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Andrea Ianiro

Researcher at Charles III University of Madrid

Publications -  75
Citations -  1749

Andrea Ianiro is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Turbulence & Boundary layer. The author has an hindex of 20, co-authored 67 publications receiving 1224 citations. Previous affiliations of Andrea Ianiro include University of Naples Federico II & Complutense University of Madrid.

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Thermo-fluid-dynamics of submerged jets impinging at short nozzle-to-plate distance: A review

TL;DR: In this article, some of the experimental contributions evolved while studying the heat transfer behavior of these jets (with a specific focusing on the secondary annular peak) are reviewed, along with the development of specific experimental techniques in thermal-fluid sciences over the last 50 years.
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Three-dimensional vortex dynamics and convective heat transfer in circular and chevron impinging jets

TL;DR: In this article, an experimental investigation at Reynolds number equal to 5000 on circular and chevron impinging jets by means of time-resolved tomographic particle image velocimetry (TR-TOMO PIV) and infrared (IR) thermography is performed at kilohertz repetition rate in a tailored water jet facility where a plate is placed at a distance of 4 diameters from the nozzle exit.
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POD-based Background Removal for Particle Image Velocimetry

TL;DR: The results show that, unlike existing techniques, the proposed method is robust in the presence of significant background noise intensity, gradients, and temporal oscillations and the computational cost is one to two orders of magnitude lower than conventional image normalization methods.
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Heat transfer rate and uniformity in multichannel swirling impinging jets

TL;DR: In this article, the influence of the swirl number on the wall heat transfer distribution on a flat plate with a swirling air jet impinging on it is experimentally analyzed, and the dependence of heat transfer rate and uniformity on swirl number is also explained.
Posted Content

Convolutional-network models to predict wall-bounded turbulence from wall quantities

TL;DR: Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs, showing better predictions than the extended proper orthogonal decomposition (EPOD), which establishes a linear relation between the input and output fields.