Direct numerical simulations of cooling flow and heat transfer in supercritical CO2 Brayton cycle coupled with solar energy

Wu, Dingchen; Wei, Mingshan; He, J.; He, Suqin; Tian, Ran

doi:10.1016/j.applthermaleng.2023.120790

Cited by 4 publications

(1 citation statement)

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“…Wu et al [24] conducted an exploration into the intricate mechanisms governing ow and heat transfer in supercritical carbon dioxide under various conditions. Their direct numerical simulation revealed the profound impact of property uctuations, buoyancy forces, and thermal deceleration on ow and heat transfer behavior.…”

Section: Introductionmentioning

confidence: 99%

CFD Validation of Wall Laws in K-ω SST Model for Adverse Pressure Gradient-Free Downward Flow

Belhadad,

Kanna,

El Rhafiki

et al. 2023

E3S Web of Conf.

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This study introduces a validation investigation employing Computational Fluid Dynamics (CFD) to assess the utilization of the Consistent Wall Law (CWL) in tandem with the K-Shear Stress Transport (SST) turbulence model, specifically in scenarios characterized by downward flow without adverse pressure gradients. The precise prediction of turbulent flows near walls is of paramount significance in the realm of engineering applications. The CWL is meticulously designed to enhance the precision of near-wall turbulence modeling by ensuring a harmonious alignment between wall functions and the flow field. To gauge the performance of CWL in the challenging context of downward flows marked by adverse pressure gradients, we conducted numerical simulations using a commercial CFD solver. The results reveal a substantial improvement in agreement with experimental data, particularly in critical near-wall regions. CWL effectively showcases its prowess in managing adverse pressure gradients and offering precise predictions for wall-related parameters. Moreover, we executed a sensitivity analysis to explore the durability of CWL across various parameter variations, confirming its effectiveness and trustworthiness. This research significantly contributes to a more profound comprehension of turbulence modeling, thereby facilitating the generation of more accurate predictions in intricate flow scenarios that are pertinent to engineering applications.

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Section: Introductionmentioning

confidence: 99%