Nanofiller-enhanced wax for heat storage (Wax+)

Modeling and Simulation of Novel Phase Change Material to improve Thermal Responsiveness

Organization:

Funded by:

NWO AES -  18052 Nanofiller-enhanced wax for heat storage (Wax+)

PhD:


Supervisor:


Collaboration:

TU Eindhoven:

  • Maarten Boomstra
  • Dr. Alexey Lyulin MD
  • Lisette Wijkhuijs
  • Dr. Heiner Friedrich 
  • Henk Huininik 

Description:

The objective of the research is to understand, model, simulate and optimize graphene-based, heat conducting nano-scale networks embedded in bulk paraffin wax. This is aimed at a significant speed up of storage and retrieval of large amounts of thermal energy from solid-liquid phase changes in the macroscopic scale. For the modelling multiscale computational methods will be developed ranging from Molecular Dynamics (MD) to direct numerical simulation (DNS) of macroscopic PDE models. This research is part of the WAX+ project - its main objective is to develop a novel phase change material (PCM) in collaboration with the Eindhoven University of Technology (TU/e) consisting of Maarten Boomstra and Dr. Alexey Lyulin MD study and Lisette Wijhuijs and Dr. Heiner Friedrich for Chemical synthesis. 

The heat transfer characteristics of graphene networks, expressed as local heat conductivity distributions along network components as predicted by MD, will be included in system-scale DNS modelling. Physical experiments of the effective heat transfer in slab geometries will provide a crucial validation reference. Heat storage associated with phase change and charging/discharging cycles of WAX+ will be studied as function of various system parameters, such as the filler volume fraction, the topology of the heat-conducting network, the effects of multiple cycling on the material integrity of WAX+. The system’s increased thermal responsiveness during processes of melting and solidification will be established. Experimental investigations of the microstructure in connection to heat conductivity, melting and solidification will be used to validate the computational models.

Output

Publications
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