The possibility of expressing the total particle and energy reflection coefficients of low-energy photons in the form of universal functions valid for different shielding materials is investigated in this paper. The analysis is based on the results of Monte Carlo simulations of photon reflection by using MCNP, FOTELP, and PENELOPE codes. The normal incidence of the narrow monoenergetic photon beam of the unit intensity and of initial energies from 20 keV up to 100 keV is considered, and particle and energy reflection coefficients from the plane homogenous targets of water, aluminum, and iron are determined and compared. The representations of albedo coefficients on the initial photon energy, on the probability of large-angle photon scattering, and on the mean number of photon scatterings are examined. It is found out that only the rescaled albedo coefficients dependent on the mean number of photon scatterings have the form of universal functions and these functions are determined by applying the least square method

This paper describes new Monte Carlo codes for proton transport simulations in complex geometrical forms and in materials of different composition. The SRNA codes were developed for three dimensional (3D) dose distribution calculation in proton therapy and dosimetry. The model of these codes is based on the theory of proton multiple scattering and a simple model of compound nucleus decay. The developed package consists of two codes: SRNA-2KG and SRNA-VOX. The first code simulates proton transport in combined geometry that can be described by planes and second order surfaces. The second one uses the voxelized geometry of material zones and is specifically adopted for the application of patient computer tomography data. Transition probabilities for both codes are given by the SRNADAT program. In this paper, we will present the models and algorithms of our programs, as well as the results of the numerical experiments we have carried out applying them, along with the results of proton transport simulation obtained through the PETRA and GEANT programs. The simulation of the proton beam characterization by means of the Multi-Layer Faraday Cup and spatial distribution of positron emitters obtained by our program indicate the imminent application of Monte Carlo techniques in clinical practice

The most powerful feature of the Monte Carlo method is the possibility of simulating all individual particle interactions in three dimensions and performing numerical experiments with a preset error. These facts were the motivation behind the development of a general-purpose Monte Carlo SRNA program for proton transport simulation in technical systems described by standard geometrical forms (plane, sphere, cone, cylinder, cube). Some of the possible applications of the SRNA program are: (a) a general code for proton transport modeling, (b) design of accelerator-driven systems, (c) simulation of proton scattering and degrading shapes and composition, (d) research on proton detectors; and (e) radiation protection at accelerator installations. This wide range of possible applications of the program demands the development of various versions of SRNA-VOX codes for proton transport modeling in voxelized geometries and has, finally, resulted in the ISTAR package for the calculation of deposited energy distribution in patients on the basis of CT data in radiotherapy. All of the said codes are capable of using 3-D proton sources with an arbitrary energy spectrum in an interval of 100 keV to 250 MeV

This paper describes a model for estimating the condition of the shafts of turbines of the current generator in Hydropower plant Đerdap 2. For this purpose, an integral diagnostic approach was used. Based on the diagnostics of the condition of the shaft and the estimated lifetime, a multi-layer perceptron (MLP) based artificial neural network (ANN) is built, which is able to estimate the remaining lifespan of the turbine shaft. The MLP ANN model has not been made in this way on turbogenerators of hydroelectric power plant Đerdap 2 until now. The significance of this approach is that experiment brings about topology of ML ANN (number of neurons and layers) which is optimal for this model, training and testing. Results obtained from the neural network can be further used for decision-making about the moment of diagnosis or maintenance actions, as well as reducing stagnation and production losses.