Multi-grid Beam and Warming scheme for the simulation of unsteady flow in an open channel

In this paper, a multi-grid algorithm is applied to a large-scale block matrix that is produced from a Beam and Warming scheme. The Beam and Warming scheme is used in the simulation of unsteady flow in an open channel. The Gauss-Seidel block-wise iteration method is used for a smoothing process with a few iterations. It is also shown that the governing equations determine the type of prolongation and restriction operators for the multi-grid algorithm

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery

Predictive factors for loneliness in female high school students; an unvariate and multivariate logistic regression analysis

Background and aims: Loneliness typically includes anxious feelings. It is particularly relevant to adolescence period. It has effect on physical and mental health. The present study aimed to identify the predictive factors of loneliness among high schools female students. Methods: A cross –sectional survey was carried out among high schools female students in Ilam during the academic year 2014-15. Sampling was done by multistage method. The student's consent to participation in the study obtained by full filled the questionnaires. Data were collected by demographic and University of California, Los Angeles questionnaire. Questionnaires with incomplete information were excluded. The Cronbach’s alpha coefficient was measured as an index of internal identicalness of the questionnaire to verify its reliability. Results: A total 400 female high school students were studied. Overall, 62.8 of students put into non- loneliness group and 37.3 of all have loneliness. The univariate logistic regression analysis demonstrates that education field, father’s education and father’s occupation were different between the groups (P < 0.05). The risk of loneliness was higher in students with a mathematical sciences education field in comparison to general education field (OR= 1.75). In multivariate logistic regression analysis the education field, father’s education and father’s occupation were considered as independent predictive variables for female students’ loneliness. The AUROC criterion was applied to compute both the sensibility and the specificity of the manikin. The overall percent of correct classification of the model is 64. Conclusion: Identify the causes of students loneliness can prevent complications and provide appropriate solutions

Pore-Facies as a tool for incorporation of small scale dynamic information in integrated reservoir studies

In this study, the quantification and incorporation of pore geometry, a qualitative parameter, and a source of dynamic information, will be demonstrated in the integrated reservoir studies. To quantify pore geometry, mercury injection capillary pressure (MICP) curves have been exploited. For each MICP curve, 20 parameters were derived and multi-resolution graph-based clustering was applied to classify the curves into nine representative distinct clusters. The number of clusters was determined based on petrography and cluster analysis. The quantified pore geometry in terms of discrete variable has been called pore-facies, and like electro-facies and litho-facies could be used in facies modelling and rock typing phases of an integrated study. The dependence of dynamic reservoir rock properties on pore geometry makes the pore-facies an interesting and powerful approach for incorporation of small-scale dynamic data into a reservoir model. A comparison among various facies definitions proved that neither litho-facies nor electro-facies is capable of characterizing dynamic rock properties, and the best results were achieved by the pore-facies method. Based on this study, it is recommended that for facies analysis in reservoir modelling, methods based on pore characteristics such as pore-facies, introduced in this paper, be used rather than traditional facies that rely on matrix properties. The next generation of the reservoir models which incorporate pore-facies-based rock types will provide a basis for more accurate static and dynamic models, a narrower range of uncertainty in the models, and a better prediction of reservoir performance

A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf

Normalized oil content (NOC) is an important geochemical factor for identifyingpotential pay zones in hydrocarbon source rocks. The present study proposes an optimaland improved model to make a quantitative and qualitative correlation between NOC andwell log responses by integration of neural network training algorithms and thecommittee machine concept. This committee machine with training algorithms (CMTA)combines Levenberg-Marquardt (LM), Bayesian regularization (BR), gradient descent(GD), one step secant (OSS), and resilient back-propagation (RP) algorithms. Each ofthese algorithms has a weight factor showing its contribution in overall prediction. Theoptimal combination of the weights is derived by a genetic algorithm. The method isillustrated using a case study. For this purpose, 231 data composed of well log data andmeasured NOC from three wells of South Pars Gas Field were clustered into 194modeling dataset and 37 testing samples for evaluating reliability of the models. Theresults of this study show that the CMTA provides more reliable and acceptable resultsthan each of the individual neural networks differing in training algorithms. Also CMTAcan accurately identify production pay zones (PPZs) from well logs

A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran

Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher gamma-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into eighty-seven training sets to build the CMIS model and thirty-seven testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC

Petrophysical data prediction from seismic attributes using committee fuzzy inference system

This study presents an intelligent model based on fuzzy systems for making aquantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (Sw) and porosity, are predicted from seismic attributes using various Fuzzy Inference Systems (FIS), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a Committee Fuzzy Inference System (CFIS) is constructed using a hybrid Genetic Algorithms-Pattern Search (GA-PS) technique. The inputs of the CFIS model are the output averages of theFIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a Probabilistic Neural Network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method

Appraisal of intra-reservoir barriers in the Permo-Triassic successions of the Central Persian Gulf, Offshore Iran

Owing to their tightness, intra reservoir barriers have the potential to prevent homogenization of reservoir fluids and so cause compartmentalization. Identification of these barriers is an important step during reservoir evaluation. In order to achieve this, three main approaches: i) detailed petrographic and core analysis, ii) petrophysical studies (flow unit concept) and iii) geochemical analysis (strontium residual salt analysis) were applied systematically in the Permo-Triassic carbonate reservoirs (Dalan and Kangan formations) of a supergiant gas reservoir located in the Central Persian Gulf. Integration of these approaches has led to a fullclarification of the intra reservoir barriers. Petrographic examinations revealed the potential stratigraphic barriers to fluids flow created by various depositional/ diagenetic characteristics. Petrophysical data such as poroperm values, pore throat size distribution and scanning electron microscopy (SEM) analysis were used to differentiate the reservoir flow units from non-reservoir rock. According to different trends in 87Sr/86Sr ratios of residual salts, the existence of flow barriers was evaluated and proved. Finally, by integrating these approaches, three intra reservoir barriers were introduced in the studied reservoir interval. These intra reservoir barriers are depositional and diagenetic in nature and are located in stratal positions with sequence stratigraphic significance. The possibility of reservoir compartmentalization was evaluated in the studied wells, and then their existence was predicted at the adjacent fields. As shown in this study, integration of petrographic examinations with flow unit determination in a sequence stratigraphic framework has the potential for recognizing intra reservoir barriers and predicting compartmentalization of the studied Permo-Triassic reservoirs

Molecular Dynamics Insights into the Structural and Water Transport Properties of a Forward Osmosis Polyamide Thin-Film Nanocomposite Membrane Modified with Graphene Quantum Dots

An approach combining molecular dynamics (MD) simulations and laboratory experiments was applied to provide new theoretical insights into the chemical structure of polyamide (PA) thin-film composite (TFC) membranes modified with graphene quantum dots (GQDs). Interaction energies, fractional free volumes, mean-square displacements, densities, and water diffusion coefficients were computed for PA and four likely chemical structures of the GQD-embedded PA membranes. These theoretical results aided with experimentally measured water fluxes allowed for determining the most likely structure of the GQD-PA membrane. The compatibility of the GQDs and PA chains was found to be due to the formation of hydrogen and covalent bonds to m-phenylenediamine units. The modified membrane has a higher water diffusivity but a lower overall free volume, compared to the pristine PA membrane. MD simulations in concert with laboratory experiments were found to provide a good understanding of the relationship between the microscopic characteristics and macroscopic transport properties of TFC membranes

A Heat Exchanger Reactor Equipped with Membranes to Produce Dimethyl Ether from Syngas and Methyl Formate and Hydrogen from Methanol

The energy crisis of the century is a motivation to present processes with higher energy efficiency for production of clean and renewable resources of energy. Hence, a catalytic heat exchanger reactor for production of dimethyl ether (DME) from syngas, and hydrogen and methyl formate (MF) from methanol is investigated in the present study. The proposed configuration is equipped with two different membranes for in-situ separation of products. Syngas is converted to DME through an exothermic reaction and it supplies a part of required energy for the methanol dehydrogenation reaction. Produced water in the exothermic side and produced hydrogen in the endothermic side are separated by using appropriate perm-selective membranes. In-situ separation of products makes the equilibrium reactions proceed toward higher conversion of reactants. A mathematical model based on reasonable assumptions is developed to evaluate molar and thermal behavior of the configuration. Performance of the system is aimed to enhance by obtaining optimum operating conditions. In this regard, Genetic Algorithm is applied. Performance of the heat exchanger double membrane reactor working under optimum conditions (OTMHR) is compared with a heat exchanger reactor without membrane (THR). OTMHR promotes methanol conversion to MF to %87.2, carbon monoxide conversion to %95.8 and hydrogen conversion to %64.6