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Yassin A. Hassan

Researcher at Texas A&M University

Publications -  388
Citations -  5486

Yassin A. Hassan is an academic researcher from Texas A&M University. The author has contributed to research in topics: Turbulence & Particle image velocimetry. The author has an hindex of 32, co-authored 371 publications receiving 4467 citations. Previous affiliations of Yassin A. Hassan include University of Illinois at Urbana–Champaign & University of Texas at Austin.

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Invariant analysis of the Reynolds stress tensor for a nuclear fuel assembly with spacer grid and split type vanes

TL;DR: In this article, the spectral element code Nek5000 has been used to investigate the turbulent structure of the flow across a pressurized water reactor spacer grid with split type mixing vanes.
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Turbulence Structure Modification and Drag Reduction by Microbubble Injections in a Boundary Layer Channel Flow

TL;DR: In this article, the presence of microbubbles within the boundary layer modifies the turbulence structure such that variations in time and space turbulent scales are observed, as well as ejection and sweep phenomena.
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Flow visualization study at the interface of alternating pitch tube bundles in a model helical coil steam generator using particle image velocimetry

TL;DR: In this paper, a simplified helical coil steam generator (HCSG) model was constructed to study the flow on the shell side of rod bundles that coil against one another, using a 12-degree single rod bundle interface.
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A proper observation and characterization of wall nucleation phenomena in a forced convective boiling system

TL;DR: In this article, an image analysis method was developed to efficiently and accurately extract the quantitative information of fundamental bubble parameters from the experimental images of boiling bubbles, and the performance of this method was tested in subcooled boiling flow circumstances, and it seems promising as a means of analyzing the vast number of images required to satisfy the high statistical demands to characterize wall nucleation features.