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Abstract: The general purpose computer code MCNP4B was used to simulate the response function of a bare NaI(Tl) detector crystal for gamma rays from an 241Am/Be source capsule. The simulated spectral shape generated by the MCNP4B code was compared with the measured spectral shape obtained using a gamma-ray spectrometer with a cylindrical shape, 7.62cm×7.62cm, NaI(Tl). In general, the agreement between the simulation and the experimental response function was good. [Copyright &y& Elsevier]

Abstract Self-absorption correction factors are fundamental in spectroscopy to correct the efficiency of the samples detection whose density is different from the radioactive standard. Mathematical simulations have been widespread as a tool to facilitate the procedure of correction factors calculation. In this paper, LabSOCS was used to calculate the self-absorption correction factor for some geometries and the values found were compared to those obtained in MCNP and experimental values. The percentage deviations found for the self-absorption correction factor calculated by LabSOCS were below 1.6% when compared to experimental values. Deviations were below 1.9% in the curve extrapolation of the experimental procedure found in literature. Results obtained show that the deviations increase proportionally to the difference between the density values of the radioactive standard and the sample. High percentage deviations were also noticed in simulations whose samples had high densities, complex geometries and low energy gamma-rays. Highlights • Great density differences between the sample and the radioactive standard should not be used. • It is noticed that percentage deviations increase with an increase in density and with an energy decrease. • Geometry complexity is a factor which increases the percentage differences of the self-absorption correction factor. • Must be careful when reproducing the same chemical compositions of the experimental procedure in a computational simulation. [ABSTRACT FROM AUTHOR]

This study presents a methodology based on the dual-mode gamma densitometry technique in combination with artificial neural networks to simultaneously determine type and quantity of four different fluids (Gasoline, Glycerol, Kerosene and Fuel Oil) to assist operators of a fluid transport system in pipelines commonly found in the petrochemical industry, as it is necessary to continuously monitor information about the fluids being transferred. The detection system is composed of a 661.657 keV (137Cs) gamma-ray emitting source and two NaI(Tl) scintillation detectors to record transmitted and scattered photons. The information recorded in both detectors was directly applied as input data for the artificial neural networks. The proposed intelligent system consists of three artificial neural networks capable of predicting the fluid volume percentages (purity level) with 94.6% of all data with errors less than 5% and MRE of 1.12%, as well as identifying the pair of fluids moving in the pipeline with 95.9% accuracy. • A methodology to identify the interface region of petroleum by-products transportation in polyducts. • The identification of the interface region was performed for several petroleum by-products. • This study has been used the MCNP6 code and artificial neural network. • The geometry consists of a gamma-ray source and uses dual-modality measurements. • The static models used for a stratified flow regime on the biphasic system were developed using the MCNP6. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the application of a computational methodology for optimizing the conversion of medical tomographic images in voxel anthropomorphic models for simulation of radiation transport using the MCNP code. A computational system was developed for digital image processing that compresses the information from the DICOM medical image before it is converted to the Scan2MCNP software input file for optimization of the image data. In order to validate the computational methodology, a radiosurgery treatment simulation was performed using the Alderson Rando phantom and the acquisition of DICOM images was performed. The simulation results were compared with data obtained with the BrainLab planning system. The comparison showed good agreement for three orthogonal treatment beams of 60Co gamma radiation. The percentage differences were 3.07%, 0.77% and 6.15% for axial, coronal and sagital projections, respectively. [Copyright &y& Elsevier]

• A methodology to identify the interface region of petroleum by-products transportation in polyducts. • The identification of the interface region was performed for several petroleum by-products. • This study has been used the MCNP6 code and artificial neural network. • The geometry consists of a gamma-ray source and uses dual-modality measurements. • The static models used for a stratified flow regime on the biphasic system were developed using the MCNP6. This article presents a methodology for predicting the purity level of oil byproducts that are transported in polyducts, thus identifying the interface region. The detection system consists of a 662 keV gamma-ray source and two NaI(Tl) detectors to register the transmitted and scattered beams. An artificial neural network (ANN) was trained and evaluated for interpret the spectra recorded in both detectors. The Purity Level and the Purity Indicator of the fluids were assigned as outputs from the ANN. The models for a stratified flow regime are composed of fluids (gasoline, kerosene, oil, and glycerol) with several combinations of biphasic flows developed using MCNP6 code. The results were determined with the MAPE of 2.40% for all patterns in the prediction of the purity level of fluids. The proposed methodology has the potential to detect the interface region and identify the petroleum byproducts, with an accuracy of 99% as purity level. [ABSTRACT FROM AUTHOR]