Sharif Digital Repository

We have developed a fourth order compact finite volume method for the solution of low Mach number compressible flow equations on arbitrary nonuniform grids. The formulation presented here uses collocated grid that preserves fourth order accuracy on nonuniform meshes. This was achieved by introduction of a new fourth order method for calculation of cell and face averaged metrics. A special treatment of nonlinear terms is used to guarantee the stability of the fourth order compact method. Moreover an approach for applying this method to multi-block domains is presented for complicated geometries and parallel processing applications. Several test cases including the flow in a lid-driven cavity,... 

Using an array of coupled oscillators, a novel broadband radar signalling scheme is introduced. It is shown that the collective output of the oscillator array is a flexible signal that can be matched to a target at a specific aspect. Also, a new robust algorithm based on eigenvectors of the correlation matrix is introduced, by which the generated signal can be matched to the target over a limited range of aspects. This capability is important, because radars are unable to estimate the aspect of a real target precisely. In this new approach to radar target identification, after estimation of the approximate aspect of an unknown target, a variety of waveforms matched to different potential... 

Turbulence–chemistry interaction models such as the conditional moment closure and various flamelet models require a presumed Probability Density Function (PDF) model of the conditioning variables. In turbulent stratified flames, a joint-PDF model for a reaction progress variable and the mixture fraction is required. The joint-PDF is often modelled by two beta functions using the first and second moments of the two conditioning variables. In this work, the performance of a joint-PDF model based on the flamelet PDF approach is compared to the double beta-PDF model. The conditional PDF of the reaction progress variable conditioned on mixture fraction is modelled with the flamelet PDF. Unlike... 

A new method of spatial filtering in high-order finite volume methods is presented and assessed. The base of this method is to filter face-averaged variables (fluxes) and then the recovery of cell-averaged ones. Two kinds of filtering method are proposed. The first kind is highly dissipative and appropriate for the numerical regions that need high dissipation, e.g. sponge zones. The second kind, on the other hand, is a precise method and hence is suitable for applying the high-order finite difference filters to the finite volume methods. Applying high-order finite difference filters directly to the high-order finite volume methods without using the proposed method causes stability problems... 

This paper presents a particle swarm optimization (PSO) based fuzzy stochastic long term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level, and reduction in voltage deviation are simultaneously considered as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other, then they are introduced to a PSO... 

Motivated by the recently discovered time-reversal symmetry-breaking superconductivity in the epitaxial Bi/Ni bilayer system with transition temperature Tc≈4.2K and the observation of a zero-bias anomaly in a background of gap-filling states in tunneling measurements, we show that gap-filling states can appear in the fully gapped dxy±idx2-y2 superconducting states. We consider a model of helical electron states with d-wave pairing. In particular, we show that both magnetic and nonmagnetic impurities can create states within the superconducting gap. Alternatively, we show that the coupling of the electron spins to the in-plane Zeeman field provided by nickel can also create gap-filling states... 

In this work, we study interacting electrons on a square lattice in the presence of strong Rashba spin-orbit interaction. The spin-orbit term forces the time-reversal electron states to be paired in even Cooper channels. For concreteness, we only consider the repulsive onsite Hubbard and nearest-neighbor Coulomb interactions, the so-called extended Hubbard model. To examine the superconducting instability, we obtain the effective interaction between electrons within the random phase approximation and treat the pairing instabilities driven by charge and spin fluctuations and their combined effects. We mapped out the phase diagram of the model in terms of interactions and electron fillings and... 

In this article, smoothed particle hydrodynamics method is applied in order to study the free surface flow generated by two-dimensional planing flat plate. For this purpose, a two-dimensional smoothed particle hydrodynamics code is developed and validated by the well-known dam breaking problem. Four trim angles and three different velocities are considered to perform a parametric study to examine their physical effects. The obtained results from smoothed particle hydrodynamics are compared against the corresponding Reynolds-averaged Navier Stokes solutions. It is observed that at lower velocities, there exists a good agreement between the smoothed particle hydrodynamics and Reynolds-averaged... 

The purpose of this study is to investigate separation performance of poly(ether-b-amide6) (Pebax1657)/glycerol triacetate (GTA) gel membranes for CO2 removal from H2, N2 and CH4. GTA as a low molecular weight and highly CO2-phill compound was added to membrane structure at various weight fractions, 20%, 40%, 60% and 80% of Pebax, to fabricate a new high solubility selective membrane with improved performance. Permeation of pure gases was studied at different temperatures from 25 to 65°C and pressures from 4 to 24bar and ideal selectivities were calculated. Results indicated enhancement in permeation for all tested gases. For example, at a pressure of 4bar and a temperature of 25°C, membrane... 

Here, an excellent superhydrophobic and highly oleophobic nanocomposite coating composed of fluoroethylene-vinyl ether (FEVE) resin as a matrix for modified SiO2 nanoparticles was synthesized on a stainless-steel wire mesh substrate via a facile sol-gel method. The surface morphology, microstructure, composition, and roughness of the coatings were investigated by field emission scanning electron microscopy (FESEM) equipped with energy-dispersive spectroscopy (EDS) and atomic force microscopy (AFM). The most efficient coating with superhydrophobicity and high oleophobicity feature indicates the water and oil repellency with contact angles (CAs) of 152° and 141°, respectively, with the high... 

Employing deep learning makes it possible to learn high-level features from raw data, resulting in more precise models. On the other hand, submodularity makes the solution scalable and provides the means to guarantee a lower bound for its performance. In this paper, a deep submodular network (DSN) is introduced, which is a deep network meeting submodularity characteristics. DSN lets modular and submodular features to participate in constructing a tailored model that fits the best with a problem. Various properties of DSN are examined and its learning method is presented. By proving that cost function used for learning process is a convex function, it is concluded that minimization can be... 

Abstractive multi-document summarization is a type of automatic text summarization. It obtains information from multiple documents and generates a human-like summary from them. In this paper, we propose an abstractive multi-document summarization method called HMSumm. The proposed method is a combination of extractive and abstractive summarization approaches. First, it constructs an extractive summary from multiple input documents, and then uses it to generate the abstractive summary. Redundant information, which is a global problem in multi-document summarization, is managed in the first step. Specifically, the determinantal point process (DPP) is used to deal with redundancy. This step... 

The increase in online text generation by humans and machines needs automatic text summarization systems. Recent research studies commonly use deep learning, besides sentence embedding and feature learning mechanisms, to find a solution for text summarization. But they ignore the fact that while finding the optimal solution for extractive text summarization is NP-hard, how do they ensure the quality of their solution? In our previous work, an extractive summarizer, called DSNSum, was proposed based on deep submodular network (DSN) that uses handcrafted features. It leverages submodularity to guarantee a minimum bound for performance. In this paper, submodular graph convolutional summarizer... 

River water quality monitoring using traditional water sampling and laboratory analyses is expensive and time-consuming. The application of artificial neural network (ANN) models to simulate water quality parameters is cost-effective, quick, and reliable. This study provides two methods of mathematical and ANN modeling to simulate and forecast five important river water quality indicators (DO, TDS, SAR, BOD5, HCO3) correlated with variables such as EC, temperature, and pH which can be measured easily and almost with no cost. The mathematical method is based on polynomial fitting with least square method and the neural network model was developed using a feed-forward algorithm. The 35 years’... 

The separation of water-oil mixtures has attracted widespread attention because of the increasing amounts of oily wastewater produced from the daily activities of humans and different industrial processes. Therefore, the development of facile and efficient oil-water separation technologies is imperative. In this work, a new highly superhydrophilic-superoleophobic coated stainless steel mesh was fabricated using virtue of the surface modification of poly (BzVimBr-Vim)@PFOA@SiO2 nanoparticles (NPs) through a facile preparation process. The new fabricated superhydrophilic and highly oleophobic coating exhibits good adhesive properties. The oil contact angle (OCA) and water contact angle (WCA)... 

The design and development of efficient approaches for water–oil separation have had widespread interest. Most previously introduced techniques and materials used for development of the successful separation of oily wastewater could not answer all the desired demands, such as being efficient and environmentally and economically friendly. Therefore, in seeking a novel method capable of answering these expectations, surfaces with special wettability were introduced. A novel, reusable, and recyclable superhydrophilic and superoleophobic poly(Vsim-Vim)@PFOA@SiO2 nanocomposite-coated stainless steel mesh was synthesized through a facile preparation process. Since the most important factors of... 

The design and development of efficient approaches for water–oil separation have had widespread interest. Most previously introduced techniques and materials used for development of the successful separation of oily wastewater could not answer all the desired demands, such as being efficient and environmentally and economically friendly. Therefore, in seeking a novel method capable of answering these expectations, surfaces with special wettability were introduced. A novel, reusable, and recyclable superhydrophilic and superoleophobic poly(Vsim-Vim)@PFOA@SiO2 nanocomposite-coated stainless steel mesh was synthesized through a facile preparation process. Since the most important factors of... 

Recently, tree structures have become a popular way for storing and manipulating huge amount of data. Classification of these data can facilitate storage, retrieval, indexing, query answering and different processing operations. In this paper, we present C-Classifier and M-Classifier algorithms for rule based classification of tree structured data. These algorithms are based on extracting especial tree patterns from training dataset. These tree patterns, i.e. closed tree patterns and maximal tree patterns are capable of extracting characteristics of training trees completely and non-redundantly. Our experiments show that M-Classifier significantly reduces running time and complexity. As...