Uniform Lorenz Forces Impact on Nanoparticles Transportation

Uniform Lorenz Forces Impact on Nanoparticles Transportation

DOI: 10.4018/978-1-5225-7595-5.ch002

Abstract

Natural convection under the influence of a magnetic field has great importance in many industrial applications such as crystal growth, metal casting, and liquid metal cooling blankets for fusion reactors. The existence of a magnetic field has a noticeable effect on heat transfer reduction under natural convection while in many engineering applications increasing heat transfer from solid surfaces is a goal. At this circumstance, the use of nanofluids with higher thermal conductivity can be considered as a promising solution. In this chapter, the influence of Lorentz forces on hydrothermal behavior is studied.
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1. Entropy Generation Of Nanofluid In Presence Of Magnetic Field Using Lattice Boltzmann Method

1.1. Problem Definition

The considered physical geometry with related parameters and coordinates are shown in Figure 1(a). A rectangular body with height t and width 978-1-5225-7595-5.ch002.m01 is placed in the center of the enclosure, is supposed to be isothermal at higher temperature than two vertical isothermal walls while the top and bottom walls are insulated. Also, it is also assumed that the uniform magnetic field (978-1-5225-7595-5.ch002.m02) of constant magnitude 978-1-5225-7595-5.ch002.m03 is applied, where 978-1-5225-7595-5.ch002.m04and 978-1-5225-7595-5.ch002.m05 are unit vectors in the Cartesian coordinate system. The orientation of the magnetic field form an angle 978-1-5225-7595-5.ch002.m06with horizontal axis such that 978-1-5225-7595-5.ch002.m07. The electric current J and the electromagnetic force F are defined by 978-1-5225-7595-5.ch002.m08 and 978-1-5225-7595-5.ch002.m09, respectively. In this section, 978-1-5225-7595-5.ch002.m10 is equal to zero (Sheikholeslami & Ganji, 2015).

Figure 1.

(a) Geometry of the problem; (b) Discrete velocity set of two-dimensional nine-velocity (D2Q9) model

978-1-5225-7595-5.ch002.f01

One of the novel computational fluid dynamics (CFD) methods which is solved Boltzmann equation to simulate the flow instead of solving the Navier–Stokes equations is called Lattice Boltzmann methods (LBM) (or Thermal Lattice Boltzmann methods (TLBM)). LBM has several advantages such as simple calculation procedure and efficient implementation for parallel computation, over other conventional CFD methods, because of its particulate nature and local dynamics. The thermal LB model utilizes two distribution functions, f and g, for the flow and temperature fields, respectively. It uses modeling of movement of fluid particles to capture macroscopic fluid quantities such as velocity, pressure and temperature. In this approach, the fluid domain discretized to uniform Cartesian cells. Each cell holds a fixed number of distribution functions, which represent the number of fluid particles moving in these discrete directions. The D2Q9 model was used and values of 978-1-5225-7595-5.ch002.m11 for 978-1-5225-7595-5.ch002.m12 (for the static particle), 978-1-5225-7595-5.ch002.m13 for 978-1-5225-7595-5.ch002.m14 and 978-1-5225-7595-5.ch002.m15 for 978-1-5225-7595-5.ch002.m16 are assigned in this model (Figure 1(b)). The density and distribution functions i.e. the f and g, are calculated by solving the lattice Boltzmann equation, which is a special discretization of the kinetic Boltzmann equation. After introducing BGK approximation, the general form of lattice Boltzmann equation with external force is as follow:

For the flow field:

978-1-5225-7595-5.ch002.m17
(1)

For the temperature field:

978-1-5225-7595-5.ch002.m18
(2) where 978-1-5225-7595-5.ch002.m19 denotes lattice time step, 978-1-5225-7595-5.ch002.m20 is the discrete lattice velocity in direction 978-1-5225-7595-5.ch002.m21, 978-1-5225-7595-5.ch002.m22 is the external force in direction of lattice velocity, 978-1-5225-7595-5.ch002.m23 and 978-1-5225-7595-5.ch002.m24 denotes the lattice relaxation time for the flow and temperature fields. The kinetic viscosity 978-1-5225-7595-5.ch002.m25 and the thermal diffusivity978-1-5225-7595-5.ch002.m26, are defined in terms of their respective relaxation times, i.e. 978-1-5225-7595-5.ch002.m27 and 978-1-5225-7595-5.ch002.m28, respectively. Note that the limitation 978-1-5225-7595-5.ch002.m29 should be satisfied for both relaxation times to ensure that viscosity and thermal diffusivity are positive. Furthermore, the local equilibrium distribution function determines the type of problem that needs to be solved. It also models the equilibrium distribution functions, which are calculated with Eqs. (3) and (4) for flow and temperature fields, respectively.
978-1-5225-7595-5.ch002.m30
(3)
978-1-5225-7595-5.ch002.m31
(4) where 978-1-5225-7595-5.ch002.m32 is a weighting factor and 978-1-5225-7595-5.ch002.m33 is the lattice fluid density.

In order to incorporate buoyancy forces and magnetic forces in the model, the force term in the Equation (2) need to calculate as below (Sheikholeslami & Ganji, 2015):

978-1-5225-7595-5.ch002.m34
(5) where A is 978-1-5225-7595-5.ch002.m35, 978-1-5225-7595-5.ch002.m36 is Hartmann number and 978-1-5225-7595-5.ch002.m37 is the direction of the magnetic field.

For natural convection, the Boussinesq approximation is applied and radiation heat transfer is negligible. To ensure that the code works in near incompressible regime, the characteristic velocity of the flow for natural 978-1-5225-7595-5.ch002.m38 regime must be small compared with the fluid speed of sound. In the present study, the characteristic velocity selected as 0.1 of sound speed.

Finally, macroscopic variables calculate with the following formula:

978-1-5225-7595-5.ch002.m39
(6)

According to Bejan (1982), one can find the volumetric entropy generation rate as

978-1-5225-7595-5.ch002.m40
(7) where HTI is the irreversibility due to heat transfer in the direction of finite temperature gradients and FFI is the contribution of fluid friction irreversibility to the total generated entropy.

In terms of the primitive variables, HTI and FFI become

978-1-5225-7595-5.ch002.m41
(8)

One can also define the Bejan number,978-1-5225-7595-5.ch002.m42, as

978-1-5225-7595-5.ch002.m43
(9)

Note that a 978-1-5225-7595-5.ch002.m44 value more/less than 0.5 shows that the contribution of HTI to the total entropy generation is higher/lower than that of FFI. The limiting value of 978-1-5225-7595-5.ch002.m45 shows that the only active entropy generation mechanism is HTI while 978-1-5225-7595-5.ch002.m46 represents no HTI contribution.

The dimensionless form of entropy generation rate, Ns, is defined as

978-1-5225-7595-5.ch002.m47
(10) one finds that
978-1-5225-7595-5.ch002.m48
(11) where the dimensionless temperature difference is defined as

978-1-5225-7595-5.ch002.m49
(12)

The dimensionless viscous dissipation function, addressed in Equation (11), takes the following form

978-1-5225-7595-5.ch002.m50
(13)

Here, Ge is the Gebhart number which is defined as (Sheikholeslami, Ashorynejad, & Rana, 2016).

978-1-5225-7595-5.ch002.m51
(14)

Average 978-1-5225-7595-5.ch002.m52 is denoted by978-1-5225-7595-5.ch002.m53, where the angle brackets show an average taken over the area, as:

978-1-5225-7595-5.ch002.m54
(15)

Selecting the fluid, trapped between the heated plate and the cavity, as the thermodynamic system, one observes that the amount of heat entered through the heated plate is equal to the one transferred to the surroundings via the isothermal walls. Moreover, one notes that the total volumetric entropy generation rate is obtainable as

978-1-5225-7595-5.ch002.m55
(16) where, in terms of 978-1-5225-7595-5.ch002.m56, it reads

978-1-5225-7595-5.ch002.m57
(17)

Applying perturbation techniques for small values of 978-1-5225-7595-5.ch002.m58, say 978-1-5225-7595-5.ch002.m59, one has

978-1-5225-7595-5.ch002.m60
(18)

The dimensionless entropy generation number can be obtained as

978-1-5225-7595-5.ch002.m61
(19)

In order to simulate the nanofluid by the lattice Boltzmann method, because of the interparticle potentials and other forces on the nanoparticles, the nanofluid behaves differently from the pure liquid from the mesoscopic point of view and is of higher efficiency in energy transport as well as better stabilization than the common solid-liquid mixture. For modeling the nanofluid because of changing in the fluid thermal conductivity, density, heat capacitance and thermal expansion, some of the governed equations should change. The effective density 978-1-5225-7595-5.ch002.m62, the effective heat capacity 978-1-5225-7595-5.ch002.m63, thermal expansion 978-1-5225-7595-5.ch002.m64 and electrical conductivity 978-1-5225-7595-5.ch002.m65 of the nanofluid are defined as:

978-1-5225-7595-5.ch002.m66
(20)
978-1-5225-7595-5.ch002.m67
(21)
978-1-5225-7595-5.ch002.m68
(22)
978-1-5225-7595-5.ch002.m69
(23) where 978-1-5225-7595-5.ch002.m70 is the solid volume fraction of the nanoparticles and subscripts 978-1-5225-7595-5.ch002.m71 and s stand for base fluid, nanofluid and solid, respectively.

978-1-5225-7595-5.ch002.m72 and 978-1-5225-7595-5.ch002.m73 are obtained according to Koo–Kleinstreuer–Li (KKL) model (Sheikholeslami & Kandelousi, 2014):

978-1-5225-7595-5.ch002.m74
(24)
978-1-5225-7595-5.ch002.m75
(25)

In order to compare total heat transfer rate, Nusselt number is used. The average Nusselt numbers are defined as follows:

978-1-5225-7595-5.ch002.m76
(26)

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