Heat and charge transport in a single barrier heterostructure are studied by implementing MONTE CARLO method. A model is proposed to simulate the device based on Boltzmann transport equation coupled with Poisson and phonon balance equations. Our calculation is showing that multilayer semiconductor heterostructures can be used for large logical cooling purposes. A temperature difference of about 3 degrees Kelvin is observed over a single barrier (trilayer) device. The effect of different parameters of the device such as doping density, applied voltage, barrier height and width on transport is investigated.

Background: The aim of this study was to investigate organ absorbed dose for the patients undergoing routine x-ray imaging procedures in hospitals under the control of Yasuj University of Medical Sciences, Iran, in year 2014 using experimental measurement and MONTE CARLO calculations.Methods: Entrance surface dose for three common radiology examinations in five radiology centers was measured. The entrance surface dose was measured in 12 randomly selected patients (male and female) for each x-ray examination. Patients were not exposed to any additional radiation and the radiographs were used for diagnostic purposes.Findings: The entrance surface dose for the chest x-ray examinations were found to be in the range of 0.29 to 1.284 mGy. The ESD values for the skull examinations were in the range of 1.96 to 3.8 mGy. For both chest and skull examinations, females received the maximum imposed radiation dose.Conclusion: The results of the present study indicate a need for quality control (QC) and quality assurance (QA) programs to be undertaken to avert high cost and high patient doses. The recommendations to avoid unnecessary radiation exposure are also needed to decrease the patient’s absorbed dose.

The computational burdens and more accurate approximations for the estimation of the failure probability are the main concerns in the structural reliability analyses. The MONTE CARLO simulation (MCS) method can simply provide an accurate estimation for the failure probability, but it is a time-consuming method for complex reliability engineering problems with a low failure probability and may efficiently approximate the failure probability. In this paper, the efficiency of MCS for the computations of the performance function is improved using a random-weighted method known as the random-weighted MCS (RWMC) method. By using the weighted exponential function, the weights of random data points generated by MCS are adjusted by selecting the random point in the design space. The convergence performances including the computational burdens for evaluating the limit sate function and the accuracy of failure probabilities of RWMC are compared with MCS by using several nonlinear and complex mathematical and structural problems with normal and no-normal random variables. The results indicate that the proposed RWMC method can estimate the accurate results with the less computational burdens, about 100 to 1000 times faster than MCS.

In many practical applications of structural reliability ANALYSIS, one of the preferred sciences is the sensitivity ANALYSIS of failure probability based on design parameters under the limit state function. This information is needed, for example, in design optimization based on reliability. The design parameters are calculated by first- and second-order reliability methods FORM\SORM to sensitivity ANALYSIS of the probability of failure approximately. Therefore, in many cases, these methods are very inaccurate or difficult to solve problems. Accordingly, the MONTE CARLO simulation method is very useful for determining the reliability sensitivity parameters. The derivation probability of failure into the parameters of the limit state function is calculated by integrating the surface of the performance function. By using the MONTE CARLO simulation to determine this integral will not be efficient due to the small portion probability of failure and will have a high computational cost. Hence, some methods can be used to estimate MONTE CARLO simulations that reduce the computational cost. One of these methods is linear sampling method. In this paper, an approximate method is used to determine the surface integral in terms of a domain integral. The integration domain can be calculated using standard MONTE CARLO simulations or importance sampling simulations. This article presents two practical examples of sandwich panels to determine the effectiveness of parameters random variables are used in the design.

Background: This study aimed to optimize efficiency in MONTE CARLO (MC) simulation using sensitivity ANALYSIS of a beam model. Methods: The BEAMnrc? based model of 6 MV beam of a Siemens Primus linac was developed. For sensitivity ANALYSIS, the effect of the electron source, treatment head, and virtual phantom specifications on calculated percent depth dose (PDD) and lateral dose profiles was evaluated. Results: The optimum mean energy (E) and the full width at half maximum (FWHM) of the intensity distribution of the electron beam were calculated as 6. 7 MeV and 3 mm, respectively. Increasing E from 6. 1 to 6. 7 MeV, increased the PDD in the fall? off region by 4. 70% and decreased the lateral profile by 8. 76%. Changing the FWHM had a significant effect on the buildup region of PDD and the horns and out? of? field regions of the lateral profile. Increasing the collimators opening by 0. 5 mm, PDD increased by 2. 13% and the central and penumbra regions of profiles decreased by 1. 98% and 11. 40% respectively. Collimator properties such as thickness and density were effective in changing the penumbra (11. 32% for 0. 25 cm increment) and the out? of? field (22. 82% for 3 g/cm3 ) regions of the lateral profiles. Conclusion: ANALYSIS of a 6 MV model showed that PDD profiles were more sensitive to changes in energy than to FWHM of the electron source. The lateral profiles were sensitive to E, FWHM, and collimator opening. The density of the collimator affected only the out? of? field region of lateral profiles. The findings of this study may be used to make benchmarking of an MC beam model more efficient.