Periodic Reporting for period 1 - MELTDYN (Understanding the melting dynamics in turbulent flows)
Período documentado: 2022-05-01 hasta 2024-10-31
One of the major challenges in modelling climate change is to accurately predict the melting rate of icebergs and glacier, as this is connected to the albedo, the ocean currents, and ocean level. Current predictions for the melting of glaciers can be off by a factor of 100, and different melting models show inconsistencies—no general consensus of the cryospheric modeling has been reached yet.
The difficulties in describing the entire melting process stems from its multiscale nature (micrometers to kilometers) and the interaction between thermal, solutal, and viscous boundary layers and their complex interplay with the continuously reshaping (due to melting) boundary. To add, it is commonly believed that melting always smooths the shape. However, from examples in nature and from theoretical analysis, it is clear that flows around melting objects can create a rough (dimpled or 'scalloped') surface, dramatically increasing the difficulty of accurate predictions due to the complicated shape-flow interplay.
The complexity of such a system is immense. The heat-flux (describing the melting rate) depends on many factors and fully simulating the entire process using computers is not possible for the foreseeable future. The best one can do is to either study select naturally-occurring instances of icebergs and glaciers by means of field measurements—with its concomitants imperfections and uncontrollabilities—or to do the largest-attainable well-controlled experiments in the laboratory and to use the latest direct numerical simulations, to understand the fundamental underlying mechanisms, and from that predict to larger scales. This bottom-up approach has been highly successful in pipe and wind-tunnel flows, thermal convection, and rotating turbulence, and we believe this approach will also be successful here, and will allow us to predict transitions and scaling so desperately needed to predict these types of processes.
The aim of this research is therefore to investigate the effect of roughness on the melting process. And we investigate this using well-controlled laboratory experiments complemented by direct numerical computer simulations of these problems.
The difficulties in describing the entire melting process stems from its multiscale nature (micrometers to kilometers) and the interaction between thermal, solutal, and viscous boundary layers and their complex interplay with the continuously reshaping (due to melting) boundary. To add, it is commonly believed that melting always smooths the shape. However, from examples in nature and from theoretical analysis, it is clear that flows around melting objects can create a rough (dimpled or 'scalloped') surface, dramatically increasing the difficulty of accurate predictions due to the complicated shape-flow interplay.
The complexity of such a system is immense. The heat-flux (describing the melting rate) depends on many factors and fully simulating the entire process using computers is not possible for the foreseeable future. The best one can do is to either study select naturally-occurring instances of icebergs and glaciers by means of field measurements—with its concomitants imperfections and uncontrollabilities—or to do the largest-attainable well-controlled experiments in the laboratory and to use the latest direct numerical simulations, to understand the fundamental underlying mechanisms, and from that predict to larger scales. This bottom-up approach has been highly successful in pipe and wind-tunnel flows, thermal convection, and rotating turbulence, and we believe this approach will also be successful here, and will allow us to predict transitions and scaling so desperately needed to predict these types of processes.
The aim of this research is therefore to investigate the effect of roughness on the melting process. And we investigate this using well-controlled laboratory experiments complemented by direct numerical computer simulations of these problems.
We have been successfully able to 1. measure the morphology of a melting ice block melting in fresh, but also in a saline environment using time-resolved 3D profilometry. Revealing intricate structure not yet measured before. 2. We have set up planar induced laser-induced fluorescence and were able to measure temperature fields around melting objects. We will now further improve this technique with multiple colors to further improve the accuracy—specifically to allow for varying dye concentration inside the (diluted) melt water. 3. We have modified the AFiD code and the geometry of a flat plate over which a pressure-driven flow occurs has been implemented. We also observe that the surface becomes non-flat, and we see pattern formation. Further increase in the driving should allow for the observation of so-called scalloping. 4. Using the same AFiD we also got interested in collective effects of melting. The proximity of other melting objects can advance or hinder the melting of neighbouring objects. And we obtained surprising, unexpected results that is likely way more important than we had anticipated. 5. The water tunnel has been modified to allow for frozen ice objects to be inserted into the flowing water to melt under the influence of a turbulent flow. We will soon combine these with morphology measurements and laser-induces fluorescence measurements to extract the details of the melting dynamics. 6. The homogeneous-isotropic turbulence facility (the dodecahedron facility) has been modified to allow the insertion of frozen objects while in operation. The first measurement reveal a constant melting rate, independent of the size of the remaining ice, as the turbulence is so strong that it fully mixes the melt water around the object.
We hope to further investigate the melting process, and more specifically, characterize the local melting rate (in terms of local Nusselt numbers) as a function of the surface morphology, the salinity, and the effect of neighbouring objects. We can then compare this with the melt rate for the case that the surface is flat and see how much faster or slower the objects melt. When we get these data this can then be used by climate modelers (for glaciers and icebergs) to better predict their melt rates.