mohammad kazem moayyedi

This study investigates the evolution of bubble shape within a square area filled with blood. The accuracy of the numerical solution is validated using Laplace's problem and the free-rising of the bubble. The analysis is conducted in two dimensions and in a transient manner. The effects of ultrasound waves are applied as a function of pressure on the boundaries of the solution domain. Results show that applying a linearly increasing pressure on the computational domain boundaries causes a reduction in bubble radius. Furthermore, it is observed that assuming the air inside the bubble behaves as an ideal gas, leads to more pronounced changes in bubble radius compared to constant density assumptions. Oscillatory pressure distributions on the external boundaries result in corresponding oscillations in bubble radius. These fluctuations in bubble size could be utilized to exert tension on the walls of blood clots, ultimately aiding in their dissolution. The most intensive bubble size fluctuations occur in the frequency of 1 (MHz). Additionally, the disproportionate changes in bubble radius with pressure variations are attributed to the hysteresis phenomenon.

Simulation of complex and non-linear dynamical systems needs numerical algorithms which are time-consuming due to hardware limitations. Therefore, many studies were focused on developing models with high speed of computation and good relative accuracy. Reduced-order model is the method that could be an alternative approach for simulating complex dynamical systems using some appropriate data from the system responses. Usually, these models are developed based on the dominant features of the desired system. Dynamic Mode Decomposition is one of the methods for calculating these basis functions. In this study, using this method and based on the concepts of dynamical systems, a reduced-order model has been developed for Burgers equation. Also, due to the incompleteness of the related modal space which is changed to low-dimensional space in order reduction procedure, the dissipation level of the surrogate model is decreased. Therefore, by using an artificial viscosity which is called the eddy viscosity approach, the stability of the model is improved. Eventually, comparing the results obtained by the stabilized reduced order model and direct numerical simulation data, the accuracy of the model with an average error of 0.4% is proven.

In this study, the simulation of internal ballistics and the exhaust flow of a solid propellant engine have been investigated. The flow field considered in this problem including the cylindrical grain and the solid wall of the engine and the flow at the nozzle outlet have been studied. The governing equations are used in an axisymmetric and compressible form to model the fuel combustion in non-erosive combustion mode. In the present study, cylindrical type of grain fuel is considered, which is one of the most useful types of fuel grains in terms of simplicity in production. In order to obtain the results from the simulation model which are close to the real data, used UDF modules in the Fluent software to describe some specific quantities and phenomena of the problem. Finally, the results obtained from the simulation in different modes of fuel burning are examined and show the accuracy of the numerical model by entering the parameters affecting the fuel burning.

In some places with hard winters, the heat from sunlight and stored in the soil may not be enough to heat the greenhouse. In such a situation, the additional heat needed must be provided by heating systems. The purpose of this study is to evaluate the effectiveness of the heating system in a glass greenhouse with side air vents and mechanical fans in both night and day periods. In this study, seven different greenhouse designs have been investigated in two periods, day and night. In the daily period, by changing the pressure of the fans, tried to obtain the best condition for providing the appropriate temperature in the greenhouse. Also, in this period, the effect of sunlight on the flow field and temperature changes inside the hall were also investigated. In the night period, the amount of temperature changes by opening and closing the side valves and changing the temperature of the hall floor has been studied. The results of the research show that a greenhouse with a pressure difference of 100 pascal during the day and a greenhouse with a floor temperature of 323.15 K and side vents closed at night have more suitable conditions for growing plants in the off-season.

In this study, a direct numerical simulation strategy using Arakawa numerical scheme for the barotropic vorticity equation as a single-layer quasi-geostrophic ocean model assuming double-gyre wind force and dissipation has been used. DNS will be compared with the exact solution for two problems with different values of Reynolds and Rossby numbers in order to evaluate the ability of this model to calculate dynamics. The results show that DNS of BVE assuming double-gyre wind force has succeeded in predicting the creation of a turbulent circulation pattern in a short time. In this prediction, it was found that the increase in Reynolds and Rossby numbers will lead to the disappearance of some vortices due to the reduction of the required depreciation for the survival of turbulence over time.

In this article, two computational frameworks are presented for the numerical simulation of flow and heat transfer under the effects of natural convection phenomena in a field containing water-copper Nano-fluid and including porous media. The first is a CFD model which is built based on accurate algorithms for spatial derivatives and time integration. The spatial derivatives have been calculated using first-order upwind and second-order central differencing approaches. Also, time integration is performed using the fourth-order Runge-Kutta method. In the second, a parametric reduced order model is developed to compute the whole flow field under the effects of some important parameters such as Darcy number and Rayleigh number. This model is constructed based on POD-snapshots method. The POD modes are calculated by the solution of an eigenvalues problem. The calculated eigenfunctions are POD modes which are ranked using energy-based criteria based on the total kinetic energy of the flow field. This approach leads to the development of a reduced-order model that can be used as a surrogate model of the CFD high-order approach. The results obtained from the reduced order model show relatively good agreements under variations of some important parameters such as Darcy and Rayleigh numbers and nanoparticles density on the flow and thermal fields with the benchmark DNS data. Also, from the results, it is concluded that the surrogate model has very small values of errors (order of 10-4 ~ 10-6) and the time spent on calculations is less than 10% of the time required for direct numerical simulation.

Electromagnetic flowmeters are high-precision devices that can be used to measure fluid flow in engineering systems, including municipal water supply networks. In this research, electromagnetic flowmeter modeling has been used which is a hybrid model based on numerical simulation results of turbulent flow and data estimation method for a pipe with a diameter of 100mm and a length of 800mm. The numerical model is built using three-dimensional simulation framework by creating a magnetic field in the center of the geometry and measuring the volume flow rate using Faraday law and Maxwell equations. In fact, the physics of the problem is such that to measure the flow rate of a fluid, a magnetic field was created in the center of the geometry where the electromagnetic inductors are located. By moving the fluid that has electrical conductivity, the intensity of electromagnetic induction was obtained at that point and the amount of velocity and flow rate was calculated using these data. For this purpose, current simulation under the effect of electromagnetic intensity was performed for the values of 0.01, 0.03 and 0.05 (Tesla) to obtain the most appropriate intensity. The results show that up to an intensity of 0.03, the magnetic field has little effect on the structure of the flow field. In order to develop an alternative model of electromagnetic flowmeter, the method of estimating the inlet average velocity based on information from numerical simulation data has been used. First, for a certain number of pressures (pressure differences), the magnetic field intensity in the position of the sensors was obtained then using these values, the inlet average velocity using direct numerical simulation was calculated. A new magnetic intensity different from the values ​​used was obtained by data mining method. In order to ensure the accuracy of the results, the obtained data were compared with the calculated values ​​of the software.

Augmented reality is one of the new technologies in recent decade that has appeared especially in mobile devices. In augmented reality, virtual objects are placed in a real environment, next to other real objects. Another reason for the importance of augmented reality is the implementation of augmented reality applications on mobile and personal devices such as smartphones and tablets. The integration of augmented reality with a numerical simulation method such as computational fluid dynamics provides a cognitive, scientific and efficient tool for expert and non-professional users to analyze practical problems. In addition, using computational fluid dynamics techniques and an augmented reality-based system, engineering analysis and simulation results are obtained directly on real-world objects, resulting in a better understanding and relationship between the simulation results and the world. It becomes real. In this research, the combination of augmented reality and computational fluid dynamics method has been used to implement an Android software to simulate the thermal changes of solids with different materials over time.

Since the formation and Direct solving the governing equations requires high time and computational cost, this study seeks to provide an equation free model based on deep learning algorithm that simulates steady state heat transfer in two-dimensional space and a relatively large size using order reduction method. Principal component analysis is a linear method and autoencoder is a nonlinear methods. The results of comparing their performance on different data sets showed that in reducion of order to very low dimensions, autoencoder and in reducion of order to very high dimensions, principal component analysis has a higher accuracy. Of course, the number of dimensions to order reduction and the characteristics of the data set such as size and number of dimensions of the data will affect the accuracy of the dimensional reduction. These two methods were used to order reduction of thermal data in order to faster simulate the phenomenon of Steady State Heat Transfer and were compared with a model based on convolutional neural network with a number of layers and multiple filters. The results showed that the models based on order reduction methods have much less computational volume and simulation time, and the outputs obtained from them, especially the model based on the autoencoder method, have a much higher accuracy.

The emission and distribution of dust particles are of the most important issues in the field of environmental studies. The transfer of dust particles is influenced by natural and environmental factors and always has destructive effects on the environment and especially on people's health. In present research, the numerical simulation of the emission of dust particles around low-lying hills has been done. The emission of dust particles is influenced by factors such as airflow velocity, environmental conditions, surface and also the type and structural characteristics of the particles. In order to accurately predict the flow field and investigate the way of dust particles emission, a numerical method based on the large eddy simulation has been used. Validation of the model was performed based on the comparison of simulation results with the laboratory data. It shows an appropriate accuracy for modeling based on the large eddy simulation. In the current research, the numerical simulation of fluid flow has been done with the Fluent module from the Ansys software package. The results of this research, including changes in the concentration of dust particles under the influence of factors such as airflow velocity and the diameter of the particles, have been presented and discussed. The results show an increase in the concentration of sand particles by reducing their diameter from 200 microns to 20 microns in this study. Also, the higher velocity of the inlet air flow into the computing domain, the lower the concentration of particles on the surface of the hill and its surrounding environment.

Engineering problems are generally mathematical models of physical phenomena. The solution to the physical problem can be found using engineering and simulation approaches such as numerical modeling. Augmented reality technology adds virtual content created by a computer or mobile phone with a camera to the physical environment of users. Using an augmented reality-based system, after analysis, the simulation results can be placed directly on real-world objects in a two or three-dimensional model corresponding to the location and dimensions of the physical body in order to better understand the results. In this research, using a combination of augmented reality and computational fluid dynamics techniques, a simulation model in an indoor environment with different pollutants has been developed. For this purpose, first, the basic concepts of augmented reality are expressed and its differences with virtual reality are examined. After examining the implementation method of the proposed model, the results for carbon dioxide and carbon monoxide pollutants have been obtained. To evaluate the proposed model, the data obtained from the mobile software are compared with the related benchmark results and show relatively good accuracy and computational capability.

Because analytical methods are less commonly used due to their low accuracy and limited application range, and numerical methods are time-consuming and have computer hardware limitations, especially in unsteady problems, researchers The development of models and solution methods has turned to higher speeds and efficiencies. One of these patterns is the order reduction method. The reduced rating method is an alternative model for simulating flow dynamics. Degraded models are developed mainly on the basis of calculating the effective structures of a dynamic system. The dynamic mode Decomposition method is one of the methods for calculating these basic structures. Using this model and using the principles of dynamic systems, the viscous Burgers equation has been transformed into a low-ranking dynamic system. If the Reynolds number is increased and the effects of the viscous term in the governing equation are reduced, the depreciation required in the system to stabilize the numerical solution is reduced. Also, due to the incompleteness of the assumed modes in the problem and the elimination of the effect of modes with higher numbers, this decrease in depreciation will be more pronounced. Therefore, by creating an artificial loss called vortex viscosity, an attempt is made to stabilize the system. Finally, by comparing the results obtained from the reduced model and direct numerical simulation results, the accuracy of this model is proven.

Mathematical modeling is used to study the phenomena and behavior of the system. Complex mathematical equations require powerful and time-consuming computational tools where they must be examined in order to obtain the correct behavior of a system.. In various science and engineering fields, many physical phenomena are introduced using a set of differential equations. They are known as mathematical models of physical systems. High-accuracy numerical simulations utilize numerical schemes and modeling tools to solve this set of equations and generate useful information about the behavior of a system. However, software engineering and processor technologies are rapidly advancing; computational complication is still an important factor in the simulation with high accuracy. It makes many restrictions in the solution of scientific problems in different research fields. Some examples of these problems are large-scale physical problems such as geophysical and atmospheric flows, which have high temporal and spatial variations. Therefore, the development of effective and robust algorithms to achieve the maximum quality of numerical simulations with the optimal computational cost is a research topic. There are several methods for dimension reduction but this study used a combination of POD and long short term memory (LSTM) network. Finally, comparing the results related to the modal coefficients which are obtained by the reduced-order model and CFD snapshots projection shows the high accuracy of the proposed method.

The radiative transfer equation models the thermal radiation in a participating medium. Except in specified cases, there is no analytical solution for this equation. Solving the radiative transfer equation with numerical methods is usually time-consuming. This work presents a fast method based on proper orthogonal decomposition to solve the radiative transfer equation. Some variables are selected as independent parameters. The radiative transfer equation for the specified value of these parameters is solved using the discrete ordinates method, and the system responses form the snapshot matrix. The matrix is decomposed singular value decomposition as a product of three matrices. Due to the magnitude of singular values, only a few first columns of these matrices are selected. As a result, the degrees of freedom of the original system are decreased, and a reduced-order model is created. Employing the radial basis functions, the system response, corresponding to any arbitrary input vector (independent parameters), can be approximated with high speed. The results show that the reduced-order method has high accuracy compared to the numerical solution. The complexities of the system do not affect the reduced-order method. Regardless of the characteristics of the medium (the value of independent parameters), the solution time is the order of 0.02 seconds.

One of the most important issues facing today's society is the issue of energy production and the challenges surrounding it. For this reason, it is very important to address the issue of energy harvesting from various methods. One of these methods is energy harvesting from vibrations caused by fluid flow. Vibrations generated by the incompressible air fluid flow around three parallel piezoelectric blades behind a circular cylinder at different longitudinal distances can be one of the best options for examining and evaluating the amount of electrical voltage generated by piezoelectric blade vibrations. According to this study, a situation in which the middle piezoelectric blade is shifted by half the length of the blade to the right and the direction of the clamp is opposite to the direction of the clamp of the up and down blades is the optimal structure for voltage output and reducing collision probability. Due to the reduced probability of the blades colliding with each other in this optimal case, the maximum Reynolds number without the blades colliding increased from 2400 in non-optimal structures to 2600 in the optimal structure, which increased the voltage output in the middle blade by 12% and about 14% for up and down blades.

In this research, using the dynamic modes decomposition and using the basic concepts of the dynamical system, a data driven-physics informed reduced-order model has been developed for the viscous Burgers equation. Accordingly, based on the projection of the governing equation into the modal space, a reduced model is obtained according to the characteristics of the dominant modes. This model when used to simulate the field dynamics over a long time period, may become unstable. Therefore, an approach based on the concept of eddy viscosity closure has been used to stabilize the model behavior. This modification allows the reduced-order model to be a surrogate model of the original equation and predict the time evolution of the dynamical system with relative good accuracy. Comparing the results of the present reduced order model with the data obtained from the direct numerical simulations shows a high computational accuracy.

Today, the parabolic solar collector set is one of the most effective technologies for generating electricity. The performance of a solar cell changes under the accumulation of dust particles on its surface. In this research a three-dimensional flow model and the Eulerian approach with SST k-w based turbulence modeling are used to study the flow field around a set of solar cells, and the Lagrangian approach is used to model the displacement of dust particles. The numerical method used in this simulation is the SIMPLE Algorithm, whilst the upstream second-order method and the Trapezoidal method are applied for the particle motion equation integration. The results show an increase in the drag coefficient with the pitch angle. Also, the lift coefficient has an absolute maximum value at an angle of 30 degrees and approaches zero at the angles of zero and 90 degrees. In addition, the pitch torque at the center of the cell rotation has a maximum value at 30 and 45 degree pitch angles and the lateral torque has its maximum value at 90 ° angle. The particle settling behavior indicates that compared to other cells, the cell that faces the prevailing wind flow has more particle accumulation on it. Also, at low-speed airflow and high pitch angle, the particle deposition increases. The research outcomes show less efficiency reduction with increasing speed, and approximately the same efficiency at the pitch angle of 45, 60, and 75 degrees.

Numerical modeling and simulation is a useful tool for analyzing the dynamic behavior of engineering systems. Using these methods, especially for unsteady problems, usually requires a lot of time. For this reason, the development of fast speed algorithms and increased computational efficiency has always been an important issue for researchers. In this method, by decreasing the constraints of the system, the computational speed will dramatically increase without changing the dominant features of the problems. In this research, using the basic concepts of dynamical systems, two problems such as diffusion and convection-diffusion of equations are investigated independently and by using the proper orthogonal decomposition method, the reduced-order model for these equations have been created. Finally, for each of the problems, based on the projection of the related governing equations in the vector space of modes and using more energetic modes, a reduced-order model is obtained with respect to the orthogonal basis functions. The model obtained has been used to simulate the time evolutions of each problem. These equations can correctly replace with the primary equation and predict the behavior of the system with very good accuracy. Comparison of the results of the reduced-order model and direct numerical simulation shows high accuracy.

In this paper, the air flow field around buildings is simulated to predict the dispersion of the fine solid pollutants. The large eddy simulation approach has been used to model the turbulence flow. In the first part of this research, a simple model including a building has been simulated and the obtained results are compared and validated with the experimental data obtained from a wind tunnel test. By setting the optimal parameters of the numerical model in the first part, in the second part, an area of Tehran city with high-rise buildings and irregular urban layout is considered and the velocity field and deposition of the contaminant particles in this model are also simulated. The result obtained in the first part of show good agreement with the experimental data and in both models the effect of some variables like the arrangement of buildings (in urban model) and the wind velocity are investigated. To analyze the value of the pollutant concentration in the urban area at each time, the integral of this variable on some important surfaces has been calculated over time and the effect of urban area layout on the integration is discussed.

In this paper, a numerical study has been carried out for coupled mass, momentum and heat transfer in the field under effects of natural convection. For this purpose, the unsteady incompressible Navier-Stokes equations with the terms of the Buoyancy forces (due to temperature gradients), energy conservation and concentration (mass) transfer equations have been simultaneously solved using appropriate numerical methods. In order to discretize spatial terms, a combined formulation contains a second-order central difference method and the first-order upwind scheme has been used. Time integration of the governing equation has been performed using the fourth order Runge-Kutta method. The effect of variations of the mass of contaminant has been studied in changing the flow and thermal fields structure. It is concluded from obtained results, an increase in mass flow rate of secondary (mass) injection, alters the structure of the flow and thermal fields. Comparison of the results obtained from the numerical model with appropriate reference data shown that the model has relatively good accuracy.

In this article, a coupled computational framework is presented for the numerical simulation of mass transfer under the effects of natural convection phenomena in a field contains water-copper Nano-fluid. This CFD model is build up based on accurate algorithms for spatial derivatives and time integration. The spatial derivatives have been calculated using first order upwind and second order central differencing approaches. Also, time integration is performed using the fourth order Runge-Kutta method. A parametric reduced order model is developed to compute the whole flow field under the effects of some important parameters. This model is constructed using POD-snapshots method based on Karhunen-Loeve decomposition. The POD modes have been calculated based on the solution of an eigenvalues problem. The obtained eigenfunctions are POD modes which are arranged using energy-based criteria based on total kinetic energy of the flow field. This approach leads to the model order reduction procedure, and the outcome model can be used as a surrogate model of CFD high order model. The results obtained from the reduced order model show close agreements to the benchmark DNS data and proving high accuracy of the proposed model.

In this paper, a reduced order model is reconstructed for boundary control and excitation of the unsteady viscous Burgers equation. First, the standard reduced order proper orthogonal decomposition model, which has been extracted from the governing equations without control inputs, was evaluated and illustrated the satisfactory results in short time period. Two approaches are used to imply the effects of boundaries excitations and the related control routines. In the first, a source term was added to the governing equation of the reduced order dynamical system and was contributed as   an expansion of the proper orthogonal decomposition modes without control input. For removing the inhomogeneities on the boundaries, the boundaries values are subtracted from all of the snapshots with an appropriate control input. The other approach is based on the rewriting of the diffusion term as  an expanded form which contains the effect of boundaries values explicitly. In both approaches, the obtained reduced order models will contain two parts, the effect of system states and the influence of boundaries control functions. The results obtained from the reduced order model without the control inputs demonstrate a good agreement to the benchmark direct numerical simulations data and prove the high accuracy of the model.

Accumulation of consumable or produced materials in out-door areas and exposed to the airflow is an important issue of environmental concern. Also, wind erosion of raw materials from the storage piles in some industries causes environmental problems and economic consequences. The materials are accumulated in various volumetric shapes in an outdoor environment, which is usually pyramidal and conical. The subject of this study is about the percentage of wind erosion from the surface of the pile and the intensity of the particle deposition due to the free-stream flow passing through the various points of the pile’s surface. In this research, the wind erosion or in other words, the movement and deposition of particles from the surface of the pile have been discussed. So, a comparison between the piles with different shapes has been performed according to the reduction of wind erosion. So, a comparison between the piles with different shapes has been performed according to the reduction of wind erosion.

In the present work, two low-dimensional models are presented and used for vibration simulation of the linear and non-linear beam models. These models help to compute the dynamical responses of the beam with fast computation speed and under the effects of different conditions. Also the obtained results can be used in the conceptual and detailed design stages of an engineering system overall design. First, a finite element analysis based on Euler-Bernoulli beam elements with two primary variables (deflection and slope) at each node is used to find static and dynamic responses of the considered linear and non-linear beams. Responses to three different static load cases are obtained and applying them as initial conditions, the time responses of the beam are calculated by the Newmark's time approximation scheme. A low-dimensional POD model which was extracted from the ensemble under the effect of an arbitrary loading is reconstructed. To apply the model to simulate the response of beam under the effect of other loads, POD modal coefficients are updated due to change of initial condition. This modification is performed based on the recalculation of the eigenvalues due to a new initial condition. Also, another low-dimensional model is constructed which is developed based on an ensemble under the effect of several parameters. To apply the model to simulate the response of the beam under the effect of other loads and variations of beam thickness, POD-HOSVD modal coefficients are updated due to the change of desired parameters. The results obtained from the low-dimensional model are showing good agreement to the benchmark data and proving high level accuracy of the model.

In this article, an improved reduced order modelling approach, based on the proper orthogonal decomposition (POD) method, is presented. After projecting the governing equations of flow dynamics along the POD modes, a dynamical system was obtained. Normally, the classical reduced order models do not predict accurate time variations of flow variables due to some reasons. The response of the dynamical system was improved using a calibration method based on a least-square optimization process. The calibration polynomial can be assumed as the pressure correction term which is vanished in projecting the Navier-Stokes equations along the POD modes. The above least- square procedure is a combination of POD method and the solution of an optimization problem. The obtained model can predict accurate time variations of flow field with high speed. For long time periods, the calibration term can be computed using a combined form of POD and Fourier modes. This extension is a totally new extension to this procedure which has recently been proposed by the authors. The results obtained from the calibrated reduced order model show close agreements to the benchmark DNS data, proving high accuracy of our model.