Journal ArticleDOI
Machine learning regression-CFD models for the nanofluid heat transfer of a microchannel heat sink with double synthetic jets
TLDR
In this paper , a comprehensive analysis consisting of computational fluid dynamics (CFD) and machine learning algorithms (MLAs) was conducted to study the effect of geometrical and operational parameters on nanofluid heat transfer in a microchannel heat sink (MCHS) with double synthetic jets (SJs).About:
This article is published in International Communications in Heat and Mass Transfer.The article was published on 2022-01-01. It has received 31 citations till now. The article focuses on the topics: Nanofluid & Heat transfer.read more
Citations
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A computational study on nanofluid impingement jets in thermal management of photovoltaic panel
TL;DR: In this paper , a nanofluid jet impingement cooling (JIC) system with different configurations is developed and integrated into the photovoltaic (PV) cells to control the surface temperature.
Journal ArticleDOI
Investigation of Double-Layered Wavy Microchannel Heatsinks Utilizing Porous Ribs with Artificial Neural Networks
Journal ArticleDOI
Investigation of double-layered wavy microchannel heatsinks utilizing porous ribs with artificial neural networks
TL;DR: In this paper , a double layered micro-channel heat sink with wavy up-down and porous ribs alongside each other is proposed to achieve better thermal and hydraulic performance with an insignificant computational time.
Journal ArticleDOI
Shape optimization of hotspot targeted micro pin fins for heterogeneous integration applications
TL;DR: In this article , the combination of impingement jet array of liquid water and non-uniform hotspot targeted micro pin fin, printed on chips, as a potential heat transfer augmentation technique was investigated.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
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Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.