Bitumen-water interfacial tension modeling by using subtractive clustering method

Calculation of interfacial tension during bitumen production is a crucial issue in heavy crude oil history. Upon variation in pressure, temperature and phases composition, interfacial tension between bitumen and water change. In this work a sophisticated method called subtractive clustering was utilized to predict dynamic interfacial tension between bitumen and water. The subtractive clustering method is composed of optimized fuzzy logic algorithm. A data bank which is collected from open-source literature, is used to create a reliable model. Then the prediction accuracy of the measured dynamic interfacial tension using subtractive clustering have been examined. Results state that the comparison of measured interfacial tension and predicted interfacial tension indicate acceptable accuracy of proposed model. Also more than 90 percent of data points have less than 3 percent absolute error.     

Multivariable autopilot design for sounding rockets using intelligent Eigenstructure Assignment technique

When a sounding rocket rolls about its longitudinal axis, the role of angle of attack and sideslip angle exchange regularly. This interchange causes the aerodynamic moments to alternate between pitching and yawing moments, which, coupled with the moment of inertia effects, further aggravates the inertial cross-coupling. In this paper, first we analyze the cross-coupling phenomena and derive a Linear, Time-Invariant (LTI), multi input — multi output model of a spinning sounding rocket and then we design an autopilot for this system. An application of multivariable control technique is presented to the design of the autopilot. The involved controller is advanced by combining Eigenstructure Assignment (EA) approach with the Particle Swarm Optimization (PSO) algorithm. Results of linear and nonlinear simulations are reported to demonstrate the performance and stability margin of the considered autopilot.     

Fuzzy‐sliding mode control of nonlinear smart base‐isolated building under earthquake excitation

In this paper, the effectiveness of the fuzzy sliding mode control strategy on three‐dimensional benchmark building with smart base isolation under seismic excitation has been examined. One of the appropriate control theories for such this nonlinear system is the sliding mode control theory; discontinuous sliding mode theory has weakness such as chattering phenomena. In this paper, we used a combination of fuzzy logic and sliding mode theory for the deletion of this defect. The proposed control theory has been scrutinized by applying on lately developed nonlinear three‐dimensional base‐isolated benchmark building. This building because of the three‐dimensional nature, coalescing of lateral and torsional responses, continuity of responses of the superstructure, and base is modeled with three degrees of freedom on every floor. In this building eight actuators assigned only at the base level and in the two directions (x, y). In other words, 16 actuators are located underneath the structure. Furthermore, the base isolation system has been modeled by considering lateral coupled equations for a better examination of the performance of the system. The results indicate that reduction of control performance is remarkable. Also, utilizing proposed control theory can decrease the responses of building in two main directions and, particularly, in the rotational degree of freedom.     

Practical Implementation of Novel Robust Position Control Scheme for Synchronous Reluctance Motor

A variable structure robust position controller is presented for a three-phase synchronous reluctance motor. Linear control and variable structure control and pulse-width modulation generation are combined. These provide robust, fast, and accurate position control without the penalty of high chattering. Schemes, including conventional sliding-mode control and the proposed scheme, are tested under parameter variations, and sudden perturbations are applied to the system at a specific time, then compared. This scheme uses both advantages of traditional variable structure control methods and linear methods. The disadvantages of each method, such as chattering and parameter sensitivity, are minimized. Results demonstrate that the proposed technique preserves the classical linear position control merits, while both the steady-state and transient behavior are significantly improved in terms of robustness, accuracy, and low ripple. The results also prove that the position reference command is perfectly tracked in spite of motor parameter uncertainties and load torque disturbance in control of the system that uses the new schemes.     

Phase evolution in mechanical alloying and spark plasma sintering of AlxCoCrCuFeNi HEAs

ABSTRACT

Al_xCoCrCuFeNi high-entropy alloys were synthesised through mechanical alloying and spark plasma sintering. Different alloys were produced by varying the aluminium content (x?=?0.5, 1.5, 2.5 and 4). The influences of the milling duration on the evolution of microstructure, constituent phases and morphology were studied. Increasing milling time resulted in grain refinement and higher solid solution homogenisation characterised by a high internal strain. As a consequence of aluminium addition, the microstructure of materials evolved from face centered cubic (FCC) and body centered cubic (BCC) phases to FCC, BCC and ordered BCC phases. Both mechanical alloying and SPS conditions as well as aluminium content led to grain refinement and variations of mechanical properties. In particular, hardness increased with increasing aluminium content. The aluminium percentage and the evolution of consequent phases are responsible for the microstructural stability at high temperatures. In addition, with Al content increase, the further synergy of strength and ductility along with a more pronounced strain hardening was obtained.     

Investigation the impact of additives on the displacement of the onset point of asphaltene precipitation using interfacial tension measurement

The purpose of this study is to explore the impact of additives on the displacement of the onset point of asphaltene precipitation in crude oil using the interfacial tension measurement method, based on the examined oil has been taken from Iran reservoirs. The experimental results suggest that the addition of surfactants of dodecyl benzene sulfonic acid (DBSA) and coconut diethanolamide (CDEA) to the oil has triggered the onset of asphaltene precipitation. These findings imply that CDEA has a more effective role in preventing asphaltene precipitation. Also, it was observed that increasing the concentration of the surfactants has led to obtaining greater results. Finally, critical micelle concentration was calculated to be 5000 and 4700 ppm in the cases of CDEA and DBSA, respectively.     

Two-phase flow component fraction measurement using gamma-ray attenuation technique

Multiphase flow meters as the potential alternatives to separation and metering techniques have been in rapid development since 1980 s.Before its field operation,the instrument should be calibrated in a standard test-facility.In spite of the known medium and large scale facilities all over the world,we developed a laboratory scale instrument for component fraction measurements.It has a two-phase flow homogenizer loop with the clamp-on potential of the meters to provide a regime independent measurement.It is capable of delivering a complete homogenization by γ-ray densitometer.With an error of±5%in component fraction measurements,this instrument is appropriate for testing and calibrating other meters.     

Artificial neural networks as a basis for new generation of rock failure criteria

Conventional methods for prediction of rock strength are based on using classical failure criteria. In this study, artificial neural networks were regarded as new tools for considering the strength of intact rock in a wide range of loading condition from uniaxial tension to triaxial compression. A comprehensive data set of the values of major and minor principal stresses at failure from 1638 laboratory tests on seven rock types was collected. For each rock type, data were randomly divided into two subsets, training and test sets. Neural networks were trained using training sets to predict the value of major principal stress at failure from uniaxial compressive stress and minor principal stress. Small architecture and regularization method were adopted to avoid over-fitting problems. The same training sets were used in determining the constants of two popular empirical failure criteria, namely Bieniawski–Yudhbir and Hoek–Brown. Then, the test sets were used to examine the accuracy and generalization of the models in predicting the strength in new situations. Comparison of the results of the neural network models with those of the empirical criteria showed that the former approach always lead to less root mean squared error and higher coefficient of determination. On average, for different rock types, using ANN models led to about 30% decrease in prediction error relative to best empirical models. These models also showed better flexibility in the prediction of major principal stress at failure in both brittle and ductile failure regimes.