EnEx-DiMIce – Directional Melting in Ice

Künstlerische Interpretation einer simulierten Schmelzfahrt auf Enceladus.
Simulation tools provide decision support for future exploration missions (image credits: K. Schüller).

It is now widely accepted that the presence of liquid water on planets and moons of our Solar System bears some potential for the development of extraterrestrial life. One candidate is the Saturnian moon Enceladus [1]. At Enceladus‘ south polar terrain, ice grains that contain organic compounds are ejected into space. They originate from a subsurface ocean and have been transported through a 30-40 km thick ice layer via cracks [2, 3]. These cracks could serve as potential access points for the exploration of subglacial aquatic ecosystems at moderate depth and eventually for the search for life. In view of future missions, various innovative melting technologies for autonomous and/or maneuverable motion through the ice have been proposed [4, 5, 6].

These innovative technological concepts also require advanced simulation technologies tailored to assess, evaluate and extrapolate their dynamic range.

The motion of melting probes – a complex multi-physics problem

Simulating the motion of melting probes requires to solve a complex thermo-fluid-mechanically coupled contact problem.
Simulating the motion of melting probes requires to solve a complex thermo-fluid-mechanically coupled contact problem.

The high-level, inverse question, which needs to be answered is: Given the coordinate of a liquid reservoir, as well as information about rocks, non-communicating englacial water lenses (which should be avoided), or other obstacles in the ice – How do I have to set the melting probe’s power and forces in order to realize an efficient, yet safe melting trajectory?

Answering this question implies the capability both to determine the motion of the melting probe through the ice, and to describe the state of the ambient ice including the evolution of the melting channel. The main objective of the project DiMIce is hence to develop a computational model that meets these requirements. In a holistic model we have to consider conduction within the ice, the melt and the interior probe, convection within the liquid melt, as well as intrinsic phase-change processes. Computing the evolution of the melt channel allows us to infer on the probe’s melting trajectory. Major challenges are the tracking of the phase-interface and to correctly reflect the thermo-fluid-mechanical coupling.

Mathematical model development – the key to understanding melting under extreme conditions

Central aspects to the work of our group are the derivation and analysis of mathematical models, namely systems of partial differential equations that describe the physical processes, and to combine these with tailored numerical solution strategies into a computational model. Its integrity is analyzed by both verifying the numerical scheme and validating the underlying model, namely studying the error introduced by simplifying assumptions and uncertainties. Careful model verification and validation are necessary to guarantee a good predictive power, which is essential when wanting to apply the simulation tool to extreme conditions for which few experimental data are available. We have for instance been able to observe a good correspondence between experimental data and the predicted trajectory radius in laboratory experiments under terrestrial conditions [7]. During the project DiMIce we aim for further developing the computational model such that these results can be extrapolated to low pressure and temperature regimes that reflect extraterrestrial conditions.

Simulation based mission support implies a cascade of tailored model solutions

Within the context of an exploration mission based on intelligent melting probes, simulation models are needed for various purposes, e.g during technology design or to support ‘on-line’ and ‘off-line’ scenario planning. Each of these purposes takes a slightly different prespective which implies the need for a cascade of computational models tailored to the actual requirement. Within the project DiMIce we develop and apply simulation methodologies on different levels of complexity during collaborations with our partner projects in the EnEx Initiative:

Temperaturverteilung um einen parabolischen Schmelzkopf.
Temperature distribution around a paraboloid melting head.
  • Together with EnEx-CAUSE (Prof. K. Schill and Prof. C. Büskens, University of Bremen) we integrate our models with sensor fusion and trajectory control strategies.
  • Together with EnEx-nExT (Prof. B. Dachwald, FH Aachen University of Applied Sciences) we analyze melting in low temperature and pressure regimes both theoretically and experimentally.
  • Together with EnEx-MIE (Dr. U. Bestmann, University of Braunschweig) we integrate our models with global trajectory planning strategies.
  • Together with EnEx-RANGE (Prof. Wiebusch and Prof. Jeschke, RWTH Aachen University) we analyze and optimize the melting performance of alternative melting probe designs.

Student research projects

Interested in working with us? Check out our open student project proposals here!


The project DiMIce is hosted at the Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University. It is supported by the Federal Ministry for Economic Affairs and Energy, Germany, on the basis of a decision by the German Bundestag (FKZ: 50 NA 1502).


[1] C.D. Parkinson et al.: Enceladus: Cassini Observations and Implications for the Search for Life. Astron. Astrophys., 463(1):353-357, 2007.
[2] H.-W. Hsu et al.: Ongoing hydrothermal activities within Enceladus. Nature, 519:207-211, 2015.
[3] J. N. Spitale et al.: Curtain eruptions from Enceladus south-polar terrain. Nature, 521:57-60, 2015.
[4] W.F. Zimmerman et al.: Cryobot: an ice penetrating robotic vehicle for Mars and Europa.  Proc. of the IEEE Aerospace Conference 2001, 311-323, Big Sky, MT, 2001
[5] Dachwald B. et al.:  IceMole: a maneuverable probe for clean in situ analysis and sampling of subsurface ice and subglacial aquatic ecosystems. Ann. Glaciol., 55(65):14_22, 2014.
[6] Kowalski J. et al.: Navigation Technology for Exploration of Glacier Ice With Maneuverable Melting Probes, In: Cold Reg. Sci. Technol., 123, 53-70.
[7] Schüller K. et al.: Curvilinear Melting – A Preliminary Experimental and Numerical Study: International Journal of Heat and Mass Transfer, 92, 884-892, 2016.