Numerical investigations on transitional flows around forward and reversed hydrofoils

Shi, Lei; Wang, Yefang

doi:10.1016/j.euromechflu.2020.08.008

Cited by 4 publications

(1 citation statement)

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“…However, note that the transition SST model is not proposed to simulate the physics of the transition process but is essentially an experimental correlation-based model. It was found to be strongly dependent on the selected correlation for the critical Reynolds number (Richez et al, 2016), with limitations in predicting massive separation flows (Shi & Wang, 2021).…”

Section: Low R E Validationmentioning

confidence: 99%

Submerged floating tunnel cross-section analysis using a transition turbulence model

Zou

Bricker

Uijttewaal

2021

Journal of Hydraulic Research

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Concepts of a submerged floating tunnel (SFT) for novel sea-crossings have been researched in recent years. An SFT tube should be moored afloat by tensioned mooring systems to maintain the tube position under complex hydrodynamic loads. In-line force is amongst the dominant hydrodynamic parameters in the SFT cross-section design and the mooring system reliability evaluation. Selecting a suitable in-line force computation method is crucial to successful and accurate SFT cross-section optimization. The transition SST model is an effective turbulence transition prediction tool in the boundary layer computation subjected to tidal flow at both low and high Reynolds numbers. Two types of parametric Bézier curves applied in airfoil optimization are used to describe the SFT cross-section. We show that an SFT cross-section described by a leading-edge Bézier-PARSEC (BP) curve has better hydrodynamic performance than a trailing-edge BP curve of equal aspect ratio (AR). To avoid large flow separation, an AR not exceeding 0.47 is recommended. An SFT cross-section design should balance hydrodynamic performance and construction cost. The SFT cross-section with AR = 0.47 using the leading-edge BP curve with fixed clearance has a comparatively small in-line force and a minimum material cost.

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Section: Low R E Validationmentioning

confidence: 99%