Tag Archives: simulation

Granular simulations using LIGGGHTS

As promised in my last blog entry, having talked a little about why I am interested in simulating granular material (i.e. materials made up of distinct particles), I want to talk a little now about some of the tools I’m using. Of course all are free and open source – so you can download them and give it a try yourself!

Granular materials are oddities – sometimes they can behave like solids, and form stable structures, and sometimes they behave like liquids, and flow and pour. And sometimes they transition readily between the two! Because of this wide ranging behaviour, we don’t (yet!) have a nice set of equations to describe their bulk behaviour (as we for do, say, for gases). Instead, it is possible to build numerical models of granular systems by modelling the behaviour of individual particles, their interactions with other particles, walls, etc. This may sound complex, but much of the complexity is in how to computationally deal with tracking enough particles to be useful, not in the underlying physics. So if we can correctly describe the interactions of a pair of particles, we should be able to describe a system of billions of such particles – providing we have enough time and computing power!

LIGGGHTS logoThis technique is called the Discrete Element Method (DEM) and is an extension of  molecular dynamics to deal with larger particles which have a finite size and  a rotational degree of freedom. There are several open source codes available which you might like to look into – I have played with three: ESyS-Particle, YADE and LIGGGHTS. Each has its own advantages, and in fact I ended up using bits of each (a geometry building module from ESyS called LSMGenGeo, some YADE scripts to build ballistic aggregates, and LIGGGHTS for my “grunt work”). In this post I’ll focus mainly on LIGGGHTS, since it is the engine at the heart of most of the calculations I’m working on right now.

LIGGGHTS is a fork of the popular molecular dynamics code LAMMPS with enhancements to better deal with the macroscopic particles used in granular mechanics  simulations. As such, the computational complexity of stably integrating the equations of motions for millions of particles, and figuring out which particles are interacting, is already well-validated by the many LAMMPS users. The enhancements made by the LIGGGHTS team focus on the contact models (the physics of two particles interacting), linking the DEM model to a fluid dynamics code (OpenFOAM), allowing importing of CAD meshes for greater flexibility, and a host of utilities to enable generating of complex particle packings, support for non-spherical particles and so on.  You can check out a recent presentation by the LIGGGHTS team [PDF] for more details!

My aim is ultimately to come up with a validated model of a cometary surface which accounts for low gravity, the various inter-particle forces, and the surface environment. But before one can run, one has to learn to walk – hence I’ve been playing with LIGGGHTS and trying to make a set of simulations that demonstrate the main features I want to include in my model. So for the the next few posts, I’m going to link a few YouTube videos showing output from LIGGGHTS and talk a little about them. If you want a sneak preview, you can jump to the YouTube playlist of these videos!

Cometary nuclei and granular material

One of my current research interests is in low gravity regoliths, and in particular the dynamics of ice and dust particles in the upper layers of a cometary nucleus. One of the main reasons for this is preparation for the Rosetta spacecraft’s arrival at comet 67P / Churyumov–Gerasimenko (for a summary, see the video I posted about previously). We have a fair bit of evidence now that cometary nucleii are covered with granular material – most probably volatile-depleted dust particles that do not get lifted from the surface by the escape of sublimating ices. Various landforms have been imaged by spacecraft that could be formed by flow or erosional processes that also imply a granular surface. But to fully understand such features, we need to better understand how granular material behaves under comet-like conditions.

The first port of call in trying to answer such questions is usually the lab – for example in our comet simulation lab at the Space Research Institute we have a vacuum chamber into which we can put various ice and dust mixtures, cool them down with either liquid nitrogen or a closed loop cooler, and switch on the pumps to remove the air. By shining a simulated Sun on the surface, and monitoring temperatures and pressures, it is possible to simulate some of the suspected surface processes taking place on a comet. Such experiments are vital to understand questions such as how gas and heat flow through a porous medium under vacuum. However, they do not capture the dynamics of a real cometary surface, where the low gravity plays an important role.

The Philae Lander
The Philae Lander

Just how important is it? Well, first consider that the surface gravity on 67P is something like 30,000 times less than on Earth. This means that the Philae lander, which has a mass of 96 kg on the Earth, will weigh only a few grams on the comet – hence it has screws built into the feet and 2 harpoons to secure itself, and even these operate only when a “holddown” thruster is firing to give some extra force. The same calculation can be applied to the weight of an individual dust particle at the surface. To see what happens to such a particle, not only weight, but other forces need to be considered – for example adhesion (“sticky”) forces, or the force of escaping gasses trying to drag the particle away from the surface. Each of these forces scale differently with the particle size. Under Earth gravity, for example, we only notice the adhesion forces when we are dealing with very small particles; since weight decreases more rapidly than adhesion as we move to smaller particles, at some point it dominates. This explains why flour acts differently from dry sand when you try to pour it.

Ground flour is mostly micron sized (a millionth of a metre), whereas sand can have grains up to a millimetre in size. Because individual flour grains are so small, adhesive forces make them cling to each other, and any container they’re in. This means that they don’t flow well and are called cohesive. Coarse dry sand, on the other hand, typically flows very readily – in this case the particles are heavier and their own weight and momentum governs their motion. Now compare the situation on Earth to that on a comet – a sand grain would experience a similar adhesive force as on Earth (there are differences due to temperature and surface cleanliness, but we’ll leave that for another post!), but it would weight 30,000 times less! So even larger particles on a comet might be expected to behave like tiny particles on the Earth. In fact from such calculations alone one can expect that even centimetre sized particles could behave cohesively under certain conditions – very different from our every day experience!

The ZARM drop tower in Bremen creates ~9.5 seconds of microgravity

However, understanding how a few particles behave is very different from understanding the complexity created when millions of such particles interact. Experiments under low gravity are certainly possible – using drop towers (think of a tall tower, pumped free of air, with your experiment dropped from the top – see the schematic above!), parabolic flights (the so-called “vomit comet”), sounding rockets, or of course experiments on orbit. But these are either of limited duration (e.g. until your payload hits the bucket of polystyrene beads at the bottom of the drop tower!), or very expensive (e.g. flying onboard the International Space Station). Instead one can use computer simulations. Modern computers and clusters of computers can simulate the collective behaviour of millions, if not billions of particles. Making sure that the physics holds still requires experiments to validate the models, but it’s often a lot quicker and cheaper than running hundreds of experiments, and it allows access to regimes that are hard to simulate on Earth!

So that’s a little about the “why?” of running such simulations – in my next post I’ll show the software I’ve been using and explain a bit of the “how?”.