RWTH Aachen University, AICES

Goals to be achieved

The Aachen Institute for Advanced Study in Computational Engineering Science (AICES) of RWTH Aachen University develops data-driven and physics-based simulation methods necessary for a virtual ice exploration testbed. Depending on the task these models range from idealized performance and trajectory models to high-fidelity process models that includes the complex interplay between thermo-fluid-mechanical processes relevant for the melting robot. Our overall goal is to apply our tools for model-based strategic mission planning and decision support, which for instance requires to approximate key performance metrics (transit time, overall / average energy requirement) or to assess sensor data acquisition strategies. Performance and dynamics of a thermal melting robot are highly sensitive to the ambient cryo-environment. We therefore complement our computational models with a task-driven, functional ice data compilation referred to as the ‘Ice Data Hub’.

Tasks within the project

  • Trajectory and performance modeling for the transit of a thermal melting probe through heterogeneous ice
  • Adaptation of the CTD-ICE concept developed within EnEx-WISE

Preliminary work

Simple macroscopic trajectory models that consider the thermodynamic melting process and the convective loss of heat via the melt-water flow have been developed previously for melting through homogeneous ice. In a parallel project (EnEx WISE, AICES@RWTH Aachen), high-fidelity process models are developed including the contact regime underneath the probe as well as complex thermo-fluiddynamical phase change processes in the melting channel.

The dynamic behavior of a thermo-electric melting probe depends on the complex interplay of various thermofluidmechanical processes like con-tact phase-change at the probe’s hot point (A), (potentially) liquid-vapor phase-change (B), liquid-solid phase-change at the channel’s lateral sides (C), heat conduction into the ice (D), and convection of melt water in the channel (E).
While design optimization tasks ideally require a high-fidelity model of the ful-ly coupled process around the probe, model-based decision requires informa-tion on overall transit time and power consumption, which can already be well approximated by efficiency / trajectory modeling (including A, B and D).

Implementation steps

  • Development of an ice data management tool (Ice Data Hub).
  • Adapting the existing and new trajectory models for the scenarios considered within TRIPLE and calculation of transit times including uncertainties.

Points of Contact

Julia Kowalski,
Marc S. Boxberg,