Education

  • Ph.D. Sep. 2010-Sep. 2014

    Nuclear Engineering

    Sharif University of Technology, Tehran

  • M.S.

    Mechanical Engineering

    Sharif University of Technology, Tehran

  • B.S.

    Applied physics

    Tabriz University, Tabriz

Extracurricular Activities

Honors, Awards and Grants

Rewarded as top graduate of doctoral degree of Sharif University of Technology in 2015.

Research Colleagues

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Development of a novel analytical method for calculating the dose equivalent rate as a case study of fields which obey the inverse square law

Khalil Moshkbar-Bakhshayesh
Journal Paper Journal of Instrumentation, 2019, Volume 14, Pages 1-13

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 examples, the DE rate is calculated for the square/rectangular gamma emitter sources in comparison with MCNP code and numerical method. Results show very good agreement. Noticeable advantages are: (1) The developed method performs a very quick calculation (i.e. more than 100000 times faster) while Monte Carlo techniques usually take time to obtain adequate statistics on small regions (2) The developed method calculates the DE rate more than 100 times faster than numerical method (3) Usually the trained person calculates the dose with Monte Carlo codes while analytical calculation does not need the trained one. The proposed analytical method is basis of software package for the DE calculation of complex surfaces which is under development.

Calculating the dose equivalent of coordinate surfaces of the Cartesian geometry: a new analytical method compared with Monte Carlo method

Khalil Moshkbar-Bakhshayesh
Journal Paper Journal of Instrumentation, 2019, Volume 14, Pages 1-14

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: 1—While Monte Carlo techniques usually take time to obtain adequate statistics on small regions, the developed method is independent of statics and therefore performs a very quick calculation (i.e. more than 100000 times faster) 2—Usually the trained person is required to calculate the dose with Monte Carlo codes while analytical calculation does not need the trained one. Moreover, analytical methods make possible to perform easily parametric analysis and to reach desired outlet of the dose. Since rectangles with proper size in sufficient numbers can completely reconstruct any surface, therefore, dose of complex surfaces can be calculated using the developed method.

Comparative study of application of different supervised learning methods in forecasting future states of NPPs operating parameters

Khalil Moshkbar-Bakhshayesh
Journal Paper Annals of Nuclear Energy, 2019, Volume 132, Pages 87-99

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 linear. RBN is used with radial activation function. Comparison of the results indicates that learning algorithms based on Gaussian distribution function (i.e. BR algorithm) and Gaussian kernel/activation functions are, in general, more precise for time series prediction. Moreover, learning methods based on Gaussian function lead to acceptable results in prediction of complicated time series, such as core inlet flowrate of large break loss of coolant accident (LBLOCA) which are changed irregularly and drastically. In other words, Gaussian learning algorithms/kernel functions/activation functions are appropriate choices for NPPs parameters forecasting.

Classification of NPPs transients using change of representation technique: A hybrid of unsupervised MSOM and supervised SVM

Khalil Moshkbar-Bakhshayesh, Soroush Mohtashami ,
Journal PaperProgress in Nuclear Energy, 2019, Volume 117, Pages 1-12

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 discard the collected information. The proposed identifier is examined by the Iris flower dataset and the Bushehr nuclear power plant (BNPP) transients. In overall, results show good performance of the developed identifier. Training with small fraction of labeled atterns, classification only by the sign of the classifiers outputs, and modular identification are main advantages of the proposed identifier. Results indicate that the developed MSOM is not able to cluster correctly quasi-static transients such as uncontrolled withdrawal of control rods (UWCR) in presence of steady state patterns. Quasi-static transients are very similar to steady state. Moreover, imposed noise on input data may eliminate minor differences among the patterns of them. This may cause wrong training of SVM. This is the most important challenge of semi-supervised learning. In other words, if the knowledge on density of unlabeled input patterns do not carry useful information for prediction of the targets of unlabeled patterns, then semi-supervised learning may degrade the prediction accuracy. Therefore, the proposed identifier is more appropriate for classification of transients in which either clusters have been apart or small noises have been imposed on data.

Unsupervised Classification of NPPs Transients Based on Online Dynamic Quantum Clustering

Khalil Moshkbar-Bakhshayesh, Esmaiel Pourjafarabadi
Journal Paper European Physical Journal Plus, 2019 (Accepted)

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 Gaussian kernel is the eigen-state of the Schrödinger equation and minimums of the Schrödinger potential are 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. Formed clusters are labeled according to the name of their related transients. Afterwards, for test of the proposed identifier, as time goes by new data points moving toward the formed potential wells. Finally, each new datum falls into appropriate cluster and therefore the type of transient is identified online. The DQC unlike previously developed unsupervised learning algorithms is not dependent on geometric proximity of data. The developed identifier is examined by the Iris flower dataset and typical WWER-1000 plant transients. Results show reasonable performance of the DQC. We use singular value decomposition (SVD) and bipolar representation of real data to reduce the dimensions of the data and to show explicitly positive and negative sides of information. The major novelty of this identifier is development of a technique for online transient identification of NPPs utilizing the DQC without any preliminary information about the input patterns. The developed method is a step forward for practical recognition of NPPs transients.

Recent Industrial projects/Training workshops

  • Feb. 2018-Sep. 2018
    Unsupervised identification of patterns using dynamic quantum clustering
  • Apr. 2019-Now
    Developing a method to generate spectrum of solid crystal scintillator detector based on plastic detector
  • Jul. 2019
    Workshop of algorithms, programming codes, and applications of soft computing (Duration: 40 hours; Location: atomic energy organization of Iran (AEOI))

Teaching Experience

  • 2018-2019

    Health physics

  • 2018-2019

    Industrial applications of radioisotopes

  • 2018-2019

    Accelerators and applications

  • 2019

    Nuclear Physica

  • 2016-2017

    Dynamics of nuclear reactors

  • 2016-2107

    Nuclear safety

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At My Office

You can find me at my office located at Deaprtment of Energy Engineering, floor 3, south side.

I am ususally at my office, but you may consider an email to fix an appointment.