Abstract
In propulsion and power systems, operating pressures are increasing to improve cycle efficiency, often reaching conditions that approach or exceed the thermodynamic critical pressure of the working fluid. A known phenomenon that occurs at supercritical pressures is supercritical heat transfer deterioration (HTD). Although this phenomenon is well studied, there is still debate about the underlying physics. This paper studies pseudo boiling, the supercritical liquid–gas phase transition, as a possible mechanism that causes HTD, by means of computational fluid dynamics (CFD), boundary layer correlations, and thermodynamic analysis. To this end, we focus on forced laminar convection of supercritical CO2 over a flat plate, excluding alternative factors such as buoyancy or turbulence. In order to accurately represent the strongly non-linear near-critical fluid properties, we use tiny neural networks (TNN) in an interpretable machine learning approach. For use in CFD, TNN combine reference quality accuracy from look-up tables with the memory footprint of equations of state and curve fits. We demonstrate that pseudo boiling theory accurately indicates pressure and temperature regions susceptible to HTD. The simulations show the distinctive boiling curve, with a peak critical heatflux and a Leidenfrost minimum, demonstrating that pseudo boiling is, indeed, a sufficient physical mechanism to cause this effect, in absence of turbulence or boyancy. During HTD, we observe the formation of a supercritical gas film at the wall, with low density, low thermal conductivity, a high compressibility factor, and low viscosity. The boundary of the gaseous film is characterized by a peak in heat capacity, indicative of pseudo boiling, and a sudden transition to a dense supercritical liquid state. This suggests that the subcritical boiling crisis does have a direct representation at supercritical pressures through pseudo boiling, and that pseudo film boiling is a sufficient mechanism to cause HTD.
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