Ehsan Eftekhari Zadeh
Abstract: Two-phase flow is very important in many areas of science, engineering, and industry. Two-phase flow comprising gas and liquid phases is a common occurrence in oil and gas related industries. This study considers three flow regimes, including homogeneous, annular, and stratified regimes ranging from 5-90% of void fractions simulated via the Mont Carlo N-Particle (MCNP) Code. In the proposed model, two NaI detectors were used for recording the emitted photons of a cesium 137 source that pass through the pipe. Following that, fast Fourier transform (FFT), which aims to transfer recorded signals to frequency domain, was adopted. By analyzing signals in the frequency domain, it is possible to extract some hidden features that are not visible in the time domain analysis. Four distinctive features of registered signals, including average value, the amplitude of dominant frequency, standard deviation (STD), and skewness were extracted. These features were compared to each other to determine the best feature that can offer the best separation. Furthermore, artificial neural network (ANN) was utilized to increase the efficiency of two-phase flowmeters. Additionally, two multi-layer perceptron (MLP) neural networks were adopted for classifying the considered regimes and estimating the volumetric percentages. Applying the proposed model, the outlined flow regimes were accurately classified, resulting in volumetric percentages with a low root mean square error (RMSE) of 1.1%.
Abstract: Scale deposition is the accumulation of various materials in the walls of transmission lines and unwanted parts in the oil and gas production system. It is a leading moot point in all transmission lines, tanks, and petroleum equipment. Scale deposition leads to drastic detrimental problems, reduced permeability, pressure and production losses, and direct financial losses due to the failure of some equipment. The accumulation of oil and gas leads to clogged pores and obstruction of fluid flow. Considering the passage of a two-phase flow, our study determines the thickness of the scale, and the flow regime is detected with the help of two Multilayer Perceptron (MLP) networks. First, the diagnostic system consisting of a dual-energy source, a steel pipe, and a NaI detector was implemented, using the Monte Carlo N Particle Code (MCNP). Subsequently, the received signals were processed, and properties were extracted using the wavelet transform technique. These features were considered as inputs of an Artificial Neural Network (ANN) model used to determine the type of flow regimes and predict the scale thickness. By accurately classifying the flow regimes and determining the scale inside the pipe, our proposed method provides a platform that could enhance many areas of the oil industry.
Abstract: We report here on the results of comparative experimental measurements of laser energy absorption in a bulk and different morphology nanowire arrays interacting with relativistically intense, ultra-high temporal contrast femtosecond laser pulses. We compare polished, flat bulk samples with vertically and randomly oriented nanowires made of ZnO semiconductor material. The optical absorption of the 45° incident laser pulses of ∼40 fs duration with a central wavelength of 400 nm at intensities above 1019Wcm2 was determined using an integrating Ulbricht sphere. We demonstrate an almost twofold enhancement of absorption in both nanowire morphologies with an average of (79.6±1.9)% in comparison to the flat bulk sample of (45.8±1.9)%. The observed substantially enhanced absorption in nanowire arrays is also confirmed by high-resolution x-ray emission spectroscopy. The spectral analysis of the K-shell x-ray emission lines revealed that the He-like resonance line emission from highly ionized Zn (Zn28+) is only present in the case of nanowire arrays, whereas, for the flat bulk samples, only neutral and low charge states were observed. Our numerical simulations, based on radiative-collisional kinetic code FLYCHK, well reproduce the measured He-like emission spectrum and suggest that high charge state observed in nanowire arrays is due to substantially higher plasma temperature. Our results, which were measured for the first time with femtosecond laser pulses, can be used to benchmark theoretical models and numerical codes for the relativistic interaction of ultrashort laser pulses with nanowires.
Abstract: Scale deposits can reduce equipment efficiency in the oil and petrochemical industry. The gamma attenuation technique can be used as a non-invasive effective tool for detecting scale deposits in petroleum pipelines. The goal of this study is to propose a dual-energy gamma attenuation method with radial basis function neural network (RBFNN) to determine scale thickness in petroleum pipelines in which two-phase flows with different symmetrical flow regimes and void fractions exist. The detection system consists of a dual-energy gamma source, with Ba-133 and Cs-137 radioisotopes and two 2.54-cm x 2.54-cm sodium iodide (NaI) detectors to record photons. The first detector related to transmitted photons, and the second one to scattered photons. The transmission detector recorded two signals, which were the counts under photopeak of Ba-133 and Cs-137 with the energy of 356 keV and 662 keV, respectively. The one signal recorded in the scattering detector, total counts, was applied to RBFNN as the inputs, and scale thickness was assigned as the output.