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Ken Ichi Abe

Researcher at Kyushu University

Publications -  85
Citations -  2494

Ken Ichi Abe is an academic researcher from Kyushu University. The author has contributed to research in topics: Turbulence & Large eddy simulation. The author has an hindex of 18, co-authored 82 publications receiving 2236 citations. Previous affiliations of Ken Ichi Abe include Toyota.

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A new turbulence model for predicting fluid flow and heat transfer in separating and reattaching flows—I. Flow field calculations

TL;DR: In this paper, the Kolmogorov velocity scale was introduced to account for the near-wall and low-Reynolds-number effects in both attached and detached flows.
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Development of a shrouded wind turbine with a flanged diffuser

TL;DR: In this paper, a wind turbine system consisting of a diffuser shroud with a broad-ring flange at the exit periphery and a turbine inside it was developed, and demonstrated power augmentation for a given turbine diameter and wind speed by a factor of about 4-5 compared to a standard (bare) wind turbine.
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Experimental and numerical investigations of flow fields behind a small wind turbine with a flanged diffuser

TL;DR: In this paper, experimental and numerical investigations were carried out for flow fields of a small wind turbine with a flanged diffuser, which gave a power coefficient higher than the Betz limit ( = 16 / 27 ) owing to the effect of the diffuser.
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An investigation of flow fields around flanged diffusers using CFD

TL;DR: In this article, an advanced closure approximation is adopted, within the framework of non-linear eddy-viscosity modeling, which aims specifically at an improved representation of turbulence anisotropy.
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Towards the development of a Reynolds-averaged algebraic turbulent scalar-flux model

TL;DR: In this paper, a priori explorations are attempted by processing the LES data presently performed for channel flows under several flow-boundary conditions including shear-free boundaries, and it has been found that an introduction of quadratic products of the Reynolds-stress tensor into the gradient diffusion model may improve the predictive performance.