Proposing an intelligent approach for measuring the thickness of metal sheets independent of alloy type.

Radiation based gauges have been widely utilized as a nondestructive and robust tool for measuring the thickness of metal sheets in industry. The typical radiation thickness meter can just work accurately when the composition of the material is fixed during the measurement process. In conditions that material composition may differ substantially from the nominal composition, such as manufacturing rolled metals factories, the thickness measurements would be along with errors. The purpose of the present research is resolving the problem of measuring the thickness of metal sheets with various alloys. The aluminum is investigated in this work as a case study but the procedure can be applied for other types of metals. As the first step, the performance of various arrangements of two main detection techniques, named dual energy and dual modality, were investigated using MCNPX code to obtain optimum technique and arrangement. The simulation results indicated that a binary combination of 241Am-60Co isotopes as the source and one transmission detector in dual energy technique is the most appropriate choice. After then, an experimental setup based on the obtained optimal technique from simulation investigations was established. The aluminum sheets with 4 alloy types of 1050, 3105, 5052 and 6061 and thicknesses in the range of 0.2-4 cm with a step of 0.2 cm were tested and the obtained data were implemented for testing and training the artificial neural network (ANN). The proposed methodology could predict the thickness of aluminum sheet independent of its alloy type with an error of less than 0.04 cm in experiments.

Designing a new graphite illuminator for imaging facility of INUS to improve neutron beam uniformity and intensity.

A cylindrical graphite illuminator with a thickness of 6.5 cm and diameter of 18 cm was installed inside the collimator of INUS (Instalatie de Neutronografie UScata) neutron imaging facility in the past. The graphite illuminator is usually utilized inside the collimator of neutron imaging facility to provide an intense and approximately uniform beam of neutrons at the outlet of collimator. With the mentioned existing illuminator in INUS imaging facility, the thermal neutron flux at the exit of collimator was measured 7.2 × 104 n/cm2/s. Also the obtained neutron beam profile in this facility shows that it is not completely uniform at the imaging screen and the intensity of neutrons at the top and bottom of beam profile are not the same. Hence, in this paper a new graphite illuminator is proposed to improve the neutron beam characteristics in INUS imaging facility. Monte Carlo N-Particle (MCNP) code was implemented in this study for evaluating the proposed illuminator. The shape of proposed illuminator is a cylinder whose one side is inclined. Three quality factors of thermal neutron intensity, thermal neutron beam uniformity and gamma radiation dose rate were used to evaluate performance of the new illuminator. In order to obtain optimum illuminator shape, three effective parameters of thickness, angle of inclined side and position of the illuminator inside the collimator were investigated in this research. The investigation was carried out on thicknesses in the range of 5 to 25 cm with a step of 5 cm, angles in the range of 10 to 60° with a step of 10° and positions of -5, 0 and 5 cm with respect to center of reactor core. After investigating and interpolating the results, it was found that the proposed illuminator with a thickness of 10 cm, angle of 54.5° and position of 0 can produce a uniform beam profile, increase the thermal neutron intensity up to 7.1% and also decrease the neutron to gamma ratio up to 5% in comparison with the existing one.

Optimization of radioactive sources to achieve the highest precision in three-phase flow meters using Jaya algorithm.

Gamma ray source has very important role in precision of multi-phase flow metering. In this study, different combination of gamma ray sources ((133Ba-137Cs), (133Ba-60Co), (241Am-137Cs), (241Am-60Co), (133Ba-241Am) and (60Co-137Cs)) were investigated in order to optimize the three-phase flow meter. Three phases were water, oil and gas and the regime was considered annular. The required data was numerically generated using MCNP-X code which is a Monte-Carlo code. Indeed, the present study devotes to forecast the volume fractions in the annular three-phase flow, based on a multi energy metering system including various radiation sources and also one NaI detector, using a hybrid model of artificial neural network and Jaya Optimization algorithm. Since the summation of volume fractions is constant, a constraint modeling problem exists, meaning that the hybrid model must forecast only two volume fractions. Six hybrid models associated with the number of used radiation sources are designed. The models are employed to forecast the gas and water volume fractions. The next step is to train the hybrid models based on numerically obtained data. The results show that, the best forecast results are obtained for the gas and water volume fractions of the system including the (241Am-137Cs) as the radiation source.

Simulation of a complete X-ray digital radiographic system for industrial applications.

Simulating X-ray images is of great importance in industry and medicine. Using such simulation permits us to optimize parameters which affect image's quality without the limitations of an experimental procedure. This study revolves around a novel methodology to simulate a complete industrial X-ray digital radiographic system composed of an X-ray tube and a computed radiography (CR) image plate using Monte Carlo N Particle eXtended (MCNPX) code. In the process of our research, an industrial X-ray tube with maximum voltage of 300 kV and current of 5 mA was simulated. A 3-layer uniform plate including a polymer overcoat layer, a phosphor layer and a polycarbonate backing layer was also defined and simulated as the CR imaging plate. To model the image formation in the image plate, at first the absorbed dose was calculated in each pixel inside the phosphor layer of CR imaging plate using the mesh tally in MCNPX code and then was converted to gray value using a mathematical relationship determined in a separate procedure. To validate the simulation results, an experimental setup was designed and the images of two step wedges created out of aluminum and steel were captured by the experiments and compared with the simulations. The results show that the simulated images are in good agreement with the experimental ones demonstrating the ability of the proposed methodology for simulating an industrial X-ray imaging system.

The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas-oil-water.

The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the 152Eu and 137Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results.

A novel method for flow pattern identification in unstable operational conditions using gamma ray and radial basis function.

Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques using RBF neural network in order to recognize the flow regime and determine the void fraction in gas-liquid two phase flows independent of the liquid phase changes. The proposed system is consisted of one 137Cs source, two transmission detectors and one scattering detector. The registered counts in two transmission detectors were used as the inputs of one primary Radial Basis Function (RBF) neural network for recognizing the flow regime independent of liquid phase density. Then, after flow regime identification, three RBF neural networks were utilized for determining the void fraction independent of liquid phase density. Registered count in scattering detector and first transmission detector were used as the inputs of these three RBF neural networks. Using this simple methodology, all the flow patterns were correctly recognized and the void fraction was predicted independent of liquid phase density with mean relative error (MRE) of less than 3.28%.