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FVM Literature Review

OBJECTIVES: 1. DEFINE WHAT IS FVM(FINITE VOLUME METHOD). 2. DIFFERENTIATE BETWEEN FDM AND FVM. 3. DESCRIBE THE NEED FOR INTERPOLATION SCHEMES & FLUX LIMITERS IN FVM. THEORY: We know that in Fluid Dynamics there are three numerical-method based solvers which are used to solve for…

STAR-CCM

Sagar Biswas
updated on 14 Nov 2022

OBJECTIVES:

1. DEFINE WHAT IS FVM(FINITE VOLUME METHOD).

2. DIFFERENTIATE BETWEEN FDM AND FVM.

3. DESCRIBE THE NEED FOR INTERPOLATION SCHEMES & FLUX LIMITERS IN FVM.

THEORY:

We know that in Fluid Dynamics there are three numerical-method based solvers which are used to solve for the governing equation of Navier-Stokes:

Finite Difference Method
Finite Element Method
Finite Volume Method

Finite Difference Method(FDM): The finite difference method (FDM) is an approximate method for solving partial differential equations. Basically, it converts linear ordinary differential equations(ODEs) or non-linear partial differential equations(PDEs) into a system of equations that can be solved using matrix algebraic techniques. Here, the grid should be linear & must be aligned in such a way so that all the grid points lie in a straight line. Here, the governing equation's properties are said to be concentrated at each and every point along the grid.

Finite Element Method(FEM): This method consists of the pictorial representation of the solutions in terms of defined basis functions. Here, the computational domain is partitioned into smaller domains of finite elements & the solution is built from the basis functions in each element. The actual equations are usually founded by restating the conservation equations in a weak form. Here, the field variables should be stated in terms of the basis functions, then the equation is multiplied by the appropriate test functions & the equation is then integrated over an element. Since the finite element method's solution is expressed in terms of specific basis functions hence far more information is available when compared to FDM or FVM.

Finite Volume Method(FVM): FVM is a discretization method using which we can approximate a single PDE or even a system of PDEs that expresses the conservation of one or more quantities. These PDEs are often regarded as conservation laws that can be of various nature such as elliptic, parabolic, hyperbolic, etc. It is used to describe the relations between partial derivatives of unknown fields such as temperature, pressure, molar fraction, concentration, the density of electrons, etc with respect to the variables within the domain(time, space, etc) under consideration.

Here in FVM, a mesh is being generated that consists of a partition of the domain where the space variables reside. Elements of those meshes are termed as control volumes where the integration of those PDEs over each control volume results in a balanced equation. Then, those sets of balanced equations are then discretized with respect to the set of discrete unknowns. The issue here is that the discretization of those fluxes at the boundaries of each control volume as in order for the FVM to be efficient, the numerical fluxes are generally conservative and consistent. When the numerical fluxes are conservative then the flux entering a control volume from its neighbor should be the opposite of the one entering the neighbor from the control volume. When the numerical fluxes are consistent then the numerical flux of a regular function interpolation tends to the continuous flux as the mesh size vanishes.

Sometimes it is possible to discretize the fluxes at the boundaries of the control volume by the FDM and then it is referred to as the conservative finite difference method. Specifically, in the case of FVM with respect to FDM, the discretization can be performed on the local balance equations, rather than on the PDEs, and hence the fluxes on the boundaries of the control volumes are discretized, rather than the continuous differential operator.

CONSIDERING 1D LINEAR HEAT CONDUCTION EQUATION:

$\frac{\partial}{\partial x} = (α \cdot \frac{\partial T}{\partial x}) + S = 0$ $(del)/(delx) = (alpha*(delT)/(delx))+S = 0$

Here,

$α =$ $alpha =$ Thermal Diffusivity

S = Source Term

$\frac{\partial T}{\partial x} =$ $(delT)/(delx) =$ Temperature gradient with respect to x

Considering the Volume Integral:

$\int (\frac{\partial}{\partial x} (α \cdot \frac{\partial T}{\partial x}) + S) \cdot d V = 0$ $int((del)/(delx)(alpha*(delT)/(delx))+S)*dV = 0$

$\int \frac{\partial}{\partial x} (α \cdot \frac{\partial T}{\partial x}) d V + \int S \cdot d V = 0$ $int(del)/(delx)(alpha*(delT)/(delx))dV+intS*dV = 0$

We know, $d V = A \cdot d x$ $dV = A*dx$

DIFFERENCE BETWEEN FINITE DIFFERENCE METHOD & FINITE VOLUME METHOD:

SERIAL NO.	FACTORS OF DIFFERENCE	FINITE DIFFERENCE METHOD(FDM)	FINITE VOLUME METHOD(FVM)
1.	GRID GENERATION	In the case of FDM, the domain is divided into numerous points that are referred to as grid points each of which represents the respective variable of interest.	In the case of FVM, the domain is divided into numerous control volumes mostly referred to as cells which consist of variables of interest that are located at the centroid of each control volume or cell.
2.	MODE OF DISCRETIZATION	In the case of FDM, the discretization is based on the derivatives since the differential form of governing equations is replaced by a system of algebraic equations at each grid point.	In the case of FVM, Volume integrals in a PDE that contains a divergence term are then converted into surface integrals by using the divergence theorem. These terms are then evaluated as fluxes at the surfaces of each finite volume.
3.	APPLICABILITY	For FDM, it is almost mandatory to use structured grids as otherwise the conservation laws will be not be preserved.	The control volume can have grids in the shape of structured or unstructured grids. The conservation principles will be maintained in both cases regardless of the type of mesh. Conservation principles including mass, momentum, and energy are maintained by definition.
4.	NUMERICAL SCHEMES	Numerical Schemes which are applicable in the case of FDM are Forward differencing, Central differencing & Backward differencing.	Numerical Schemes which are applicable in the case of FVM are Upwind differencing, Hybrid differencing, Power-law which is used for giving a more accurate approximation to the one-dimensional exact solution
5.	ADVANTAGES	Implementation is easier, Higher-order schemes are available & it requires lower computational cost in general along with improved efficiency when compared to FVM while solving problems of similar nature as it uses simpler grid structures. If one can overcome the boundary-condition problem on curved boundaries, FDM gives very efficient and high-quality results.	FVM’s most significant advantage is that it only needs to do flux evaluation for the cell boundaries. Also, this is true for nonlinear issues as well, which, in turn, makes it an excellent choice for the handling of (nonlinear) conservation laws appearing in transport problems. The accuracy of the FVM, for example, close to a corner of interest, can be increased by refining the mesh around that corner.
6.	DISADVANTAGES	We have to use the mapping functions in methods like the variable transformation technique where it is required when the points are not aligned along with the cartesian coordinates. The Finite difference method is more difficult to use for handling material discontinuities. It doesn't lend itself for local grid refinement or adaptive mesh refinement that is needed to resolve local rapid variations in solutions such as around a corner of a complex shape and hence it becomes increasingly challenging when dealing with more complex geometrical features.	In FVM, the functions that approximate the solution when using the finite volume method cannot be easily made of high-order as the presence of false diffusion exists since lower-order accuracy upwind schemes are preferred due to stability issues in higher-order accurate schemes like central differencing, and hence it is difficult to achieve higher-order schemes in comparison to FDM.
7.	APPLICATIONS	We can deal with many physical problems using FDM such as predictions of quantities based on models developed to study weather along with its attributes, It has applications in the field of astrophysics and seismology as well. There aren't any known commercially available software using FDM although research codes are being developed to solve selective physical problems.	Using FVM, evaluation of external aerodynamics features of aircraft bodies, analysis of various kinds of heat transfer modes and mechanisms such as analysis of heat dissipation in case of a graphic processing unit(GPU) within an enclosed domain, assessment of wells in petroleum engineering, etc. COMMERCIAL SOFTWARES USING FVM: OpenFOAM, ANSYS CFX & FLUENT, PHOENICS, STAR-CD, STAR-CCM+

DIFFERENTIAL FORM OF FDM:

DIFFERENTIAL FORM OF FVM:

INTERPOLATION SCHEMES IN FINITE VOLUME METHOD:

The interpolation or the approximation schemes are required in the FVM method for the conversion of the integral form of the governing equation to an algebraic equation which can then be solved using various numerical methods. These schemes also play a significant role in the calculation of the values of the flow quantities in the point of concern from the values that are already known from their neighboring faces/cells. Some of the interpolation schemes are listed as follows:

Upwind Interpolation
Linear Interpolation
Quadratic Upwind Interpolation Scheme(QUICK)
Hybrid, TVD(Total variation dimension) & ENO(Essentially non-oscillatory) interpolation scheme.

1. Upwind Interpolation:

The upwind interpolation scheme is essentially used for convection-dominated problems. This interpolation scheme is essentially equivalent to using the forward difference method or backward difference method depending on the flow direction. Here, the direction of velocity at a given point determines the variable value to be used in an interpolation process. As the name suggests, we use the node upwind or upstream from the given node.

The upwind interpolation(UDS) for approximating the value of variable 'f' at the east face of the control volume is given by:

Upwind interpolation is the only approximation that satisfies the boundedness criterion unconditionally in the sense that irrespective of the velocity values and grid spacing, the solution will always remain bounded. This is the use of upwind interpolation which will never yield oscillatory solutions.

Upwind Interpolation is numerically diffusive. If we have sharp peaks or oscillatory values which are actually present in the solution then those would be damped out because of the presence of numerical diffusion and hence numerical diffusion is magnified in multi-dimensional flows, especially in cases where the flow is oblique to the grid space. Peaks or rapid variations in the variables will be smeared out because of the introduction of an additional diffusive coefficient which is called a numerical diffusive coefficient when UDS is used. It is first-order accurate and hence grids are required to obtain accurate solutions.

2. Linear Interpolation(CDS):

The approximated value of the variable at control volume centroid by linear interpolation of the values at two nearest computational nodes.

$f \underset{e}{}$ $fundersete$ = $f E λ \underset{e}{}$ $fElamdaundersete$ + $f \underset{p}{}$ $fundersetp$ $(1 -$ $(1-$ $λ \underset{e}{}$ $lamdaundersete$ )

where $λ \underset{e}{}$ $lamdaundersete$ = $x \underset{e}{}$ $xundersete$ - $x \underset{p}{}$ $xundersetp$ / $x \underset{E}{}$ $xundersetE$ - $x \underset{P}{}$ $xundersetP$

The linear interpolation is equivalent to the use of the CDS formula of the first-order derivative, and hence this scheme is also termed as Central Differencing Scheme(CDS).

This is 2nd-order accurate and may produce oscillatory results therefore small grids are preferred for this method. This is one of the most widely used interpolation methods.

3. Quadratic Upwind Interpolation(QUICK):

The approximated values of the variable at the control volume's centroid by quadratic interpolation of the values at the three nearest computational nodes which consist of one downstream node 'D' and two upstream nodes 'UU'.

$f \underset{e}{}$ $fundersete$ = $f \underset{U}{}$ $fundersetU$ + $g 1 ($ $g1($ $f \underset{D}{}$ $fundersetD$ - $f \underset{U}{}$ $fundersetU$ ) + $g \underset{2}{}$ $gunderset2$ ( $f \underset{U U}{}$ $funderset(UU)$ - $f \underset{U}{}$ $fundersetU$ )

$g \underset{1}{}$ $gunderset1$ = ( $x \underset{e}{}$ $xundersete$ - $x \underset{U}{}$ $xundersetU$ )( $x \underset{e}{}$ $xundersete$ - $x \underset{U U}{}$ $xunderset(UU)$ )/( $x \underset{D}{}$ $xundersetD$ - $x \underset{U}{}$ $xundersetU$ )( $x \underset{D}{}$ $xundersetD$ - $x \underset{U U}{}$ $xunderset(UU)$

$g \underset{2}{}$ $gunderset2$ = ( $x \underset{e}{}$ $xundersete$ - $x \underset{U}{}$ $xundersetU$ )( $x \underset{e}{}$ $xundersete$ - $x \underset{D}{}$ $xundersetD$ )/( $x \underset{U U}{}$ $xunderset(UU)$ - $x \underset{U}{}$ $xundersetU$ )( $x \underset{U U}{}$ $xunderset(UU)$ - $x \underset{D}{}$ $xundersetD$ )

Quadratic Upwind Interpolation is third-order accurate in both uniform and non-uniform grids.

4. Hybrid Interpolation scheme:

This interpolation method is a blend of both central differencing scheme and Upwind differencing scheme which is based on local peclet number. It can be described by the general partial equation as follows:

Here, $ρ$ $rho$ is density, 'u' is the velocity vector, is the diffusion coefficient & $S \underset{ϕ}{}$ $Sundersetphi$ is the source term. In this equation property, $ϕ$ $phi$ can be temperature, internal energy, or component of velocity vector u in x, y, and z directions.

For one-dimensional analysis of the convection-diffusion problem in steady-state and without the source, the equation reduces to,

Here, Peclet number(Pe) is a non-dimensional parameter used as a measure of the relative strengths of convection and diffusion given as:

The grid used for discretization in Central Difference Scheme:

The grid used for discretization in Upwind Difference Scheme for positive Peclet number (Pe>0):

The grid used for discretization in Upwind Difference Scheme for negative Peclet number (Pe < 0):

FLUX LIMITERS:

For convective fluid flow, it has been observed that low-order schemes are usually stable but quite dissipative in nature around points of discontinuity or shocks while the higher-order schemes are unstable in nature and show oscillations in the vicinity of discontinuity. Highly accurate oscillation-free schemes are known as high-resolution schemes. Flux limiters are used to tune higher and lower order schemes in such a way that the resulting scheme gives a higher-order accuracy in the smooth region of the flow and maintains first-order accuracy in the vicinity of shocks and discontinuities. For such a scheme total variation diminishing(TVD) criteria is being maintained of which basic form is as follows:

F( $u \underset{i + \frac{1}{2}}{}$ $u underset(i+1/2)$ ) = $f^{l o w}$ $f^(low)$ (i+1/2)- $ϕ$ $phi$ ( $r \underset{i}{}$ $runderseti$ )( $f^{l o w}$ $f^(low)$ ( $i + \frac{1}{2}$ $i+1/2$ )- $f^{h i g h}$ $f^(high)$ ( $i + \frac{1}{2}$ $i+1/2$ ))

F( $u \underset{i + \frac{1}{2}}{}$ $u underset(i+1/2)$ ) = $f^{l o w}$ $f^(low)$ (i-1/2)- $ϕ$ $phi$ ( $r \underset{i - 1}{}$ $runderset(i-1)$ )( $f^{l o w}$ $f^(low)$ ( $i - \frac{1}{2}$ $i-1/2$ )- $f^{h i g h}$ $f^(high)$ ( $i - \frac{1}{2}$ $i-1/2$ ))

where,

$f^{l o w}$ $f^(low)$ = low precision flux(1st order accurate)

$f^{h i g h}$ $f^(high)$ = high precision flux(higher order accurate)

$ϕ (r) =$ $phi(r) =$ flux limiter function where, r = $u \underset{i}{}$ $u underseti$ - $u \underset{i - 1}{}$ $u underset(i-1)$ / $u \underset{i + 1}{}$ $u underset(i+1)$ - $u \underset{i}{}$ $u underset(i)$

Most common forms of flux limiters functions are as follows:

CHARM, HCUS, HQUICK, koren, minmod, etc.

REFERENCES:

1. WIKIPEDIA

2. https://www.cfd-online.com/

3. 3Blue1Brown