The Smoothed Particle Hydrodynamics (SPH) method is a powerful particle
approach which was originally developed for the study of astrophysics
(Monaghan and Gingold, 1983; Monaghan, 1988) and later employed to study
the wave overtopping over coastal structures (Dalrymple and Rogers, 2006;
Gomez-Gesteira and Dalrymple, 2004). In the simulations of incompressible
flows using the SPH model, the incompressibility is achieved by employing
an equation of state and the fluid is assumed to be slightly compressible,
which is called the weakly compressible SPH method. In SPH method, materials
are approximated by particles that are free to move around rather than
by fixed grids or meshes. The particles are basically moving interpolation
points that carry with them physical properties, such as the mass, velocity,
viscosity, density of the fluid and any other properties that are relevant.
The inter-particle forces are calculated by smoothing the information
from nearby particles in a way that ensures that the resultant particle
motion is consistent with the motion of a corresponding real fluid, as
determined by the Navier-Stokes equations.
Despite its fully Lagrangian property, when the standard formulation
of SPH is applied to multi-phase flows only small density differences
are permitted between the considered phases because it is implicitly assumed
that the density gradient is much smaller than that of the smoothing kernel
(Monaghan, 1994; Monaghan and Kocharayan, 1995; Monaghan et al.,
1999). As a remedy, Ritchie and Thomas (2001) suggest a summation of the
particle-averaged pressure, not density, to handle large density gradients.
However, their method does not satisfy mass conservation. Colagrossi and
Landrini (2003) modified the approximation form of spatial derivatives
to diminish the effects of large density difference across the interface.
Nevertheless, since the density summation is replaced by a non-conservative
density evolution equation mass conservation is not satisfied either.
Although the conservation errors are decreased somewhat by a special density
re-initialization approach, they may accumulate and affect the flow behavior
considerably in long time computations. Hu and Adams (2006) employed a
particle smoothing function in which neighboring particles only contribute
to the specific volume but not to the density. The resulting algorithm
resolves a density discontinuity at a phase interface naturally and satisfies
mass conservation since a density summation equation was employed, this
method was suffering from clumping and clustering, so Hu and Adams (2007)
developed an incompressible SPH model which solves the Poisson equation
for pressure by a projection formulation. This model is a good model for
two phase flows. The scope of this study is finding a quick and simple
way for correction the standard semi-compressible SPH model, for high
density difference multiphase flows. The essential step is that by changing
the mass of particles which have interaction with particles from a different
phase, the undesirable force between two particles is controlled, this
simple idea improves the results very well and comparison model`s results
with previous numerical, analytical and experimental results supports
In the present study, first the general concepts of the standard SPH
modeling for incompressible flows are given. On this ground, the present
SPH-implementation is introduced and described in details and derived
graphs for mass coefficient in a case that light fluid is surrounded by
a heavy fluid (e.g., bubble rising in water) are presented. Using these
graphs the lock-exchange flow for different fluids is simulated and compared
with experimental works and other numerical models.
MATERIALS AND METHODS
SPH is a particle based method for modeling coupled fluid flows, solid
structure deformation and heat transfer. The particles represent blobs
of discretised fluid or solids that move around in response to the fluid
or solid stresses produced by the interaction with other particles. Importantly,
SPH does not use any fixed grids or meshes to track the fluid and calculate
the fluid velocities. Formally, SPH is a Lagrangian continuum method for
solving systems of partial differential equations. The fluid (or solid)
is discretised and the properties of each of these fluid/solid elements
are attributed to their centers, which are then interpreted as a particle.
SPH uses an interpolation kernel to smooth the values of any information
held by the particles to give smooth continuous interpolated fields.
Governing equations: The almost incompressible medium water is
not made totally incompressible but more compressible in SPH. The full
mass conservation equation has to be used:
The Eq. 1a-c give a time derivative of the density,
velocity and the position of fluid particles. ρ, u, v, x, t represent
the density, velocity, viscosity, position and time, respectively and
fd is a body force such as gravity. With a relation between
the pressure and the density it is no longer necessary to solve the implicit
Poisson equation for the pressure.
Numerical model: In SPH, the fluid field is represented as a collection
of N neighbor particles, interacting each other through evolution equations
of the general form:
where, ρi, ui, Pi, mi,
xi are the density, velocity, pressure, mass and position of
particle i, fi is a body force, IIij is the
viscous term and Wij = W(rij, h) is the smoothing
function. rij = ri−rj is the position
vector from particle j to particle i and h is the smoothing length. By
neglecting the atmospheric pressure, the state equation between pressure
and density is as follow (Batchelor, 1974):
where, ρi and ρi are the reference pressure
and density of fluid, γ = 7, P0 = c2ρ0/γ
and c is the used speed of sound and instead of using the real sound speed
which needs using a very small time step for the stability reasons, an
artificial sound speed of c ≈ 10 μmax is used in
SPH. With this value for speed of sound, the maximum relative density
differences are small in order of ~ 1% (Monaghan, 1994). Now the calculation
time stays reasonable and water is only slightly compressible in SPH.
In multi-phase flows P0, ρ0, c and γ may
be different for each phase. The quartic spline kernel which is used here
is in this form
where, q = rij/hij, rij is the absolute
distance between particles i and j and. The spatial derivative of kernel
function for two dimension problems is calculated as:
The choice of the smoothing length h is determining the number of interactions
for each particle. When h is too small, there are not enough particles
nearby to interact with, giving a low accuracy. When h is too big, local
properties are smeared out too much, this gives a low accuracy again and
the calculation becomes slow. Here an adaptive kernel function is used
by defining a variable smooth length, using the following equation (Monaghan,
where, d is the number of dimensions. According to this formula the smooth
length is increasing by decreasing in density and vice-versa. So, in places
such as interface between two phases or near the boundaries, kernel function
adopts itself to the conditions. With a smoothing length around 1.4 times
the initial particle spacing, Δp, the circle of influence has a radius
of 3.5 and 2.8 Δp for quartic and cubic spline functions, respectively.
With this smoothing length every particle has 36 and 20 neighbors in two
dimensions for mentioned functions, respectively. That is a good optimum
for both accuracy and calculation speed.
II ij is the viscous term and is defined as an artificial
viscous stress term. Different methods for defining this term have been
produced by scientists (Monaghan, 1994; Cleary, 1998; Gomez-Gesteira and
Dalrymple, 2004). One of the prevalent definitions which is used in this
study is (Monaghan, 2005).
is the average density. ,
this is the velocity difference between two particles.
is the constant for defining the viscosity difference between two particles
and α is chosen normally between limits 1 > α > 0. β
is a coefficient which in all simulations is equal to 2. This artificial
viscosity introduces both shear and bulk viscosity, but with neglectable
changes in the density it is almost entirely shear viscosity. This approach
to model viscosity guarantees that momentum is transferred from the particle
with the highest velocity to the particle with lowest velocity.
Colagrosi and Landrini (2003) showed that the averaging of the densities
helps ensure that the free surfaces are smooth and physically acceptable
and Dalrymple and Rogers (2006) performed this filtering every 40 time
steps by reinitializing the density of each particle according to:
In this study, the reinitialisation of density is done every 40 time
steps for each phase separately, it prevents of density deviations and
keeps the density of particles near to reference density.
The XSPH correction for movement of particle is included according to
Eq. 9, it means an average between the particle`s velocity
and the average field velocity from all neighbor particles is used for
moving a particle in Eq. 2c. This smoothing on velocity
prevents particles penetration. Also for consistency reasons, the smoothed
velocity has to be used in continuity equation. It should be noted that
the new smoothed velocity is used just in Eq. 2a and
c and in the momentum equation the non-smoothed velocity
is used (Monaghan, 1989).
where, ε is a constant (0 < ε < 1). With ε =1 the
particles are moved with the field velocity, with ε = 0 the particles
are moved with their own velocity, normally ε = 0.5.
Time integration: By using the SPH approximation, three differential
equations should be integrated in time:
These differential equations are integrated in time by using the predictor-corrector
time integration which predicts the velocity, density and particle position
at the intermediate time steps, as follows:
where, An, An+1/2 and An+1 denote the
value of variable A at the beginning, intermediate and at the end of (n+1)th
time step, respectively.
With these predictors the time integration is second order accurate.
The time step, δt, is calculated according to the Courant condition
modified for the existence of viscosity:
δto is the initial time step, h is the smoothing length,
σ is a coefficient between zero and one, normally σ = 0.5 and
vσ is defined in Eq. 7b.
Boundaries: Boundaries in SPH can be modeled by virtual particles
that characterize the system limits. Basically, three different types
of particles can be distinguished:
||Boundary particles with repulsive forces: The closed boundary
is modeled with a single row of boundary particles with mass, fixed density
equal to fluid particles density and no pressure. Their positions are
fixed or external imposed during the calculation (Fig.
1a). A repulsive boundary force is used to stop approaching fluid
particles in analogy with the forces among molecules (Monaghan, 1994,
||Quasi fluid boundary particles: One or more rows of fixed fluid
particles are placed at the boundary. No estimates of boundary forces
or directions are needed; the boundary particles just like fluid particles
verify equations of continuity and state, except that their position is
fixed or externally imposed in time. They build up pressure just like
normal fluid particles; this is how they prevent particle penetration.
Fluid particles near the boundary have interaction with a double staggered
row of quasi-fluid particles. When a slip boundary is needed these quasi
fluid particles are used in the calculation of the viscous stress terms,
for a free-slip boundary they are not used (Crespo et al., 2007).
Modeling of closed boundaries in SPH method, (a) boundary
particles with repulsive force and (b) ghost particles, dark particles
are fluid and gray particles are ghost particles
||Ghost particles: For every particle within a distance shorter
than the kernel support radius (e.g., 2 hi for cubic spline
function) from the boundary, a virtual (ghost) particle is mirrored outside
the boundary every time step (Randles and Libersky, 1996). Both particles
have same mass, density and pressure, but velocities normal to boundary
are opposite. The velocities parallel to boundary can be the same in sleep
boundaries and opposite for non-slip ones (Fig. 1b).
In this research the closed boundaries are simulated by ghost particles;
the main advantage of this method is that boundary is compatible with
different phases and it can adopt itself with fluid particles. Because
we are working with particles with different characteristics, especially
with different mass and densities, the boundary is adaptive with particles.
By using fixed particles for closed boundary, either quasi-fluid particles
or repulsive force particles, there will be difficult to simulate an adaptive
boundary which can be compatible with different phases. Also, using particles
with repulsive forces impose an undesirable kinetic energy in system which
needs some initial time steps for damping this energy. For problems which
start with an instability such as bubble rising or dam breaking, initial
damping of the kinetic energy is difficult but by using ghost particles
the amount of initial kinetic energy is small and there is no need to
In this study, the bottom and wall friction are defined by selecting
the velocity of ghost particles in direction parallel to boundary in this
where, ug and uf are the parallel to boundary velocity
components of ghost and fluid particles, respectively and Cv
is a coefficient which defines the amount of friction and slip or non-slip
boundary (−1≤Cv≤1), in Fig. 1b
this coefficient is equal to 0.3.
Correction in standard SPH: The standard SPH which is used for
simulating two liquids with different density ratios, works well for density
ratios greater (ρl/ρd) (than 0.1,
but for smaller density ratios it suffers from different problems. At
smaller density ratios, because particles attain large velocities in opposite
directions on the two sides of the interface, the instability occurs sooner
and the solution rapidly deteriorates. We understood that the main source
of increasing velocities in the interface is the mass differences between
two particles which are belong to two separate fluids; this mass difference
in momentum equation causes the increase of the rate of velocity changes.
So, we concentrated on changing the mass of particles which have interaction
with particles from other fluid artificially. Two coefficients have been
defined as cmd and cml which are the coefficient
of mass of dense fluid and light fluid, respectively. When a particle
from denser fluid interacts with a particle from lighter fluid, the masses
of these particles are changed as follows:
In order to finding a regular method for defining these coefficients,
several tests with different particle sizes for different density ratios
have been done for evolution of rising a circular bubble through the initially
still heavier fluid. The brief of these simulations is that, as density
ratio goes to one or particle size goes to zero, the value of coefficients
cmd and cml goes to one.
The bubble rising in a heavier liquid: We found that numerical
stability of a two-phase flow model with SPH in a case that lighter fluid
is surrounded by a denser fluid is more difficult than a case which denser
fluid is surrounded by lighter fluid. In this case a circular bubble of
fluid A is free to rise through the initially still fluid B. we suppose
that surrounding liquid is water (ρw = 1000 kg m-3)
and the simulations are done for bubbles with different characteristics.
According to the experimental and analytical results (Batchelor, 1974;
Liu and Zheng, 2006; Kwaguch et al., 2006), if the volume of a
bubble in water increases beyond 0.0006 cc, the bubble becomes oblate,
owing to the variation of pressure in the water over the surface of the
bubble and it is also observed to rise in an oscillatory manner. Further
increasing in the bubble volume is accompanied by a progressive flattening
of the rear face of the bubble and for volume above 5 cc, for which surface
tension is negligible, the bubble is shaped like an umbrella. The rear
face is rather unsteady and the edge of the slice is jagged and irregular,
but by contrast the whole of the front appears to be steady, smooth and
closely spherical or in two dimension circular (Fig. 2).
Here we consider the general shape of bubble and the speed U with which
the bubble rises through the water. According to analytical solution of
Batchelor (1974) and comparison with experimental results (Davis and Taylor,
1950) for a wide range of bubble sizes and different liquids the following
formula has been produced for two-dimensional bubbles:
||Photograph of air bubble in nitrobenzene; R = 3 cm,
U = 0.37 m sec-1 (Simplified from Davis and Taylor, 1950)
Comparison of SPH results (—) for bubble rise (rear
face of bubble) with analytic formula (−) (Batchelor, 1974),
ρl/ρd = 0.001, cmd
= 1.0, cml = 2.5, dp = 0.04 and R = 0.28 m
where, R is the radius of curvature of the bubble surface and g is the
gravity acceleration. ρd and ρl are, respectively the density of surrounding liquid and bubble. In the
case of bubble rising, if cmd be selected less than one, particles
of bubble will spread among water particles and this is not desirable,
also for high density ratio using mass coefficients equal to one result
in deterioration of model. So for balance the forces from near interface
particles, the mass of lighter particles should be increased by selecting
cml greater than one. The position of rear face of air
bubble, ρ = 1 kg m-3 with radius equal to 0.28 m in a
tank of water, ρw = 1000 kg m-3 is compared
with analytical formula (Eq. 16). There is a time lag
between two results, because in SPH model, an initial interaction between
particles, causes a bit decreasing in volume of bubble, but by shifting
the graph of SPH to left a good agreement is considered (Fig.
3). In this simulation the artificial viscosity coefficients of air
and water are 0.001 and 0.01, respectively. For finding proper cml for bubbles with different densities in water and for different particle
sizes, the shape and speed of rising bubble are considered. The final
values for cml are shown in Fig. 4.
In all simulations which have been done for finding the proper cml,
an agreement similar to Fig. 3) should be achieved and
the shape of rising bubble should be similar to Fig. 2 and 5.
By rise of bubble, the circle or changes to a mushroom shape and then
the tail of bubble is separated by vortex current and finally there is
an umbrella shape bubble with a flat rear face (Fig. 2,
5). By increasing the density ratio and decreasing the
particle size and increasing the resolution of model the amount of cml
is going to one. In all simulations the value of cmd is equal
to one, just for density ratios larger than 0.5, for reaching at exact
speed of bubble rising, there is need to increasing the mass of surrounding
particles by choosing cmd greater than one, in these cases
cml should be chosen greater than one for keeping the
shape of bubble in correct form.
The values for cml against density
ratio of two fluids. Different graphs show different particle sizes;
-◊-, -□- and -Δ- represent particle size of 0.02,
0.04 and 0.05, respectively
||Air bubble rising in water, The frames show the position
and shape of bubble from bottom to top at times 0.0, 0.2, 0.3, 0.4,
0.6, 0.8 and 1.0 sec. The conditions are similar to Fig.
For this case study one fluid was surrounded by another one, completely,
but in a lot of practical problems, there is a changeable interface between
two phases. In these cases it is possible to choose special amounts for
cml and cmd by an engineering judgment according
to results of this section; here we want to model lock-exchange between
two fluids with high density ratios using the mentioned method.
High density ratio lock-exchange flow: Lock-exchange flow is a
good test for direct numerical simulations of flows of large density difference
fluids. When a horizontal channel is divided into two parts by a vertical
splitter plate and each chamber is filled with a fluid of different density,
an intruding, gravity-driven flow occurs when the splitter removed. It
consists in the spread of a dense current of the heavier fluid under the
lighter fluid and a lighter fluid current above the heavier fluid, as
is shown in Fig. 6. This referred to as lock-exchange
flow. In the experiments by Gröbelbauer et al. (1993), gases
with a density ratio of up to 21.6 were released in an unevenly divided
horizontal channel of half-height H = 0.15 m, as shown in Fig.
6. The lock gate could be placed at a distance 20 H from the right
or left wall and 10 H from the other one. The passage time of either the
light or dense front was measured at fixed positions on the horizontal
walls of the larger chamber and the Froude number of each front for the
various gas pairs was calculated.
Table 1 shows the pairs of used gases and the range
of conducted numerical simulations.
||Lock-exchange flow, experimental setup used by Gröbelbauer
et al. (1993) H = 0.15 m
The dynamic viscosity μ of these
gases lies between 12.57x10-6 Pa sec (freon 22), 18.64x10-6 Pa sec (helium) and 21x10-6 Pa sec (argon), while the kinematic
viscosity (v) ranges from 3.43x10-6 m2 sec-1 (freon 22) to 1.10x10-4 m2 sec-1 (helium). Thus, it is natural
to keep the dynamic viscosity constant in the attempt to reproduce these
experiments by numerical simulation. This might be different for liquids.
At the interface between the dense and light fluids, shear-layer instabilities
develop which give rise to Kelvin-Helmholtz billows. The smoke visualization
by Gröbelbauer et al. (1993), indicates some instability
on the interface especially close to the dense front when the density
ratio is large. As long as this instability remains two dimensional, its
essential features are accurately captured by the direct numerical simulations.
In order to validate the numerical simulations presented in this study,
the conditions of the experiments Gröbelbauer et al. (1993) were reproduced as closely as possible. The parameters of four of
these experiments reproduced by numerical simulation are shown in Table
1. The characteristic Reynolds number of these flows was calculated
with the viscosity μ and density of the lighter gas, Re = ρlUh/μl.
By using the SPH method the kinematic viscosity is calculated from this
formula (Monaghan, 2005).
The coefficients for dense and light particles, cmd and cml
are also shown in Table 1). An adaptive kernel function
which its smoothing length varies according to the changing of density
is used here for increasing automatic and unconstrained resolution in
intrusion fronts, which have steep density and velocity gradients. It
is assumed that for all amounts of β, the three-dimensional effects
are negligible, so it is possible to use the two-dimensional numerical
model for decreasing computational costs.
In Fig. 7 the numerical results of our SPH model are
compared with the experimental results of Gröbelbauer et al.
(1993) and with numerical results of Étienne et al.
For the light front the agreement between the arrivals of the simulated and
the experimental fronts is as well as the results of Étienne et al.
||Values of the density parameter ρ* and Reynolds
numbers in the experiments of Gröbelbauer et al. (1993)
and in the numerical simulations presented here
|vl = μl/ ρl,
Re = ρlUh/μl, ,
Comparison of the numerical and experimental results, (a) light
front and (b) dense front. For lock-exchange, in all cases, Exp: Experimental
results of Gröbelbauer et al. (1993), FE: Finite Element
model of Étienne et al. (2005), SPH: Current SPH model,
ρd = 1000 kg m-3 and α = ( ρd ρl)/ ρd
Comparison of the numerical and experimental results for Froude
numbers, (a) Light front and (b) dense front, ρd=
1000 kg m-3 and α = ( ρd ρl)/ ρd
For the dense front, a nearly constant shift in time between the calculated
and measured front arrivals is observed (Fig. 7b) similar
to Étienne et al. (2005) numerical results. Possible explanations for
this time shift according to reasoning of Étienne et al. (2005) are
either a large numerical inconsistency in time around t = 0 or a time lag in
the measurements. Thus, we are left to suppose that there is either a uniform
time lag in the measured arrival time of the front, which may be due to a detection
problem, or that the time shift is due to the opening of the gate.
In Fig. 8a and b, the experimental
and numerical Froude numbers
of light and dense fronts for different density ratios are compared. Since
the Froude number only accounts for the established velocity of the front,
the shift in front arrival between numerical and experimental results
has no effect.
It is shown from Fig. 8a that, for the light front,
the SPH model is in close agreement with the experiments of Gröbelbauer
et al. (1993) and also with numerical results of Étienne
et al. (2005), numerical and experimental results concerning Frl
deviate from the straight line
. Figure 8b shows that for the dense front, the SPH
model as well as the model of Étienne et al. (2005), for
Reynolds numbers corresponding to the experiments of Gröbelbauer
et al. (1993), fit closely the experiments. The data points
can be closely fitted by a power law of the form .
In Fig. 9 the shape of lock-exchange flow and nondimensional
vorticity for β = 0.11, at two stages in the flow development is
shown. The vorticity of each particle is defined as (Monaghan, 1992):
The general shape of vorticity is approximately similar but is not in
excellent agreement. This may be improved by doing some correction on
vorticity formula in SPH and by increasing the resolution of SPH model.
||Comparison of vorticity at the interface of two fluids,
top to bottom, vorticity from Étienne et al. (2005),
vorticity from SPH and the density profile, respectively
A multi-phase free surface flow implementation of Smoothed-Particle Hydrodynamics
(SPH) is presented and a new correction on standard SPH is provided. In
this model without defining the interface between two phases or using
complicated correction for standard SPH method and just by changing the
mass of particles which have interaction with particles from other phases
according to derived graphs from bobble rising in a heavier fluid and
an engineering judgment, it is possible to model the violent two-phase
free surface fluids with density ratios ( ρd/ ρl)
up to 1000. Numerical examples are investigated and compared with analytic
solutions, previous results and experiments. The results suggest that
this method can be faithfully applied to multi-phase flows. Since its
construction is based on the standard SPH method the involved corrections
are simple to implement and suitable for straightforward extension to
The authors gratefully acknowledge Iranian Water Resources Management
Co. support through grant ENV1-84120.