computers to simulate their behavior and test quantum algorithms. This is a source of complexity since one of the challenges to simulate quantum algorithms is the exponential memory requirement. In this work, we propose a method to... 

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... 

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... 

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:... 

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... 

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... 

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... 

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),... 

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,... 

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... 

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...