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Quantitative Characteristic X-Ray Analysis for Different Compound Samples by Using Monte Carlo Method

100%
Tekın H., Altunsoy E., Manici T., Yilmaz B.
Acta Physica Polonica A
|
2017
|
vol. 132
|
issue 3
439-441
EN
X-ray spectrometry is an elemental analysis technique with broad application in science and industry. It is based on the principle that individual atoms, when excited by an external energy source emit X-ray photons of a characteristic energy or wavelength. Here we obtained the characteristic X-ray peaks of different brass compounds and compared the intensity of characteristic peaks by changing with Zn rate. In this study we modelled different brass samples by using Monte Carlo method and changed the zinc rate in brass samples for each compound. We obtained the characteristic X-ray peaks of different compounds and compared the intensity of characteristic peaks by changing with Zn rate. We achieved a good agreement between peak intensity and corresponding element rate in compound. Also we obtained that characteristic peak rate increases by corresponding element rate in compound. This study shows that Monte Carlo method is very effective method to simulate material features due to their characteristic peaks and their intensities.
2
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An Artificial Neural Network-Based Estimation of Bremsstarahlung Photon Flux Calculated by MCNPX

88%
Tekin H., Manici T., Altunsoy E., Yilancioglu K., Yilmaz B.
Acta Physica Polonica A
|
2017
|
vol. 132
|
issue 3
967-969
EN
Bremsstrahlung has an important place in the field of experimental physics, especially for description of photon-matter interaction and for characterization and analysis of materials. Bremsstrahlung photon is created by a high-energy electron, deflected in the electric field of atomic nucleus. Bremsstrahlung is also important for experimental studies, not only in the field of nuclear physics and particle physics but also in the fields of solid state physics, applied physics and astrophysics. In recent years, Monte Carlo simulation has become a widely used method for calculations related to bremsstrahlung. On the other hand, predictions by using artificial neural network can be performed with high accuracy. This study aims at observing variation in the photon flux as unction of target thickness and at processing output data by using an artificial neural network. We achieved a high degree of compatibility between two different methods. This study suggests that artificial neural network is a powerful tool for prediction of Bremsstrahlung and for other scientific problems.
3
Content available remote

A Prediction Study on Bremsstrahlung Photon Flux of Tungsten as a Radiological Anode Material by using MCNPX and ANN Modeling

88%
Tekin H., Kara U., Manici T., Altunsoy E., Erguzel T.
Acta Physica Polonica A
|
2017
|
vol. 132
|
issue 3
433-435
EN
Medical imaging is a technique that is mostly known as visual representations of the parts of body for clinical scans and analysis. In imaging process for medical purpose there take part radiologists, radiographers/radiology technicians, medical physicists, sonographers, nurses, and engineers. As an apart issue from the medical imaging devices, we can treat X-rays using devices such as radiography, computed tomography, fluoroscopy, dental cone-beam computed tomography, and mammography. All these devices are to perform X-ray using during medical imaging process. An X-ray beam is generated in a vacuum tube that is principally composed of an anode and a cathode material to produce X-ray beams, whose name is X-ray tube. The anode represents the component in which the X-ray beam produced that made from a piece of metal. For decades, tungsten (W) has been used as an anode material of various X-ray tubes. Tungsten has high atomic number and high melting point of 3370°C with low rate of volatilization. In this study, we performed Monte Carlo simulation for flux calculations of W target by using MCNP-X general purpose code and considered result as a data set for artificial neural network. It can be concluded that the results agreed well between Monte Carlo simulation and artificial neural network prediction.
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