Modeling in Engineering
Year:2019 | Volume:17 | Issue:57|Papers Count:25

In this paper three dimensional numerical study of solidification of phase change material (PCM) in a triplex tube heat exchanger is studied. Water is used as heat transfer fluid (HTF) which flows through the inner and outer tubes while the shell side is filled with RT82 as the PCM. The main purpose of this study is to investigate the effect of fin arrangement and the addition of CuO nanoparticles with different volume fractions on solidification behavior of phase change material. Also a comparison between triplex tube heat exchanger with fin and nanoparticles and triplex tube heat exchanger without fin and nanoparticles is done. Also, the amount of stored energy has been investigated. The results show that with the addition of nanoparticles, the solidification time for all heat exchanger with fines has decreased with respect to the without fin heat exchanger. The results also show that the reduction of solidification time compared to the without fin mode for interior pipe arrangement (Case B), for external pipe arrangement (Case C), both side pipes (Case D), and both side pipes (Case E), 40%, 57%, 58% and 56% respectively. Also, by changing the fluid temperature from 65 to 60 and 55 ° C, the solidification time decreased to 21% and 34%, respectively.

Low salinity water (LSW) flooding is one of the emerging EOR methods which has recently attracted lots of attention. Wettability alteration from oil-wet to water-wet triggered by ion exchange between low salinity water and rock surface is one of the dominant mechanisms to increase oil recovery by LSW. This paper studied the performance of LSW flooding in a sandstone oil reservoir where wettability alteration was investigated by coupling two-phase flow equations and geochemical reactions between water and rock surface. A wettability alteration index based on the ion equivalent fraction of sodium was introduced that mainly consists of the effects of ion exchange and clay properties on oil/water relative permeability functions. Also, the effect of calcite mineral dissolution was investigated by the relevant geochemical reactions. Results showed that salinity difference between injected and formation water caused ion exchange processes between water and rock surface. Also, calcite dissolution enhanced ion exchange processes and thus led to wettability alteration to more water-wet conditions. Results showed that the presence of dissolved CO2 in water increased the rate of calcite dissolution. It was also found that the rate of ion exchange and calcite dissolution reactions decreases far from the injection well. The results of this study revealed that LSW flooding provided incremental oil recovery of 8% of the oil initially in place as compared to high salinity water flooding.

Due to increasing of cancers and production of new anti-cancer drugs for its treatment, in this study, the interaction of new hydrophobic anti-cancer drug paclitaxel with the cell membrane has been discussed using computational tools. We have done molecular dynamics simulation using NAMD and also the initial structures that were achieved from the protein data bank have been modified using VMD package. Langevin algorithm was used for temperature control in 310 K, the human body temperature and the Brandson algorithm was utilized for pressure control in 1 bar. The simulation has been done during 10 ns. The simulation equations were based on Newton’ s Motion Law and a Lenard− Jones potential. The anti-cancer drug paclitaxel interaction with the cell membrane has been investigated from the van der Waals energy and center of mass (COM) perspectives that show less stability and low absorption of the drug to the cell membrane. Computational results of this study confirm the validity of previously published computational and laboratory studies. According to the drug hydrophobicity, less stability, low absorption and also low efficacy has been shown in interaction with the cell membrane. As a result, administration of the anti-cancer drug can be very effective and efficient by using new drug delivery methods.

The purpose of this research is to find the optimal location for establishing a new service unit, like fuel station, within the urban area so that the average distance traveled by each user to the nearest unit reaches the lowest value. For this purpose, Monte Carlo simulation method was applied along with two optimization approaches including brute-search and genetic algorithm. In addition, image processing tools were used for distinguishing the urban regions and identifying the border of each zone. As a case study, the construction of a new fuel station unit in Tabriz city is carried out in this article. To achieve this goal, 40, 000 users were randomly selected according to the population density of each zone within the city and the average distance of each of them was calculated from the nearest station. Afterwards, using the two mentioned algorithms, the new station was added in such a way that the minimum mean traveling distance was achieved. Considering the same number of random users for both methods, the genetic algorithm with initial population, generation and mutation rate of 60, 30 and 0. 2, respectively, was resulted in better performance in terms of time and the mean distance. The mean distance between drivers and stations before adding a new unit was 2105 meters. However, by adding a new fuel station using the brute-search method and the genetic algorithm, this distance is reduced to 1908 and 1901 meters, respectively.

The exact calculation of the flow rate of natural gas is a very effective factor in economic, operating conditions control, equipment maintenance, and etc., of the gas industry of the importing and exporting countries. Today, in the country's gas industry, the flow rate is estimated by natural gas density correction factor (NG-DCF) calculated by the equations of state as a cost-effective method. Permanent access to input data required for equations of state, in particular, the composition of all gas components, requires high costs, hence the information estimated as model input data is mostly applied. Since the calculation accuracy of the NG-DCF can be affected by the model inputs, it is necessary to recognize and analyze the effect of each of them on the model output, to investigate the model output sensitivity from the model inputs, and to determine the uncertainty of the model output. In this study firstly, the NG-DCF was modeled by a precise-standard equation of state, AGA8-DCM (Detail Characterization Method), then various analytical and numerical methods have been applied to calculate the sensitivity analysis and uncertainty of the NG-DCF to avoid from estimation error. The results of the sensitivity analyses showed that after the gauge operating pressure and operating temperature, the composition percentage of methane, ethane and nitrogen in natural gas are the most effective linear parameters on the NG-DCF. Also, the atmospheric pressure changes, which are often neglected, are known as one of the parameters affecting the NG-DCF.

Kinetic modeling was used to determine the type of the oxygen function in partial oxidation of propane to AA over Mo1V0. 3Te0. 23Nb0. 12Ox catalyst. Experimental data was collected under different operating conditions in a fixed bed tubular reactor. A reaction network consisting of parallel/sequential reactions with various pathways for the production of carbon oxides was considered. Some assumptions were made in the development of models including: the existence of catalytic sites with different activities, connection/non-connection of catalytic sites together, connection/non-connection of catalytic sites with gas phase oxygen and also the type of the active oxygen in different reactions. Based on these assumptions and also with regard to the reaction network 5 models were developed based on the Mars-Van Krevelen and Eley Rideal mechanisms. Kinetic parameters were optimized using genetic algorithm. It can be concluded that gas phase oxygen does not directly play role in the lattice oxygen production. Therefore gas phase oxygen concentration has negligible effect on AA selectivity. While carbon oxides are produced through reaction between propane and non-selective oxygen sites. The concentration of these sites is directly affected by the partial pressure of the gas phase oxygen. It can be concluded if propane and oxygen have less contact with each other, the selectivity to desired products increases. Accordingly two zone fluidized bed and circulating fluidized bed are proposed for this reaction.

In this paper, a new mixed integer mathematical model is developed for desifning a sustainable closed loop supply chain management problem. In this model, some real world manufacturing features are taken into account like exonomical, environmental and social responsibility indexes. The main novelty of propose model the consideration of three key indexes of local and sustainable job opportunities and also creating new job opportunities in terms of social responsibility of a supply chain. Moreover, the greenness of a supply chain in terms of CO2 emmisions are investigated through the developed model. In order to evaluate and validate the proposed mathematical model, some numerical examples are generated randomly, and solved using the CPLEX solver on Gams optimization package. In addition, some magerials insightsd are included in the paper by performing deep sensitivity analysis of the esseintial parameters. Based on obtained results, considering all sustainbiliy aspects of a supply chain in a concurrent way can reduce costs, significantly.

One of the most important issues of concern to human societies in recent years is urban waste management that is one of the main requirements of each city, and without any notice of it, it can be problematic for it and even residents of the surrounding villages. Urban areas generate the highest amount of waste and consequently, they need an efficient system for collecting and disposing of waste where its determination and stabilization is very difficult and costly. In this regard, this paper examines the multi-trip vehicle routing problem with time windows specific to the urban waste collection, with the goal is to minimize the total cost including routing costs, the earliness and lateness penalty cost for violating the service time windows and the usage costs of vehicles. To solve the problem in practical dimensions, grey wolf optimization (GWO) algorithm is developed where its performance is tested compared to CPLEX solver of GAMS and simulated annealing (SA) algorithm. The obtained results demonstrate that the proposed GWO have an acceptable performance to generate high-quality solutions. Finally, to study the behavior of the objective function versus the real-world demand parameter changes, a sensitivity analysis is performed on this parameter and the optimal management policy is analyzed. ed.

In the competitive location problems, the matter of the optimal location of single or multiple facilities are in a condition in which competitors exist as well. This paper deals with a type of a competitive location on which a leader possibly uses the investment of other investors through concession and receives a percentage of their income. He also can place his own facilities on potential locations that are available. In fact there are three decision-makers, one as a leader, others as followers who get in the game of facility location for placing their facilities. The location of facilities is a simultaneous game in which decisions are made in two levels, in the first level they are asynchronous and sequential, in the second level decisions are synchronous, namely the leader selects places for his own facilities, where other decision-makers (investor) play the simultaneous and non-cooperative game, and according to Nash equilibrium, select the optimal location. The leader uses new locations to optimize his objective function. Eventually, a numerical example is presented to test the model and a comprehensive sensitivity analysis is carried out to extract some managerial insights.

So far, several methods have been utilized for truss optimization where metaheuristic thechniqes have been used as one of the most common methods for such purpose. These thechniqes have received much attention due to their independence from gradient information and efficient global search property. In some of the metaheuristic thechniqes, random generation of the preliminary population leads to creation of repetitive population, dependency of final optimum solution on preliminary population, increasing of the number of analyses and reduction of the algorithm efficiency. To overcome these disadvantages, the present study employs some intelligent thechniqes for the generation and the selection of the population. So, a new metaheuristic algorithm called SPR is proposed for discrete truss optimization problems. The SPR algorithm is based on the sensitivity of the elements, the partitioning of the search space and the rebirthing of the optimization procedure. The structural members which are more sensitive to changing the cross-sectional areas are recognized through the procedure. In order to find the best approximate solution for any design variable, the search space is discritized to some partitions. Rebirthing technique searches for better optimum solutions around the last local optimum point for any design variable to end the stagnation states. The performance of the proposed algorithm is investigated using several benchmark size optimization problems of truss structures from the literature. In comparison to the available results in the literature, the proposed SPR algorithm showed capable of obtaining the optimum design with much lesser computational attempt.

The application of artificial simulations in predicting the behavior of materials, especially when we have real results, is very important in terms of time and cost. Therefore, in this study the data collected from unconfined compressive strength test on stabilized soil samples with lime, waste industrial and sodium silicate by neural network (GRNN) and genetic algorithm (GEP) have been investigated. Moreover, based on the results of unconfined compressive strength for the limited percentages of the experiment, simulation has been performed and verified. Then, with the development of the neural network and genetic algorithm for different states and percentages of mixing in stabilized soil, the optimized mixing percentage has been set. According to the results of genetic algorithm model, the optimal mixing design for this type of clay is 6% lime, 6% industrial waste, and 1. 5% sodium silicate. The results of neural network had better predictive power than the genetic algorithm, so that the best prediction for the 90-day model of the neural network with R2 and RMSE values is 0. 998 and 0. 019, respectively, and the least prediction for the 7-day model of genetic algorithm with R2 and RMSE is 0. 967 and 0. 059, respectively.

Today, business process management systems (BPMS) are rapidly expanding. Organizations and corporations need to leverage BPMS to manage their business processes and to increase economic productivity for competing in the international market. Therefore, researchers are to increase the features of BPMS. Workload balancing in BPMS is one of the challenges which have been studied by researchers. The purposes of workload balancing include increasing system stability, improving the resource efficiency and enhancing the quality of its products. In this paper, undesirable effects of the time delay of arrival rate in the tasks are presented. To overcome the undesirable effects, we define the workload pattern of resources and we propose a heuristic method to manage and regulate the processes in the BPMS as Process Concurrency. we leverage PSO algorithm to optimize process concurrency. Therefore, in addition to balancing the workload of resources, workload for each resource is maintained uniform through time. Creating uniformity in workload, as a result, increase the resource efficiency and improve the quality of products and services. To evaluate the proposed method, experimental results showed the optimal process Concurrency 29. 8% increase balance and uniformity in the resource workload.

Today, the use of intelligent systems in medical diagnosis is gradually increasing. These systems lead to a reduction in error, which may be experienced by inexperienced experts. In this study, the use of artificial intelligent systems in predicting and diagnosing breast cancer, which is one of the most common cancers among women, is being considered. In this research, the diagnosis of breast cancer is performed with a two-stage approach. In the first step, the two parameters of the effective properties and the number of secret layer nodes for optimizing the MLP neural network are simultaneously optimized by a genetic algorithm. Then, using selected features and number of hidden layer nodes, a MLP neural network modeling model is developed for diagnosis of breast cancer in the second step. Here, a FinGrain parallels genetic algorithm based on optimized parameters is used to adjust the weight of the MLP neural network. The evaluation of the experiments shows that the proposed method compared to the two GAANN and CAFS methods on the WBCD dataset yielded better results and reported an accuracy of 98. 72% in the average time.

Service Function Chaining is an architecture for orchestrating network services that assign choice to the network. This architecture is essentially a policy structure that should form the proper chain of services. Managing these networks is susceptible to error due to the combination of services with dedicated configurations. Accordingly, solutions will be needed to provide an appropriate ambiance for such a situation. Therefore, before running, the chains must be fully controlled, which requires the definition of chaining rules. Among the issues raised in this architecture are: checking the accuracy of the chains, as well as reducing the number of combinations of service chains. To solve these issues, the grammar is used in this paper. In this way, based on the scenarios in the Internet Engineering Task Force, they first create them and then their grammar is obtained using Regular Expressions and Finite Automaton. Subsequently, using the Cocke– Younger– Kasami algorithm, the grammar evaluation is performed and the number of combinations of services is also shown. The results show that this grammar can be verified by checking the service chain and also significantly reducing the number of combinations of service chains.

Software development cycle includes analysis, design, implementation and testing, and several other phases. The software testing phase is one of the costly stages of software development, and should be effectively implemented so that the software can be accessed without error by the users. One of the most effective activities for software development and its reliability is to predict software flaws before reaching the testing stage, which helps to save time in the production, maintenance and cost of software. One of the most effective models for predicting software flaws is the use of multilevel perceptron neural networks with post-error training algorithm. One of the weaknesses of the post-error training algorithm is the possibility of trapping the neural network at the local minimum points. Considering the potential of hyper-algorithms in exiting the local minimum mines and finding the minimum in general, in this paper, the combination of grasshopper meta-heuristic algorithm and post-error training algorithm were used to solve the weakness of the training algorithm and to improve its accuracy in predicting software defect. Is. In order to evaluate the results of the proposed model, neither the actual database was used nor the cross-evaluation method was the basis for presenting the results. The proposed model's performance has been compared with six defective software prediction models. The results of this comparison show that the proposed model is able to provide more accuracy and accuracy in comparison to other models in a large number of data sets.

In a smart home, all its internal components interact with each other through an integrated system and environmentally reasonable logic. In this house, energy management (EMS) can play a role in reducing energy costs. Therefore, in this paper, given the increasing importance of this discussion, the operation of smart home with the presence of electric vehicles with the capability of exchanging power with the network, the Energy storage system and solar panels have been modeled and evaluated in the framework of mixed integer linear programming. In this paper Various studies including the sale of electrical energy produced by solar panels to power grid, Loads with time-consuming movement such as washing machines as well as various demand response strategies based on energy price have been considered to evaluate the economic and technical effects of electric vehicles, energy storage batteries And solar panels. In assessing the performance of solar panels, uncertainty regarding the prediction of solar power is considered, and the proposed model has been analyzed by considering the conditional value at risk (CVAR) criteria. The proposed model is implemented in the GAMS software and the results indicate that the use of additional power systems will significantly reduce the cost of smart home power.

This paper presents a new approach to optimizing of the control strategy in parallel hybrid vehicles. By proposing a new objective function and in order to efficient management of the power split between the combustion and electrical motors, fuel consumption and the amount of emissions are minimized with considering the penalty functions. Using the ADVISOR software, as one of the most common software used for a hybrid vehicle, as well as nonlinear equations of these vehicles, a parallel hybrid vehicle in the urban driving cycle of the United States is simulated. In this paper, an extended algorithm based on the Particle Swarm Optimization (PSO) is used. In the proposed PSO algorithm, the coefficients of contraction and the mirror effect of speed are used to increase the efficiency of the method. With the new constraints proposed for the PSO algorithm, the results have been improved. Also, simulation results are compared with the results obtained from the algorithms presented in recent papers. This comparison also shows the efficiency and accuracy of the proposed algorithm in optimizing the control parameters that lead to reduced fuel consumption and emissions from the vehicle.

The smart energy management system as a powerful tool is implemented to manage both demands and generation units. The energy management problem in a Microgrid is usually formulated as a nonlinear optimization problem. According to nonlinear and discreet nature of the problem, solving it by a centralized method requires high computational capabilities. In this paper, two distributed energy management system called Alternating Direction Method of multiplier Predictor (ADMM) and Corrector Proximal Multiplier (PCPM) have been investigated in order to jointly schedule the central controller as well as local controllers. The algorithms consider optimal power flow equations within the distributed energy management problem. The proposed distributed algorithms have been investigated on a typical MG and the efficiency of the algorithm has been evidenced through case studies. Simulation results show that the proposed method decreases the operational cost of MG. Also, the results evidenced that the ADMM has been converged faster and provided a lower operation cost if compared to the PCPM.

Self-healing polymers are a class of smart materials that have attracted many researchers due to their unique ability. These materials are able to heal part of the damage without the need to identify the site. In order to study the behavior of these polymers, a thermodynamically consistent model is proposed to predict the mechanical response. Along with this, the implicit time-discrete form of the constitutive model is presented in order to utilize in ABAQUS software by UMAT subroutine. In the discretization, the Newton-Raphson method has been used to update internal variables such as viscoplastic strain components. Considering the calibration of the material parameters by the experimental results of the asphalt sample, the movement of vehicle on the desired pavement has simulated. In this research, a detailed explanation of how to perform the finite element analysis of mechanical behavior of asphalt pavements is presented using constitutive model and its implicit discretization. In the following, its validation with existing experimental results is also carried out. Next, a comparison between the simulations is performed without and with healing effect. From the results of finite element analysis, the important role of healing in the life of the pavement can be noted, which can increase the recovery of damage up to 20%.

Friction stir welding is a new method, which is classified in the category of solid state bonding processes. In such processes during the process, the workpiece and tool materials do not melt, and the process temperature is always at a lower level than the melting temperature. This technique is used to joint aluminum alloys and does not have any restrictions on use such as fusion welding. In the present study, the experimental study of the effect of the position of metal with higher strength on the advancing and retreating side and on the ultimate tensile stress and fine hardness of the samples obtained from the joint specimens by the friction stir welding method. Two aluminum alloys, 6061 and 5054, were used, and each of these alloys was placed in two experimental and two directions on the both sides. By examining the tensile test graphs from the two experimental conditions, it was found that the samples in which the metal with higher strength placed on the advancing side found to have a higher final strength in the welded specimen. When the metal with higher strength in the retreating side was placed, the final strength of the bonded samples from both base alloys was lower and the softness and strain of the break in the samples increased the firmness of the base metals. The micro hardness of both experimental modes was studied and a W-shaped pattern was obtained for both experimental friction stir welding.

This numerical study carried out to find the effects of wall geometry and fluid content of a sinusoidal corrugated channel of turbulent fluid flow on increasing thermal-hydraulic performance factor. The channel consisted of two zones namely, zone1 where the fluid pass through it, and zone2 where the fluid was trapped in the channel walls. . Air and TiO2-water Nano fluid were used as fluid in 1st and 2nd zones. Nano fluid was homogeneous, single-phase, with volumetric concentration of 1%. The upper and lower plates of the channel were heated with a constant heat flux of 616 W/m2. The Reynolds numbers of fluid flow of channel were 3700 to 40, 000. The turbulent fluid flow in the 1st zone was simulated using the standard k-ε model. The effects of fluid type used inside the channel wall and wave angle and wave height were investigated. The results showed that when the TiO2-water nanofluid was in the 2nd zone and air passed through 1st zone, the channel performance was the best. The optimum wave angle is 35 degrees and produced the most thermal-hydraulic performance factor in 12000 to 40000 Reynolds numbers. With increasing wave height from 4 to 6 mm, the thermal-hydraulic performance coefficient increases from 65 to 110%. The effect of wave height on the increase of the thermal-hydraulic performance coefficient was more significant than the wave angle change.

Study of blood flow in cerebral arteries is of great importance in recognition of initiation and critical conditions of arterial diseases in the brain. So, in this investigation with the novel assumption of arteroal wall deformability, blood flow and arterial wall shear stress in the circle of Willis were analyzed. CT angiography images were used to design the geometry and available model for solving blood flow. Finite elements method was utilized to solve the problem. With the study of the wall of the arteries, the behavior of the hyperelastic was assumed. Therefore, the solution of the flow is studied by the method of fluid-structure interaction. The boundary conditions of the fluid (blood), including multi-branching as well as autoregulation, were investigated at the inlet and outlet of the arteries. The maximum shear stress on the wall was equal to 3. 9 Pascal. In addition, blood pressure in the upper arteries in circle of Willis was significantly reduced as compared to the blood pressure at the outlet of the heart. Analysis of shear stress contour in diastol showed that the maximum shear stress occurs in PCA, while it is minumum in initial parts of PCA. Approach of this study to use radiological images of specific humans can be helpful in diagnosis of initiation and development of arterial diseases. Moreover, in acute situations of these diseases, through anticipating of dangers such as tearing of the vessels due to variations of wall shear stress, such a performed simulation may aid physicians for probable treatments.

The aim of this work is to enhance thermal performance characteristics in a solar flat plate collector with inserts typical twisted tapes and Al2O3-water nanofluid as the working fluid. Using twisted tapes make contact surface area and swirl intensity to increase. Numerical present analysis of the thermal and fluid behaviors of the tubes with twisted tapes in different arrangements has been investigated under constant heat flux condition with the Reynolds number ranging from 4000 to 20000. For numerical solution of this problem, The Fluent commercial software has been used in which Realizable k-ε turbulence model was applied. According to the tests, the numerical results show that the twisted tape inserted in a solar flat plate collector induce swirling flows which help to improve fluid mixing, therefore increasing diameter ratio (D*) and revaluation of ratio (N) enhance heat transfer rate and the other hand, the pressure drop and also the friction factor increase. The maximum thermal performance factor of 1. 08 is obtained by using twisted tapes with N = 7 and D* = 0. 014 at the lowest Reynolds number of 4000

Johnson-Cook (JC) material model takes into account the effect of strain and strain rate hardening and thermal softening, and it is used as the most applicable material model by researchers for determination of material plastic behavior during simulation of machining process. Experimental determining the constants of this equation is costly and time-consuming task. In this regard, at the present study a new method was presented and it is utilized without necessity to long time procedure and costly equipment. In this method, JC equation constants for simulation of Inconel 718 alloy machining were obtained using optimization algorithm and couple of experimental flow stress results. Obtained results using this method were compared with results of other material models determined by researchers using the experimental and analytical tests. Based on this, using the implementation of user-subroutine in finite element software, simulation of machining process at different cutting conditions was performed for each material model and results of cutting forces (two components), chip geometry (three components), and maximum temperature were compared with corresponding experiments. Finally, average of total error was reported 13. 7% that is suitable compared with other material models and it provides reasonable hope that, the presented strategy is employed successfully at other investigations.