Sina T. Profile | Freelancer

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Matlab and Mathematica

5.0

(2 reviews)

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$15 USD / hour

istanbul,

$15 USD / hour

It's currently 1:04 AM here

Joined June 19, 2015

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Sina T.

@sinex123

5.0

(2 reviews)

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$15 USD / hour

istanbul,

$15 USD / hour

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Mathematical Modeling and Simulation

I have a passion for mathematical analysis and modeling. I can program in MATLAB, C/C++, and FORTRAN and I am familiar with numerical solution methods. I am a P.hd candidate in the field of heat transfer and fluid mechanics. My favorite subjects are radiation heat transfer, conduction heat transfer, optics and light scattering. I also have two years of experience in a mechanical design with Solid Works and Mechanical Desktop.

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Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

Transient heat transfer analysis in a slab with different boundary conditions. The solution of transient heat transfer should converge to steady state solution after a certain time. Here the slab is heating from the bottom by a heat flux function. The upper boundary condition is convection heat transfer with constant convective heat flux coefficient (h). The left and right B.C. are adiabatic. The slab is at uniform temperature at t = 0. We expect the temperature of different nodes in the slab converge to a fix value after passing time.

transient heat conduction analysis with finite difference

Transient heat transfer analysis in a slab with different boundary conditions. The solution of transient heat transfer should converge to steady state solution after a certain time. Here the slab is heating from the bottom by a heat flux function. The upper boundary condition is convection heat transfer with constant convective heat flux coefficient (h). The left and right B.C. are adiabatic. The slab is at uniform temperature at t = 0. We expect the temperature of different nodes in the slab converge to a fix value after passing time.

transient heat conduction analysis with finite difference

Transient heat transfer analysis in a slab with different boundary conditions. The solution of transient heat transfer should converge to steady state solution after a certain time. Here the slab is heating from the bottom by a heat flux function. The upper boundary condition is convection heat transfer with constant convective heat flux coefficient (h). The left and right B.C. are adiabatic. The slab is at uniform temperature at t = 0. We expect the temperature of different nodes in the slab converge to a fix value after passing time.

transient heat conduction analysis with finite difference

Transient heat transfer analysis in a slab with different boundary conditions. The solution of transient heat transfer should converge to steady state solution after a certain time. Here the slab is heating from the bottom by a heat flux function. The upper boundary condition is convection heat transfer with constant convective heat flux coefficient (h). The left and right B.C. are adiabatic. The slab is at uniform temperature at t = 0. We expect the temperature of different nodes in the slab converge to a fix value after passing time.

transient heat conduction analysis with finite difference

1974030

The name boids corresponds to a shortened version of bird-oid object, which refers to a bird-like object . The purpose of boid simulation is to have a simulation as close as possible to the natural phenomena of flocking behavior and also the behavior of the flock when a predator is attacking. In this case a panic wave emerge in the flock. The natural phenomena can be seen in here www.youtube.com/watch?v=ctMty7av0jc There are three rules that each bird or fish individually follows separation: steer to avoid crowding local flock mates alignment: steer towards the average heading of local flock mates cohesion: steer to move toward the average position (center of mass) of local flock mates The simulation shows that the animals behave individually and have no synchronization. But these three rules make the magnificent natural behavior. Each animal is just aware of the animals near it. But because of the panic wave propagation in the flock it can get the information about fast.

SIMULATION of BOIDS BEHAVIOUR WITH PREDATOR PRESENCE

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

Monte Carlo method is used for finding the radiative heat transfer between two parallel plate with an gas between them as a participating medium. The gas is scattering and absorbing. The finite element solution is verified with Monte Carlo solution.

Monte Carlo method for participating medium

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

We used Monte Carlo method for verifying our solution with finite difference solution for a radiative heat transfer in a slab. We have constant temperature or constant heat flux B.C in the walls.

Monte Carlo Method for Radiative Heat Transfer

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

Shooting method is used to solve Falkner Skan differential equation for different boundary conditions. The result used to find boundary layer thickness, shear stress, drag etc ...

Wedge and corner flow (Falkner Skan equation)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

One of the Difficulties encountered in the solution of Inverse Heat Transfer Problems (IHTP) is that they are mathematically classified as ill-posed in general sense, because their solutions may become unstable, as a result of the errors within measurements used in the analysis. In this project, we look at the heat equation defined on some domain containing a point heat source at a known location. The magnitude of the heat source is assumed to be unknown and vary with time. The values of transient temperature are given at other locations. The heat equation we consider is the linear heat equation and, thus, the resulting optimization is that of a linear function. Because of being ill-posed we have to introduce regularization to promote smoothness of the solution in time.

Inverse Heat conduction (Detection of heat source function)

Transient heat transfer analysis in a slab with different boundary conditions. The solution of transient heat transfer should converge to steady state solution after a certain time. Here the slab is heating from the bottom by a heat flux function. The upper boundary condition is convection heat transfer with constant convective heat flux coefficient (h). The left and right B.C. are adiabatic. The slab is at uniform temperature at t = 0. We expect the temperature of different nodes in the slab converge to a fix value after passing time.

transient heat conduction analysis with finite difference

Transient heat transfer analysis in a slab with different boundary conditions. The solution of transient heat transfer should converge to steady state solution after a certain time. Here the slab is heating from the bottom by a heat flux function. The upper boundary condition is convection heat transfer with constant convective heat flux coefficient (h). The left and right B.C. are adiabatic. The slab is at uniform temperature at t = 0. We expect the temperature of different nodes in the slab converge to a fix value after passing time.

transient heat conduction analysis with finite difference

Transient heat transfer analysis in a slab with different boundary conditions. The solution of transient heat transfer should converge to steady state solution after a certain time. Here the slab is heating from the bottom by a heat flux function. The upper boundary condition is convection heat transfer with constant convective heat flux coefficient (h). The left and right B.C. are adiabatic. The slab is at uniform temperature at t = 0. We expect the temperature of different nodes in the slab converge to a fix value after passing time.

transient heat conduction analysis with finite difference

Transient heat transfer analysis in a slab with different boundary conditions. The solution of transient heat transfer should converge to steady state solution after a certain time. Here the slab is heating from the bottom by a heat flux function. The upper boundary condition is convection heat transfer with constant convective heat flux coefficient (h). The left and right B.C. are adiabatic. The slab is at uniform temperature at t = 0. We expect the temperature of different nodes in the slab converge to a fix value after passing time.

transient heat conduction analysis with finite difference

1974030

The name boids corresponds to a shortened version of bird-oid object, which refers to a bird-like object . The purpose of boid simulation is to have a simulation as close as possible to the natural phenomena of flocking behavior and also the behavior of the flock when a predator is attacking. In this case a panic wave emerge in the flock. The natural phenomena can be seen in here www.youtube.com/watch?v=ctMty7av0jc There are three rules that each bird or fish individually follows separation: steer to avoid crowding local flock mates alignment: steer towards the average heading of local flock mates cohesion: steer to move toward the average position (center of mass) of local flock mates The simulation shows that the animals behave individually and have no synchronization. But these three rules make the magnificent natural behavior. Each animal is just aware of the animals near it. But because of the panic wave propagation in the flock it can get the information about fast.

SIMULATION of BOIDS BEHAVIOUR WITH PREDATOR PRESENCE

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5.0

$44.00 USD

Great helper and well communicator. Always willing to help. Recommended to all.

Muhammad Rashid R.

@rashidriaz32

•

9 years ago

5.0

$10.00 USD

Solve mathematical optimization problem with cvx

Excellent freelancer. Always available and has good communication skills. Surely rehire.

Matlab and Mathematica

Ben B.

@hyperqq

•

9 years ago

Education

MS of Mechanical Engineering

Bogaziçi Üniversitesi,

Turkey

2013 - 2015

(2 years)

Qualifications

ETS

2014

Total score 102, with writing 25, speaking 26, speaking 26, writing 25

Publications

Light Scattering from Defected Nano Sized Objects on a Surface with DDA-SI

Electromagnetic & Light Scattering XV, Leipzig, Germany

Study of light scattering of nano size objects on a surface for defect detection

Heating of Noble Metal Nano-structures on a Dielectric Surface Due to Plasmonic Resonance

First Thermal and Fluids Engineering Summer Conference, New York

Study of heating of nano structures on dielectric surfaces with laser illumination with effect of a probe

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Matlab and Mathematica

2

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