Andrea Ianiro
Charles III University of Madrid
83 Papers
120 Citations
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. Previous affiliations of Andrea Ianiro include University of Naples Federico II & Complutense University of Madrid.
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Papers
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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Convolutional-network models to predict wall-bounded turbulence from wall quantities
Luca Guastoni,A. Güemes,Andrea Ianiro,Stefano Discetti,Philipp Schlatter,Hossein Azizpour,Ricardo Vinuesa +6 more
TL;DR: In this paper, 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.
POD-based Background Removal for Particle Image Velocimetry
M.A. Mendez,Marco Raiola,Alessandro Masullo,Stefano Discetti,Andrea Ianiro,Raf Theunissen,Jean-Marie Buchlin +6 more
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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•Posted Content
Convolutional-network models to predict wall-bounded turbulence from wall quantities
Luca Guastoni,A. Güemes,Andrea Ianiro,Stefano Discetti,Philipp Schlatter,Hossein Azizpour,Ricardo Vinuesa +6 more
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.
Heat transfer rate and uniformity in multichannel swirling impinging jets
Andrea Ianiro,Gennaro Cardone +1 more
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.
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