Sharif Digital Repository

The connectivity between two points in porous media is important for evaluating hydrocarbon recovery in underground reservoirs or toxic migration in waste disposal. For example, the connectivity between a producer and an injector in a hydrocarbon reservoir impact the fluid dispersion throughout the system. The conventional approach, flow simulation, is computationally very expensive and time consuming. Alternative method employs percolation theory. Classical percolation approach investigates the connectivity between two lines (representing the wells) in 2D cross sectional models whereas we look for the connectivity between two points (representing the wells) in 2D aerial models. In this... 

In this paper, the performance of a novel HDH solar desalination system equipped with a combination of heat pipe (HP), evacuated tube collector (ETC) and air bubble column humidifier is experimentally investigated. This novel HDH system uses advantages of ETC-HP as a highly efficient thermal absorption and conductor device, and at the same time employs the advantages of an air bubble column humidifier, i.e. high interface area and effective mixing in order to heat the water and humidify the air, respectively. The effects of various parameters including incoming air flow rate into the humidifier, initial depth of water in the humidifier, and adding fluids such as oil and water in the space... 

Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to... 

Echocardiography as a common place device has been widely used for diagnosis. Segmentation of left ventricle just using the standard level set formulation based on the magnitude of intensity cannot restrict the contour evolution completely due to the lack of sharp edges. This paper aims to introduce a robust method for segmentation of three-dimensional echocardiography images based on two boundary maps namely distance map and probabilistic map as stopping criteria of contour evolution which are extracted using two different approaches. The distance map is extracted by applying Free Form Deformation (FFD) method on manually determined landmarks of initial spherical volume to register the... 

In this paper, an efficient approach for ultrasonic object segmentation with special application for left ventricle segmentation in echocardiography images is proposed. At first, an efficient hybrid trend for ultrasonic image edge detection is suggested. Then, a modified level set approach is introduced based on the extracted edges and the computed probabilistic map as the stopping criteria for the contour evolution. Both synthetic and clinical images are utilized as validation measures with respect to the prior techniques which indicate outperform results quantitatively and qualitatively. Left ventricle segmentation using proposed method illustrates expert-approved performance, providing a... 

The substitution of conventional mathematical models with fast and accurate modeling tools can result in the further development of desalination technologies and tackling the need for freshwater. Due to the great capability of data-driven methods in analyzing complex systems, several attempts have been made to study various desalination systems using data-driven approaches. In this state-of-the-art review, the application of various artificial intelligence and design of experiment data-driven methods for analyzing different desalination technologies have been thoroughly investigated. According to the applications of data-driven methods in the field of desalination, the reviewed... 

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods... 

Sparse representation is a powerful tool for image processing, including noise removal. It is an effective method for Gaussian noise removal by taking advantage of a fixed and learned dictionary. In this study, the variable distribution of Rician noise is reduced in magnetic resonance (MR) images by sparse representation based on reconstruction error sets. Standard deviation of Gaussian noise is used to find these errors locally. The proposed method represents two formulas for local error calculation using standard deviation of noise. The acquired results from the real and simulated images are comparable, and in some cases, better than the best Rician noise removal method due to the... 

A novel combined power and refrigeration cycle is introduced and analyzed from the thermodynamic standpoint. The proposed cycle is an integration of a Kalina cycle equipped with an ejector (EKalina) and an ejector refrigeration cycle (ERC). In order to enhance the overall cycle performance, the possibility of employing two-phase ejectors in combined power and refrigeration cycles is studied. A parametric analysis is conducted to investigate the effects of the key thermodynamic parameters namely turbine inlet pressure, split ratio, basic ammonia concentration, heat source and evaporator temperatures on the cycle performance. The developed model is also verified with available data and it is... 

This paper presents the results obtained and the deductions made from a series of microstructural studies and mechanical tests involving gray cast iron which was sand cast using a variety of modules. The effect of cooling rate on the primary dendrite arm spacing (DAS), secondary dendrite arm spacing (SDAS), thickness of ferrite-cementite layer (λe) and the hardness (HB) were evaluated. Results show that the both DAS and SDAS and also λe are highly dependent on the cooling rate, and they decreases as the cooling rate increases. More attempts were also done to correlate the HB with DAS, SDAS and λe. It was found that HB increases as DAS, SDAS and λe decreases  

Performance of a small-scale trigeneration system driven by low-temperature geothermal sources for producing fresh water, heating (hot water) and electricity is investigated from thermodynamic and economic standpoints. This system, utilizing a single stage absorption heat transformer leads to an increase in heat source temperature to be used in single stage evaporation desalination process and also providing water heating. Furthermore, an organic Rankine cycle is used for electric power generation. The developed model is validated with available data and effects of decision variables namely geothermal source, absorber and condenser temperatures on energy and exergy efficiencies of the... 

Effects of cold rolling on the microstructure and mechanical properties of Fe-Ni-Mn-Mo-Ti-Cr maraging steels were studied. To investigate the microstructure and mechanical properties, optical microscopy, scanning electron microscopy, X-ray diffraction, tensile test, and hardness test were used. The results show that the solution-annealing treatment in the cold-rolled steel redounds to the formation of submicrocrystalline Fe2 (Mo, Ti) Laves phase particles, which are stable at high temperatures. These secondary Laves phase particles prevent from recrystallization at high temperatures and correspond to semi-brittle fracture in the subsequent aging treatment  

Evaporative condensers are regarded as highly-efficient and eco-friendly heat exchangers in refrigeration systems. Data-driven methods can play a key role in performance prediction of evaporative condensers, conducted without the complexity of theoretical analysis. In this study, four machine learning models including multi-layer perceptron artificial neural network (ANNMLP), support vector regression (SVR), decision tree (DT), and random forest (RF) models have been employed to predict heat transfer rate and overall heat transfer coefficient of a small-scale evaporative condenser functioning under a wide range of working conditions. A set of experimental tests were conducted, where inlet...