Sharif University of Technology
Recent publications
In this study a 2D cubic chamber model filled with paraffin is analyzed with and without the inclusion of magnetic Fe3O4 nanoparticles at concentrations of 0.5, 1, 1.5 and 2 wt%, and an external magnetic field of intensities 0.005, 0.01, 0.015 and 0.02 T. It is ascertained that adding magnetic nanoparticles leads the horizontal temperature gradient to be reduced owing to increments in thermal conductivity. Additionally, this feature is found to be accelerated by applying an external magnetic field, which shapes highly conductive cluster formations of nanoparticles. However, since the increase in nanoparticle concentration and magnetic intensity increases the composite viscosity, there is an optimum configuration while applying both schemes. As such, the addition of 1 wt% nanoparticles provides the best results, as the melting time is reduced up to 25% compared to pure paraffin. Meanwhile, the melting time of a 1 wt% nanoparticle-containing phase change material (PCM) in the presence of an external magnetic field is improved up to 24% compared to the case with no external magnetic field. Also, the heat transfer coefficient of a 1 wt% nanoparticle-containing PCM both with and without an external magnetic field is also staggeringly enhanced compared to pure paraffin. Good correspondence with experimental data was achieved.
This paper evaluated one-pot synthesis of magnetic manganese graphene oxide nanocomposite (MnFe2O4-GO) for Rhodamine-6G (Rh-6G) removal from aqueous solutions. The presented adsorbent was characterized by scanning electron microscopy (SEM), vibrating sample magnetometer (VSM), Fourier transform infrared spectroscopy (FTIR), and X-ray powder diffraction (XRD). For the optimization, central composite design (CCD) was carried out to evaluate the effect of different parameters such as pH (6), equilibrium time (15 min) and adsorbent amount (0.6 g/L). Under the proposed conditions, the adsorption efficiency was investigated through Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherm models. Hence, due to high value of R2 (> 0.99) the adsorption process was well fitted to Langmuir isotherm. Findings showed that the absorption capacity of MnFe2O4-GO was 24.96 mg.g-1 and unmodified GO 14.82 mg.g-1in 6 minutes. Based on the thermodynamic studies, the high values of ΔH° (146.19 KJmol-1) and ΔG° (- 0.04 KJmol-1) indicated that the adsorption mechanism was endothermic and physio-sorption at room temperature. Overall, the results proved that the prepared MnFe2O4-GO nanocomposite was suitable to remove the Rh-6G from aqueous media.
Low-strength concrete (LSC) elements are prone to several seismic and static loads and are one of the priorities to be considered for FRP strengthening. However, certain provisions should be taken into account according to provisions, as elements with considerably low compressive strength are not eligible for FRP confinement. This experimental study investigates (1) the effect of rebar planting on increasing the initial compressive strength of LSC to achieve allowable compressive strength for FRP strengthening, and (2) the effect of CFRP confinement on increasing the strength of rebar-embedded specimens and determining the most effective factor for strength improvement. For this purpose, 38 standard concrete cylinders were tested under compressive load. The variables of this study were rebar length and diameter, the compressive strength of concrete, and the number of CFRP sheets. Two initial compressive strengths below the designated compressive strength of 17 MPa (12.5 and 14.5 MPa) were selected. After determining rebar-reinforced specimens with compressive strength of more than 17 MPa, CFRP confinement and compressive tests of these cylinders were utilized. A statistical single-factor ANOVA analysis is performed to determine the most effective variable for ultimate strength and strain, individually. In the end, available models in the literature were utilized to predict experimental data. The results indicated the effectiveness of rebar planting for strength enhancement up to 53%, also showing that specimens with initial compressive strength of 14.77 MPa were suitable for CFRP confinement after rebar planting. The experimental and statistical ANOVA results demonstrated the CFRP confinement and its interaction with rebar embedment as the most effective factors with respect to increasing the load-bearing capacity of LSC concrete.
Miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management. Hydrological models quantify water withdrawals, but their estimates are unduly precise. Model imperfections need to be appreciated to avoid policy misjudgements.
Congestion management (CM) is inevitable in today's competitive power markets. A CM method should be fast, fair, effective, and motivational. Moreover, in critical congestions, the system simplification and congestion clearing time are also of considerable importance. All the mentioned features can be found in an intelligent zonal CM. In this paper, using sensitivity analysis and power tracing techniques, a new CM model is developed based on power system partitioning. By the proposed model, a congestion index (CI) is introduced by which a candidate zone(s) is specified including some elements (generators and loads) with the highest CIs which means that they have high participation in congestion creation and simultaneously high effectiveness in congestion alleviation. Using the proposed method, power system operators can put their focus on the candidate zone(s) and alleviate the congestion effectively by some remedial measures such as generation rescheduling and load shedding. The proposed methodology is applied on IEEE 39-bus New England test system including 46 transmission lines. Results show the effectiveness and practicality of the proposed model, so that the congestion at the critical lines is alleviated well and their available transfer capabilities (ATCs) are increased to the reliable amounts.
This study focuses on the spectrochemical estimation of pH and titratable acidity (TA) of apples of Fuji variety at different stages of ripening. A novel approach is proposed for near-infrared (NIR) spectral analysis using hybrid machine learning methods that combine artificial neural networks (ANN) and metaheuristic algorithms. One hundred twenty samples were collected at three ripening stages and spectral data within two bands of NIR were extracted from each sample to predict the acidity properties. Alternatively, the 4 most effective wavelengths were also selected using a hybrid of ANN and the cultural algorithm. The experimental results prove that the models using spectral bands and the 4 most effective wavelengths are comparable, with a correlation coefficient, R, of 0.926 for the prediction of pH and 0.925 for TA using spectral bands, while for the second approach the R obtained were 0.924 and 0.920 for pH and TA, respectively. The models could not accurately predict extremely high or low pH and TA values, due to the clusters that formed after regression. However, for a classification problem in low/high acidity, both approaches were able to achieve a high accuracy of 100% for pH and 99.2% for TA.
The introduction of cryptocurrencies as a new form of money has attracted a tremendous amount of attention in recent years. This new financial paradigm relies on miners to validate transactions by running their cryptocurrency mining devices (CMDs). Nowadays, the significant profitability of the mining business has tempted a large number of private players in the electrical industry to employ their renewable energy resources to mine digital currency. Here, microgrid (MG) owners may use their excessive generated power to mine digital money instead of exporting it to the main grid. This paper is devoted to investigate the influential potentials of this trending business on distribution networks operation and performance. Thus, a new energy management (EM) formulation is proposed to model the mining loads at the first step. Then, a Monte-Carlo simulation is introduced to obtain the annual profit of this mining business under the existing uncertainties. Afterward, appropriate financial indices are proposed to help the MG owner to choose the best type of CMDs, and their optimal number to be installed. Finally, this paper demonstrates how the current price profile of electricity will gradually tend the grid-interactive MG to initiate the mining business in many countries. The results show that the MG acts as a passive energy entity where the import of electricity to mine cryptocurrency is possible. In a nutshell, the higher the electricity price, the lower the mining installation and the more active MG to positively contribute to electricity generation.
In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were investigated. For this purpose, partial least squares-discriminant analysis (PLS-DA) for supervised classification to find a boundary between authentic and adulterated saffron samples. Classification accuracies for all models for calibration and prediction sets were 100 %. Finally, a mixed dataset was prepared and analyzed using the proposed strategy which again 100 % of accuracies for calibration and prediction sets were obtained. At the end, data driven soft independent modelling of class analogy (dd-SIMCA) was used to evaluate model for class modeling. Sensitivity was 95% for authentic class and specificities for all adulterants were 100%.
Derivation of the probability density evolution provides invaluable insight into the behavior of many stochastic systems and their performance. However, for most real-time applications, numerical determination of the probability density evolution is a formidable task. The latter is due to the required temporal and spatial discretization schemes that render most computational solutions prohibitive and impractical. In this respect, the development of an efficient computational surrogate model is of paramount importance. Recent studies on the physics-constrained networks show that a suitable surrogate can be achieved by encoding the physical insight into a deep neural network. To this aim, the present work introduces DeepPDEM which utilizes the concept of physics-informed networks to solve the evolution of the probability density via proposing a deep learning method. DeepPDEM learns the General Density Evolution Equation (GDEE) of stochastic structures. This approach paves the way for a mesh-free learning method that can solve the density evolution problem without prior simulation data. Moreover, it can also serve as an efficient surrogate for the solution at any other spatiotemporal points within optimization schemes or real-time applications. To demonstrate the potential applicability of the proposed framework, two network architectures with different activation functions as well as two optimizers are investigated. Numerical implementation on three different problems verifies the accuracy and efficacy of the proposed method.
Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.
Regression testing activities greatly reduce the risk of faulty software release. However, the size of the test suites grows throughout the development process, resulting in time-consuming execution of the test suite and delayed feedback to the software development team. This has urged the need for approaches such as test case prioritization (TCP) and test-suite reduction to reach better results in case of limited resources. In this regard, proposing approaches that use auxiliary sources of data such as bug history can be interesting. We aim to propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. To evaluate this approach we study its performance on real-world open-source projects. The bug history is used to estimate the fault-proneness of source code areas. The diversification of test cases is preserved by incorporating fault-proneness on a clustering-based approach scheme. The proposed methods are evaluated on datasets collected from the development history of five real-world projects including 357 versions in total. The experiments show that the proposed methods are superior to coverage-based TCP methods. The proposed approach shows that improvement of coverage-based and fault-proneness-based methods is possible by using a combination of diversification and fault-proneness incorporation.
Flow turbulators have promising heat transfer applications. Here, the novel design proposed in our previous study (vibrational ball turbulators, VBTs, mounted on an elastic wire inside a circular tube) is investigated experimentally from heat transfer and thermal performance standpoints. The effects of diameter (Y) and longitudinal distance (pitch, X) ratios of VBTs, the Reynolds number, and the axial tension of the wire (σ0) on the average Nusselt number (Nu), the average Nusselt number ratio (Nu divided by that of a plain tube, Nup), the friction factor ratio (f/fp), and a parameter called the thermal performance factor (η=(Nu/Nup)/(f/fp)1/3) are studied. Different ball diameter (Y=0.3, 0.38, and 0.46) and ball pitch ratios (X=1.53, 2.3, and 3.07) are utilized at Reynolds numbers between 10,000 and 15,000. VBTs are thermally advantageous, and for X=1.53 and Y=0.46, Nu/Nup and f/fp peak at 2.3 and 18.06, respectively, averaged over all Re. However, η is maximum (1.2, Re-averaged) for the minimum X (=1.53) and, counterintuitively, the minimum Y (=0.3). Increasing σ0 also has an adverse effect on η. A correlation to predict η as a function of X, Y, and Re is proposed. Also, to bridge the gap between the thermal response and vibrational behavior of the system (which was studied in our earlier work), another correlation for η as a function of Re and dimensionless VBT amplitude and frequency is introduced and indicates that increasing the vibration amplitude (or reducing the frequency) slightly deteriorates η.
Emergence of reformation and privatization in energy systems has caused the development of multi-agent distribution systems. In this context, each agent as an independent entity aims to efficiently operate its respective resources; while, the distribution network operator (DNO) strives to control the grid in an efficient and reliable manner. Respectively, in case of failure incidences in the grid, DNO should address the economic losses as well as reliability concerns of the agents. Consequently, this paper intends to organize a framework that enables the DNO in order to incentivize the cooperation of agents to alleviate operational effects of the contingency condition. Accordingly, DNO provides bonuses to agents to modify their scheduling with the aim of optimizing the incurred operating costs in post-contingency conditions. Respectively, Stackelberg game is applied to model the incentivizing resource scheduling optimization in post-contingency conditions, and strong duality condition is used to re-cast the preliminary bi-level model into a one-level mathematical problem. Furthermore, a step-wise strategy is illustrated to facilitate the application of the obtained optimization model while considering islanded areas in the grid. Eventually, the proposed strategy is implemented on the IEEE-33-bus-test-network to examine its usefulness and applicability in management of the distribution networks in post-contingency conditions.
This manuscript describes the implementation of plasma-enhanced chemical vapor deposition (DC-PECVD) and vapor-liquid-solid (VLS) techniques to fabricate a yolk-shell SnO2@[email protected] nanowire (NW) structure. SnO2 nanowires have been synthesized on the stainless steel mesh substrate through the VLS method. The PECVD-assisted growth of carbon nanolayer on the SnO2 and SiO2 coated SnO2 NWs has been performed to fabricate SnO2@C core-shell and SnO2@SiO2@C yolk-shell structures, respectively. A consequent silica etching process converted the SnO2@SiO2@C into SnO2@[email protected] structure. The electrochemical performance of bare SnO2 NWs, SnO2 NWs @ C, and SnO2 @Void @ C coaxial NWs structures have been investigated in half cell Lithium-ion battery (LIB) coin cells. A noticeable electrochemical enhancement has been observed for the SnO2@[email protected] electrode, with a specific capacity of 723 mAh g⁻¹ at 0.2C current density after 100 cycles of charge/discharge, compared to 34 and 455 mAh g⁻¹ for SnO2 NWs and SnO2@C NWs, respectively. This significant improvement can be related to the stable SEI formation on the carbon-coated layer and the electrolyte interface. Besides, the proper void volume created between the SnO2 NWs and the carbon layer provides sufficient space for expanding the SnO2 NWs during the lithiation process. Moreover, the adequate electrical and ionic conductivity of the deposited carbon layer can improve the electrochemical performance of the fabricated anode material. Using PECVD for deposition of the carbon nanolayer benefits from being highly controllable and manageable, as well as its scalability for industrial application. Furthermore, the utilized approach is fast, inexpensive, and low temperature. The reported carbon coating process is proposed as an effective method for protecting the active electrode materials, and as a result, enhancing the performance of lithium-ion batteries.
Multiphase flow is a challenging area of computational fluid dynamics (CFD) due to their potential large topological change and close coupling between the interface and fluid flow solvers. As such, Lagrangian meshless methods are very well suited for solving such problems. In this paper, we present a new fully explicit incompressible Smoothed Particle Hydrodynamics approach (EISPH) for solving multiphase flow problems. Assuming that the change in pressure between consecutive time-steps is small, due to small time steps in explicit solvers, an approximation of the pressure for following time-steps is derived. To verify the proposed method, several test cases including both single-phase and multi-phase flows are solved and compared with either analytical solutions or available literature. Additionally, we introduce a novel kernel function, which improves accuracy and stability of the solutions, and the comparison with a well-established quintic spline kernel function is discussed. For the presented benchmark problems, results show very good agreements in velocity and pressure fields and the interface-capturing with those in the literature. To the best knowledge of the authors, the EISPH method is presented for the first time for multiphase flow simulations.
Thermal energy storage (TES) occurs by changing the internal energy of materials in the form of sensible heat, latent heat, and thermo-chemical heat or a combination thereof. Latent heat storage (LHS) by phase change materials (PCMs) has many applications among these storage techniques. Despite the many advantages of LHS, the main problem of LHS systems using PCMs is their low thermal conductivity, which necessitates the combination of heat transfer enhancement techniques. Numerous diverse studies have utilized only one improvement method, while limited research has employed hybrid enhancement techniques. The typical improvement methods, such as adding nanomaterials, using fins, employing porous media, and micro/nano encapsulating the PCMs, can be combined in the TES system. A hybrid technique is when a combination of two or more enhancement methods is applied to a TES system. In addition, a new approach is introduced and then utilized in the hybrid enhancement method. An auxiliary fluid in direct contact with the PCM improves heat transfer in the melting and solidification processes. In this direction, this review discusses different enhancement strategies of melting and solidification rates in TES systems in recent years. The hybrid techniques introduced in this literature review show that the TES system performance has improved, further justifying the use of this method in future studies. Lastly, the challenges and future work direction are recommended, for exploring other hybrid enhancement methods for LHS systems.
This paper presents an original approach for the evaluation of reliability of active distribution networks with unknown topology. Built upon novel reformulations of conventional definitions for distribution reliability indices, the dependence of system-oriented reliability metrics on network topology is explicitly formulated using a set of mixed-integer linear expressions. Unlike previously reported works also modeling mathematically the relationship between reliability assessment and network topology, the proposed approach allows considering the impact of distributed generation (DG) while accounting for switching interruptions. Moreover, for the first time in the emerging closely related literature, the nonlinearity and nonconvexity of the customer average interruption duration index are precisely characterized. The proposed mixed-integer linear model is suitable for various distribution optimization problems in which the operational topology of the network is not specified a priori. Aiming to exemplify its potential applicability, the proposed formulation is incorporated into a distribution reconfiguration optimization problem. The effectiveness and practicality of the proposed approach are numerically illustrated using various test networks.
Background Impairments of upper limb (UL) sensory-motor functions are common in Parkinson's disease (PD). Virtual reality exercises may improve sensory-motor functions in a safe environment and can be used in tele-rehabilitation. This study aimed to investigate the effects of supervised and non-supervised UL virtual reality exercises (ULVRE) on UL sensory-motor functions in patients with idiopathic PD. Methods In this clinical trial study, 45 patients with idiopathic PD (29 male) by mean ± SD age of 58.64 ± 8.69 years were randomly allocated to either the control group (conventional rehabilitation exercises), supervised ULVRE or non-supervised ULVRE. Interventions were 24 sessions, 3 sessions/week. Before/after of interventions and follow-up period all assessment was done. Hand Active Sensation Test and Wrist Position Sense Test were used for assessing UL sensory function. Gross and fine manual dexterity were assessed by Box-Block Test and Nine-Hole Peg Test, respectively. Grip and pinch strength were evaluated by a dynamometer and pinch gauge, respectively. Results The results showed significant improvement in discriminative sensory function (HAST-weight and HAST-total), wrist proprioception, gross manual dexterity and grip strength of both less and more affected hands as well as fine manual dexterity of the more affected hand in the three groups in patients with idiopathic PD (P < 0.05). Conclusion The results of this study indicated that both supervised and non-supervised ULVRE using the Kinect device might potentially improve some aspects of UL sensory-motor functions in patients with PD. Therefore, ULVRE using the Kinect device can be used in tele-rehabilitation, especially in the current limitations induced by the COVID-19 pandemic, for improving UL functions in patients with PD.
Natural gas hydrate, a crystalline solid existing under high-pressure and low-temperature conditions, has been regarded as a potential alternative energy resource. It is globally widespread and occurs mainly inside the pores of deepwater sediments and sediments under permafrost area. Hydrate production via well depressurization is deemed well-suited to existing technology, in which the pore pressure is lowered, the natural gas hydrate is dissociated into water and gas, and the water and gas are produced from well. This method triggers multiphysics processes such as fluid flow, heat transfer, energy adsorption, chemical reaction and sediment deformation, all of which are dependent on the amount of gas hydrates remaining in the pores. Therefore, modeling of hydrate production is computationally intensive and expensive. While back-analysis through observed production history is essential for better understanding of the reservoir characteristics and reliable prediction for future gas hydrate production, a large number of required simulations makes it impractical. This study employs Artificial Intelligence (AI) to achieve an efficient back-analysis of the gas hydrate production conducted at the offshore Nankai site, Japan, in 2013. The results show that the AI-based metamodel is capable of reproducing outputs of heavy computation of the multiphysics processes and thus performs back-analysis greatly efficiently. The efficient AI-based metamodel also makes it possible to carry out sensitivity analysis and it is found that the permeability and the preyield plasticity parameter are most influential to reservoir responses. The approach of this study can be applicable to other reservoirs and will reveal the ground truth in-situ properties and the most influential properties, contributing to better understanding of reservoir behavior for future gas hydrate production.
Herein, poly (acrylic acid) (PAA) microgels were synthesized via alcohol type cross-linked by a free radical precipitation polymerization approach. At the first time, 1,6-hexanediol (1–6 diol), trimethylolpropane (TMP), and pentaerythritol (PEN) were selected as multifunctional cross- linking agent to synthesize cross-linked poly(acrylic acid) microgels. Alcohol type cross-linking agents can connect the PAA chains. The cross-linking reaction takes place due to reaction between hydroxyl groups of various cross-linkers and carboxyl groups of PAA chains. All of the hydroxyl groups do not participate in the reaction with acid groups of polymer chains through the polymerization stage; therefore, unreacted hydroxyl groups will react through sample drying (post-curing stage). The influence of cross-linker functionality and its concentration on various properties like swelling capacity, gel content, Tg (glass transition temperature), and rheological behavior were examined. The PAA microgels prepared via this cross-linking approach were compared to properties of microgels synthesized by epoxy type and vinyl type cross-linking agents in the previous studies. As a result, synthesized microgels via novel mechanisms have higher properties (for example, rheological and thermal properties) than that of PAA microgels prepared via the conventional mechanism. These behaviors can be due to decreasing \(\overline{Mc}\)(average molecular weight of two successive cross-links) in the polymeric network by utilizing new cross-linkers.
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9,776 members
Azarmidokht Hosseinnia
  • Department of Chemistry
Ali Movaghar
  • Department of Computer Engineering
Gholamreza keshavarz haddad
  • Graduate School of Management and Economics
Azadi Ave., 11365-11155, Tehran, Tehran, Iran
Head of institution
Professor Mahmoud Fotuhi-Firuzabad
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