Estimation of void fraction for homogenous regime of two-phase flows in unstable operational conditions using gamma-ray and neural networks

 Almost all the multi-phase flow meters (MPFMs) using gamma-ray attenuation, are calibrated for liquid and gas phases with constant density and pressure. When operational conditions such as temperature and pressure change in pipelines, the radiation-based multi-phase flowmeters would measure the flow rate with error. Therefore, performance of MPFMs would be improved by eliminating any dependency on the fluid properties such as density. In this work, a method based on dual modality densitometry combined with Artificial Neural Network (ANN) is proposed in order to estimate the void fraction in homogenous regime of gas-liquid two-phase flows in unstable operational conditions (changeable temperature and pressure) in oil industry. An experimental setup was implemented to generate the optimum required input data for training the network. ANNs were trained on the registered counts of the transmission and scattering detectors in various liquid phase densities and void fractions. Void fractions were predicted by ANNs with mean relative error of less than 0.78% in density variations range of 0.735 up to 0.98 g/cm

Precise prediction of radiation interaction position in plastic rod scintillators using a fast and simple technique: Artificial neural network

Precise prediction of the radiation interaction position in scintillators plays an important role in medical and industrial imaging systems. In this research, the incident position of the gamma rays was predicted precisely in a plastic rod scintillator by using attenuation technique and multilayer perceptron (MLP) neural network, for the first time. Also, this procedure was performed using nonlinear regression (NLR) method. The experimental setup is comprised of a plastic rod scintillator (BC400) coupled with two PMTs at two sides, a 60Co gamma source and two counters that record count rates. Using two proposed techniques (ANN and NLR), the radiation interaction position was predicted in a plastic rod scintillator with a mean relative error percentage less than 4.6% and 14.6%, respectively. The mean absolute error was measured less than 2.5 and 5.5. The correlation coefficient was calculated 0.998 and 0.984, respectively. Also, the ANN technique was confirmed by leave-one-out (LOO) method with 1% error. These results presented the superiority of the ANN method in comparison with NLR and the other methods. The technique and set up used are simpler and faster than other the previous position sensitive detectors. Thus, the time, cost and shielding and electronics requirements are minimized and optimized. Keywords: Radiation interaction position, Plastic rod scintillator, Position sensitive detector, Artificial neural network, Nonlinear regressio

Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation

AbstractVoid fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas–liquid two-phase flows by using γ-ray attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam γ-ray attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly) were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%

The association between cardiac physiology, acquired brain injury, and postnatal brain growth in critical congenital heart disease

ObjectiveTo assess the trajectory of perioperative brain growth in relationship to cardiac diagnosis and acquired brain injuries.MethodsThis was a cohort study of term neonates with hypoplastic left heart syndrome (HLHS) and d-transposition of the great arteries (d-TGA). Subjects underwent magnetic resonance imaging of the brain pre- and postoperatively to determine the severity of brain injury and total and regional brain volumes by the use of automated morphometry. Comparisons were made by cardiac lesion and injury status.ResultsA total of 79 subjects were included (49, d-TGA; 30, HLHS). Subjects with HLHS had more postoperative brain injury (55.6% vs 30.4%, P&nbsp;=&nbsp;.03) and more severe brain injury (moderate-to-severe white matter [WM] injury, P&nbsp;=&nbsp;.01). Total and regional perioperative brain growth was not different by brain injury status (either pre- or postoperative). However, subjects with moderate-to-severe WM injury had a slower rate of brain growth in WM and gray matter compared with those with no injury. Subjects with HLHS had a slower rate of growth globally&nbsp;and in WM and deep gray matter as compared with d-TGA (total brain volume: 12&nbsp;cm3/wk vs 7&nbsp;cm3; WM: 2.1&nbsp;cm3/wk vs 0.6&nbsp;cm3; deep gray matter: 1.5&nbsp;cm3/wk vs 0.7&nbsp;cm3; P&nbsp;&lt;&nbsp;.001), after we adjusted for gestational age at scan and the presence of brain injury. This difference remained after excluding subjects with moderate-to-severe WM injury.ConclusionsNeonates with HLHS have a slower rate of global and regional brain growth compared with d-TGA, likely related to inherent physiologic differences postoperatively. These findings demonstrate the complex interplay between cardiac lesion, brain injury, and brain growth