In recent years, a considerable amount of research has been directed towards making energy generation more efficient to combat global warming. To aid this goal, the use of supercritical fluids (SCFs) is gaining a lot of traction. SCFs have only one phase and experience a sharp variation of thermophysical properties when heated sufficiently. These facts can be exploited to gain several advantages over conventionally used sub-critical fluids. However, the strong property variation in heated SCFs also complicates the flow Physics considerably if the wall heating is strong enough. Further, the flows in such settings are often turbulent and spatially developing. For these flows, a strong variation of properties can lead to a modulation of turbulence, which the conventional turbulence models can not predict well. This thesis is an attempt to better understand how spatially developing heated supercritical turbulent flows behave and use this understanding to improve turbulence model predictions.

In this thesis, we investigate two supercritical heated developing turbulent flows from Nemati et al. (2015). One case is oriented horizontally and the other is oriented vertically. The latter has an additional effect of buoyancy resulting from its vertical orientation and strong density variation near the wall. We analyze how the strong property variation modulates the turbulence in both cases. Then, we assess if Semi-Local Scaling (SLS) and Apparent Reynolds Number (ARN) theories can characterize the modulated turbulence. Further, we propose a methodology to make use of ARN by itself, and in combination with SLS to improve turbulence model predictions.

The results indicate that ARN theory provides a robust way to sensitize conventional turbulence models to the additional effects arising in supercritical heated developing turbulent flows due to property variation. Additional research in turbulent heat flux modeling is deemed necessary to improve the model performance further.