# The van Keken thermochemical composition benchmark#

This section is a co-production of Cedric Thieulot, Juliane Dannberg, Timo Heister and Wolfgang Bangerth with an extension to this benchmark provided by the Virginia Tech Department of Geosciences class “Geodynamics and ASPECT” co-taught by Scott King and D. Sarah Stamps.

One of the most widely used benchmarks for mantle convection codes is the isoviscous Rayleigh-Taylor case (“case 1a”) published by van Keken et al. in van Keken et al. [1997]. The benchmark considers a 2d situation where a lighter fluid underlies a heavier one with a non-horizontal interface between the two of them. This unstable layering causes the lighter fluid to start rising at the point where the interface is highest. Fig. 147 - Fig. 150 shows a time series of images to illustrate this.

Although van Keken’s paper title suggests that the paper is really about thermochemical convection, the part we examine here can equally be considered as thermal or chemical convection; all that is necessary is that we describe the fluid’s density. We can do that by using an inhomogeneous initial temperature field, or an inhomogeneous initial composition field. We will use the input file in cookbooks/van-keken/van-keken-discontinuous.prm as input, the central piece of which is as follows (go to the actual input file to see the remainder of the input parameters):

subsection Material model
set Model name = simple

subsection Simple model
set Viscosity                     = 1e2
set Thermal expansion coefficient = 0
set Density differential for compositional field 1 = -10
end
end

subsection Initial composition model
set Model name = function

subsection Function
set Variable names      = x,z
set Function constants  = pi=3.14159
set Function expression = if( (z>0.2+0.02*cos(pi*x/0.9142)) , 0 , 1 )
end
end

The first part of this selects the simple material model and sets the thermal expansion to zero (resulting in a density that does not depend on the temperature, making the temperature a passively advected field) and instead makes the density depend on the first compositional field. The second section prescribes that the first compositional field’s initial conditions are 0 above a line describes by a cosine and 1 below it. Because the dependence of the density on the compositional field is negative, this means that a lighter fluid underlies a heavier one.

The dynamics of the resulting flow have already been shown in Fig. 147 - Fig. 150. The measure commonly considered in papers comparing different methods is the root mean square of the velocity, which we can get using the following block in the input file (the actual input file also enables other postprocessors):

subsection Postprocess
set List of postprocessors = velocity statistics
end

Using this, we can plot the evolution of the fluid’s average velocity over time, as shown in Fig. 151. Looking at this graph, we find that both the timing and the height of the first peak is already well converged on a simple $$32\times 32$$ mesh (5 global refinements) and is very consistent (to better than 1% accuracy) with the results in the van Keken paper.

That said, it is startling that the second peak does not appear to converge despite the fact that the various codes compared in van Keken et al. [1997] show good agreement in this comparison. Tracking down the cause for this proved to be a lesson in benchmark design; in hindsight, it may also explain why van Keken et al. stated presciently in their abstract that “…good agreement is found for the initial rise of the unstable lower layer; however, the timing and location of the later smaller-scale instabilities may differ between methods.” To understand what is happening here, note that the first peak in these plots corresponds to the plume that rises along the left edge of the domain and whose evolution is primarily determined by the large-scale shape of the initial interface (i.e., the cosine used to describe the initial conditions in the input file). This is a first order deterministic effect, and is obviously resolved already on the coarsest mesh shown used. The second peak corresponds to the plume that rises along the right edge, and its origin along the interface is much harder to trace – its position and the timing when it starts to rise is certainly not obvious from the initial location of the interface. Now recall that we are using a finite element field using continuous shape functions for the composition that determines the density differences that drive the flow. But this interface is neither aligned with the mesh, nor can a discontinuous function be represented by continuous shape functions to begin with. In other words, we may input the initial conditions as a discontinuous functions of zero and one in the parameter file, but the initial conditions used in the program are in fact different: they are the interpolated values of this discontinuous function on a finite element mesh. This is shown in Fig. 153. It is obvious that these initial conditions agree on the large scale (the determinant of the first plume), but not in the steps that may (and do, in fact) determine when and where the second plume will rise. The evolution of the resulting compositional field is shown in Fig. 154 and it is obvious that the second, smaller plume starts to rise from a completely different location – no wonder the second peak in the root mean square velocity plot is in a different location and with different height!

The conclusion one can draw from this is that if the outcome of a computational experiment depends so critically on very small details like the steps of an initial condition, then it’s probably not a particularly good measure to look at in a benchmark. That said, the benchmark is what it is, and so we should try to come up with ways to look at the benchmark in a way that allows us to reproduce what van Keken et al. had agreed upon. To this end, note that the codes compared in that paper use all sorts of different methods, and one can certainly agree on the fact that these methods are not identical on small length scales. One approach to make the setup more mesh-independent is to replace the original discontinuous initial condition with a smoothed out version; of course, we can still not represent it exactly on any given mesh, but we can at least get closer to it than for discontinuous variables. Consequently, let us use the following initial conditions instead (see also the file cookbooks/van-keken/van-keken-smooth.prm):

subsection Initial composition model
set Model name = function

subsection Function
set Variable names      = x,z
set Function constants  = pi=3.14159
set Function expression = 0.5*(1+tanh((0.2+0.02*cos(pi*x/0.9142)-z)/0.02))
end
end

This replaces the discontinuous initial conditions with a smoothed out version with a half width of around 0.01. Using this, the root mean square plot now looks as shown in Fig. 152. Here, the second peak also converges quickly, as hoped for.

The exact location and height of the two peaks is in good agreement with those given in the paper by van Keken et al., but not exactly where desired (the error is within a couple of per cent for the first peak, and probably better for the second, for both the timing and height of the peaks). This has to do with the fact that they depend on the exact size of the smoothing parameter (the division by 0.02 in the formula for the smoothed initial condition). However, for more exact results, one can choose this half width parameter proportional to the mesh size and thereby get more accurate results. The point of the section was to demonstrate the reason for the lack of convergence.

## Extension to an adaptive smoothing lengthscale.#

In this section we extend the van Keken cookbook, following up on the work previously completed by Cedric Thieulot, Juliane Dannberg, Timo Heister and Wolfgang Bangerth. This section was contributed by Grant Euen, Tahiry Rajaonarison, and Shangxin Liu as part of the Geodynamics and ASPECT class at Virginia Tech.

As already mentioned above, using a half width parameter proportional to the mesh size allows for more accurate results. We test the effect of the half width size of the smoothed discontinuity by changing the smoothing parameter to values proportional to the mesh size. In the formula for the smoothed initial conditions, this parameter is the division by 0.02. We use 7 global refinements because the root mean square velocity converges at greater resolution while keeping average runtime around 5 to 25 minutes. These runtimes were produced by the BlueRidge cluster of the Advanced Research Computing (ARC) program at Virginia Tech. BlueRidge is a 408-node Cray CS-300 cluster; each node outfitted with two octa-core Intel Sandy Bridge CPUs and 64 GB of memory. Average runtimes for global refinements 5 through 10 using one node can be seen in Table 6. For 7 global refinements (128$$\times$$ 128 mesh size), the size of the mesh is 0.0078 corresponding to a half width parameter of 0.0039. The smooth model allows for much better convergence of the secondary plumes, although they are still more scattered than the primary plumes.

Table 6 Average runtimes for the van Keken Benchmark with smoothed initial conditions. These times are for the entire computation, a final time step number of 2000. All of these tests were run using ASPECT version 1.3 in release mode, and used different numbers of processors on one node on the BlueRidge cluster of ARC at Virginia Tech.#

Global

Number of Processors

Refinements

4

8

12

16

5

28.1 seconds

19.8 seconds

19.6 seconds

17.1 seconds

6

3.07 minutes

1.95 minutes

1.49 minutes

1.21 minutes

7

23.33 minutes

13.92 minutes

9.87 minutes

7.33 minutes

8

3.08 hours

1.83 hours

1.30 hours

56.33 minutes

9

1.03 days

15.39 hours

10.44 hours

7.53 hours

10

More than 6 days

More than 6 days

3.39 days

2.56 days

This convergence is due to changing the smoothing parameter, which controls how much of the problem is smoothed over. As the parameter is increased the smoothed boundary grows; as the smoothed boundary shrinks it becomes sharper until the original discontinuous behavior is revealed. As the boundary grows, the two distinct layers eventually become one large, transitioning layer. These effects can be seen in Fig. 155. The overall effect is that the secondary rise is at different times based on these conditions. In general, as the smoothing parameter is decreased the smoothed boundary shrinks, and the plumes rise more quickly. As it is increased the boundary grows, and the plumes rise more slowly. This trend can be used to force a more accurate convergence from the secondary plumes.

The evolution in time of the resulting compositional fields (Fig. 156) shows that the first peak converges as the smoothed interface decreases. There is a good agreement for the first peak for all smoothing parameters. As the width of the discontinuity increases, the second peak rises more slowly and later in the run.

Now let us further add a two-layer viscosity model to the domain. This is done to recreate the two nonisoviscous Rayleigh-Taylor instability cases (“cases 1b and 1c”) published in van Keken et al. in van Keken et al. [1997]. Let’s assume the viscosity value of the upper, heavier layer is $$\eta_{t}$$ and the viscosity value of the lower, lighter layer is $$\eta_{b}$$. Based on the initial constant viscosity value 1$$\times10^{2}$$ Pa s, we set the viscosity proportion $$\frac{\eta_{t}}{\eta_{b}}=0.1, 0.01$$, meaning the viscosity of the upper, heavier layer is still 1$$\times10^{2}$$ Pa s, but the viscosity of the lower, lighter layer is now either 10 or 1 Pa s, respectively. The viscosity profiles of the discontinuous and smooth models are shown in Fig. 157.

For both cases, discontinuous and smooth, and both viscosity proportions, 0.1 and 0.01, the results are shown at the end time step number, $$t=2000$$, in Fig. 158. This was generated using the original input parameter file, running the cases with 8 global refinements, and also adding the two-layer viscosity model.

Compared to the results of the models using constant viscosity throughout the domain, the plumes rise faster when adding the two-layer viscosity. Also, the larger the viscosity difference is the earlier the plumes appear, and the faster their ascent. To further reveal the effect of the two-layer viscosity model, we also plot the evolution of the fluids’ root mean square velocity over time, as shown in Fig. 159.

We can observe that when the two-layer viscosity model is added, there is only one apparent peak for each case. The first peaks of the 0.01 viscosity contrast tests appear earlier and are larger in magnitude than those of 0.1 viscosity contrast tests. There are no secondary plumes and the whole system tends to reach stability after around 500 time steps.