This paper presents a new, efficient and robust Three-phase load flow for unbalanced distribution systems. The proposed method uses equivalent current injections, based on Newton-Raphson method. The load flow issue is considered as an optimization problem and is decoupled into two sub problems without using any assumptions. The Jacobian matrix is set to be constant, thus, the method is fast, robust and efficient. The proposed method has been tested on the IEEE 13 Bus Radial Distribution Test Feeder with satisfactory results.

Virtual power plants (VPPs) have gained significant attention in recent years as a promising solution for optimizing the operation of power systems. VPPs allow for the integration and coordination of Distributed Energy Resources (DERs), such as renewable energy sources, to provide grid support and stabilize the power supply. This paper presents a VPP that utilizes a Three-phase power flow analysis for its modeling. This approach allows for an accurate representation of the power flow in the VPP, considering the dynamic nature of the power system. In addition to the Three-phase power flow analysis, the authors propose a tabu continuous ant colony search (TCACS) to optimize the operation of the VPP, which combines tabu search and ant colony optimization to find near-optimal solutions for complex optimization problems. It uses a tabu list to guide the search towards promising solutions while avoiding getting stuck in local minima. The performance of the proposed VPP has been evaluated through simulation studies, and the results show that it is effective at minimizing power loss and improving the stability of the power system. The proposed VPP can be a valuable tool for utilities to manage their power generation and distribution, particularly in the context of increasing renewable energy integration.

One of the challenging problems in the oil and gas industry is accurate and reliable multiphase flow rate measurement in a Three-phase flow. The application of methods with minimized uncertainty is required in the industry. Previously developed correlations for two-phase flow are complex and not capable of Three-phase flow. Hence phase behavior identification in different conditions of designing and modeling of Three-phase flow is important. Numerous laboratory and theoretical studies have been done to describe the Venturi multiphase flow meter in both horizontal and vertical flow. However, it is not possible to select the measurement devices for all similar conditions. In this study, a new venturi model is developed to implement in Simulink/Matlab for predicting the mass flow rate of gas, water, and oil. This model is simple and semi-linear. Several classified configurations of Three-phase flow are simulated using computational fluid dynamics analysis to get hydrodynamics parameters of the flows to use as inputs of the model. The obtained data is used as a test and train data in the least squares support vector machine (LSSVM) algorithm. The pressure drop and mass flow rate of gas, oil, and water are calculated with the LSSVM method. Two tuning parameters of LSSVM, namely γ and 𝜎 2, are obtained as 1150954 and 0. 4384, 53. 9199 and 0. 18163, 8. 8714 and 0. 14424, and 1003913. 2214 and 0. 74742 for the pressure drop, the mass flow rate of oil, gas mass flow rate, and the water mass flow rate, respectively. Developed models are found to have an average relative error of 5. 81%, 6. 31%, and 2. 58% for gas, oil, and water, respectively.

The used metering technique in this study is based on the dual energy (Am-241 and Cs-137) gamma ray attenuation. Two transmitted NaI detectors in the best orientation were used and four features were extracted and applied to the model. This paper highlights the application of Adaptive Neuro-fuzzy Inference System (ANFIS) for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems. In fact, the aim of the current study is to recognize the flow regimes based on dual energy broad-beam gamma-ray attenuation technique using ANFIS. In this study, ANFIS is used to classify the flow regimes (annular, stratified, and homogenous) and predict the value of volume fractions. To start modeling, sufficient data are gathered. Here, data are generated numerically using MCNPX code. In the next step, ANFIS must be trained. According to the modeling results, the proposed ANFIS can correctly recognize all the Three different flow regimes, and other ANFIS networks can determine volume fractions with MRE of less than 2% according to the recognized regime, which shows that ANFIS can predict the results precisely.

Underbalanced drilling as multiphase flow is done in oil drilling operation in low pressure reservoir or highly depleted mature reservoir. Correct determination of the pressure loss of Three phase fluids in drilling annulus is essential in determination of hydraulic horsepower requirements during drilling operations. In this paper the pressure loss of solid-gas-liquid Three-phase fluids flow in inclined annulus was estimated using artificial neural network (ANN). Experimental data which are available in the literature were used for design of ANN. Pressure loss as output of ANN, was estimated from five effective parameters as inputs of ANN including gas and liquid superficial velocities, the inclination from horizontal, rate of penetration (ROP), pipe rotation speed (RPM). The correlation coefficient between predicted and experimental value for train and test data is 0.998 and 0.997 respectively. The root mean square error (RMS) and average absolute percent relative error (AAPE) for train data are 0.0082 and 2.77% and for test data, they are 0.0108 and 3.68 % respectively. The reliable results showed the high ability of artificial neural network for estimating pressure loss of Three phase flow in annulus.

Hydrodynamic of a turbulent impinging jet on a flat plate is studied experimentally and numerically. Experiments are conducted for the Reynolds number range of 72000 to 102000 and a fixed jet-to-plate dimensionless distance of H/d=3. 5. Based on the experimental setup, a multi-phase numerical model is simulated to predict the flow properties of impinging jets using two turbulent models. Mesh-independency of the numerical model is studied to ensure the preciseness of the results. Numerical and experimental forces on the target plate are compared in order to examine the performance of turbulent models and wall functions. As a result, the force obtained by the Reynolds stress turbulent model alongside with non-equilibrium wall function is in good agreement with the experiment. The correlation equations are obtained for predicting the water thickness over the target plate and impingement force versus Reynolds number. It is also indicated that the maximum shear stress on the target plate is located at radial dimensionless distance of r/d=0. 75.

An annular venturi injector (AVI) was proposed to form an intermittent flow structure in airlift pump for a good pump performance. Experiments were conducted to investigate the performance of the airlift pump with this AVI by comparing with pump performance with traditional injectors, at a series of air flow rates. It was found that airlift pump with AVI had higher flow rates of output liquid and particle, than those with the traditional injectors. This AVI promoted the gas core to collapse and formed an intermittent flow structure in rising pipe. For this intermittent structure, its slug length, firstly increased to a maximal value with increasing gas flow rate and then remained stable even under a high gas flow rate, while its slug frequency decreased with gas flow rate and then remained to a minimal value under a high gas flow rate.