Applying FCM-ANFIS algorithm as a novel computational method for prediction of viscosity of bitumen and heavy alkane mixture

Recently the studies expressed that the noticeable number of oil reservoirs in all over the world are heavy oil and bitumen reservoirs. So the importance of enhancement of oil recovery (EOR) processes for heavy oil and bitumen reservoirs is highlighted. The Dilution of the reservoir fluid by solvents such as tetradecane is one of well-known methods for these types of reservoirs which effects oil recovery by decreasing viscosity. In the present study, Fuzzy c-means (FCM) algorithm was coupled with Adaptive neuro-fuzzy inference system (ANFIS) to predict viscosity of bitumen and tetradecane in terms of temperature, pressure and weight percent of tetradecane. The coefficients of determination for training and testing steps were calculated such as 0.9914 and 0.9613. The comparison of results and experimental data expressed that FCM-ANFIS algorithm has great potential for estimation of viscosity of bitumen and tetradecane.     

Investigation on the resistance spot-welded austenitic/ferritic stainless steel

In this work, the effect of weld current on joining capability of austenitic stainless steel (AISI 304) and ferritic stainless steel (AISI 430) sheets with application of resistance spot welding process was investigated. Macrostructure, microstructure, microhardness, tensile shear strength, and failure mode of welded materials were evaluated for different weld currents. The values of weld current were 2.5, 3.75, and 5 kA. It was found that when the weld current increased, the nugget size and the weld strength were increased. Two distinct failure modes including interfacial and pullout were observed during tensile shear test. Finally, an adequate weld current was obtained.     

Enhanced Dynamic Hierarchical Replication and Weighted Scheduling Strategy in Data Grid

The Data Grid provides massive aggregated computing resources and distributed storage space to deal with data-intensive applications. Due to the limitation of available resources in the grid as well as production of large volumes of data, efficient use of the Grid resources becomes an important challenge. Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner. Effective scheduling in the Grid can reduce the amount of data transferred among nodes by submitting a job to a node where most of the requested data files are available. In this paper two strategies are proposed, first a novel job scheduling strategy called Weighted Scheduling Strategy (WSS) that uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers the number of jobs waiting in a queue, the location of the required data for the job and the computing capacity of the sites Second, a dynamic data replication strategy, called Enhanced Dynamic Hierarchical Replication (EDHR) that improves file access time. This strategy is an enhanced version of the Dynamic Hierarchical Replication strategy. It uses an economic model for file deletion when there is not enough space for the replica. The economic model is based on the future value of a data file. Best replica placement plays an important role for obtaining maximum benefit from replication as well as reducing storage cost and mean job execution time. So, it is considered in this paper. The proposed strategies are implemented by OptorSim, the European Data Grid simulator. Experiment results show that the proposed strategies achieve better performance by minimizing the data access time and avoiding unnecessary replication.     

Application of energies of optimal frequency bands for fault diagnosis based on modified distance function

Low-dimensional relevant feature sets are ideal to avoid extra data mining for classification. The current work investigates the feasibility of utilizing energies of vibration signals in optimal frequency bands as features for machine fault diagnosis application. Energies in different frequency bands were derived based on Parseval's theorem. The optimal feature sets were extracted by optimization of the related frequency bands using genetic algorithm and a Modified distance function (MDF). The frequency bands and the number of bands were optimized based on the MDF. The MDF is designed to a) maximize the distance between centers of classes, b) minimize the dispersion of features in each class separately, and c) minimize dimension of extracted feature sets. The experimental signals in two different gearboxes were used to demonstrate the efficiency of the presented technique. The results show the effectiveness of the presented technique in gear fault diagnosis application.     

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.     

Combination of supercritical fluid extraction with dispersive liquid-liquid microextraction for extraction of organophosphorus pesticides from soil and marine sediment samples

Supercritical fluid extraction (SFE) coupled with dispersive liquid-liquid microextraction (DLLME) and followed by gas chromatography-flame ionization detection (GC-FID) was applied for extraction and determination of ultra-trace amounts of seven organophosphorus pesticides (OPPs) (o,o,o-triethyl phosphorothioate, thionazin, sulfotepp, disulfoton, methyl parathion, parathion, and famphur) in soil and marine sediment samples. Supercritical CO₂ at 150 bar, 60 °C, 10 min static and 30 min dynamic extraction times was used to extract the pesticides. The extracts were collected in 1.0 mL of acetonitrile. Seventeen μL of carbon tetrachloride was dissolved in the collecting solvent and the mixture was then injected rapidly into 5.0 mL of aqueous solution. About (5.0 ± 0.2 μL) of sediment phase was collected after centrifuging and finally 2.0 μL of it was injected into gas chromatography (GC) injection port for analyses. The extraction recoveries for the target analytes were in the range of 44.4% and 95.4% and relative standard deviation (RSD%) for four-replicate measurements was below 7.5%. The limit detections of the method for determining the pesticides were in the range of 0.001-0.009 mg kg⁻¹. The method was successfully applied for analysis of OPPs in real soil and marine sediment samples and satisfactory results were obtained.     

Inverse Identification of Thermal Properties of Charring Ablators

The modified Levenberg-Marquardt method is used for simultaneous estimation of decomposition kinetic coefficients and temperature-dependent thermophysical properties of charring ablators with a moving boundary over a wide temperature range. No prior information is used for the functional forms of the unknown thermal conductivity and specific heat. The procedure used differs from the traditional one in that it does not require prescribed time-dependent surface heat flux, recession rate, and pyrolysis gas mass flow rate. These time-dependent quantities may recover during an iterative procedure. The measured temperatures are simulated numerically by the Charring material ablation code, which accounts for unsteady ablation. The method can determine unknown parameters in an efficient manner with reasonable accuracy, without exact advance knowledge about the net surface heat flux, surface recession, and gas flux through the material.     

Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks

Recently, directional sensor networks have received a great deal of attention due to their wide range of applications in different fields. A unique characteristic of directional sensors is their limitation in both sensing angle and battery power, which highlights the significance of covering all the targets and, at the same time, extending the network lifetime. It is known as the target coverage problem that has been proved as an NP-complete problem. In this paper, we propose four learning automata-based algorithms to solve this problem. Additionally, several pruning rules are designed to improve the performance of these algorithms. To evaluate the performance of the proposed algorithms, several experiments were carried out. The theoretical maximum was used as a baseline to which the results of all the proposed algorithms are compared. The obtained results showed that the proposed algorithms could solve efficiently the target coverage problem.     

Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir

Determination of petrophysical parameters by using available data has a specific importance in exploration and production studies for oil and gas industries. Modeling of corrected permeability as a petrophysical parameter can help in decision making processes. The objective of this study is to construct a comprehensive and quantitative characterization of a carbonate gas reservoir in marine gas field. Artificial neural network is applied for prediction of permeability in accordance with other petrophysical parameters at well location. Correlation coefficient for this method is 84 %. In the study, the geological reservoir model is developed in two steps: First, the structure skeleton of the field is constructed, and then, reservoir property is distributed within it by applying new stochastic methods. Permeability is modeled by three techniques: kriging, sequential Gaussian simulation (SGS) and collocated co-simulation using modeled effective porosity as 3D secondary variable. This paper enhances the characterization of the reservoir by improving the modeling of permeability through a new algorithm called collocated co-simulation. Kriging is very simple in modeling the reservoir permeability, and also, original distribution of the data changes considerably in this model. In addition, the SGS model is noisy and heterogeneous, but it retains the original distribution of the data. However, the addition of a 3D secondary variable in third method resulted in a much more reliable model of permeability.     

Proposed Relationship for Proper Shear Strength of Elliptical Damper Based on Its Geometrical Parameters

Vertical link beam as a practical yielding damper is a kind of passive control device which dissipates seismic energy and thus reduces the rate of damage to structures through in-plane shear deformations. Proper stiffness, ductility and significant energy dissipation of these dampers together with their practical advantages justify their widespread experimental and numerical research. In this paper, a total of 42 elliptical dampers (vertical link beams with elliptical cross sections) in various lengths and thicknesses are modeled using the ABAQUS finite element software and finally a design relationship for this damper is presented based on the above mentioned geometrical parameters using pushover analysis. The accuracy of the proposed relationship is studied by applying various layouts of designed dampers on two types of chevron bracings to improve their performance and obtain proper energy dissipation without brace buckling. The equivalent viscous damping ratios of 32–33% obtained for elliptical dampers indicate high increase in energy dissipation capability.