Collective effects of neighbouring melting ice objects

TL;DR

This study investigates how the relative positioning of neighbouring melting ice objects affects their melting dynamics through simulations, revealing non-trivial interactions influenced by fluid layering and convection.

Contribution

It provides new insights into the collective melting behavior of ice objects, highlighting the impact of alignment and initial spacing on melting times and morphology.

Findings

Vertical alignment affects melting time and shape significantly.

Melting can be enhanced or delayed by over 10% or 20%.

Data collapse onto a single curve for mixed convection conditions.

Abstract

We present a study on the melting dynamics of neighbouring ice bodies by means of idealised simulations, focusing on collective effects, with the goal of obtaining fundamental insight into how collective interactions influence the melting of ice. Two neighbouring (vertically or horizontally aligned), square-shaped, and equally sized ice objects (size on the order of centimetres) are immersed in quiescent fresh water at a temperature of 20{\deg}C. By performing two-dimensional direct numerical simulations, and using the phase-field method to model the phase change, the collective melting of these objects is studied. When the objects are horizontally aligned, no significant influence of the neighbouring object on the melting time is observed. On the other hand, when vertically aligned, though the melting of the upper object is mostly unaffected, the melting time and the morphology of the lower ice body strongly depends on the initial inter-object distance. We report that the melting of the bottom object can be enhanced by more than 10%, or delayed more than 20%, displaying a non-monotonic dependence on the initial object size. We show that this behaviour results from a non-trivial competition between layering of cold fluid, which lowers the heat transfer, and convective flows, which favour mixing and heat transfer. For this melting in mixed convection, we were able to collapse our data onto a single curve.