A transfer learning metamodel using artificial neural networks for natural convection flows in enclosures

Ashouri, Majid; Hashemi, Alireza

doi:10.1016/j.csite.2022.102179

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Maddah et al [16] applied ANN to predict the exegetic efficiency in a double-pipe heat exchanger equipped with twisted tapes using experimental data; there were five designed parameters as inputs for the ANN. Ashouri et al [17] used a deep neural network to predict the Nusselt number for a two dimensional square enclosure. It is worth noting that all the above ANN-based thermal system predictors achieve high accuracy with the determination coefficient R 2 > 0.99.…”

Section: Introductionmentioning

confidence: 99%

Thermal Performance in Convection Flow of Nanofluids Using a Deep Convolutional Neural Network

et al. 2022

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This study develops a geometry adaptive, physical field predictor for the combined forced and natural convection flow of a nanofluid in horizontal single or double-inner cylinder annular pipes with various inner cylinder sizes and placements based on deep learning. The predictor is built with a convolutional-deconvolutional structure, where the input is the annulus cross-section geometry and the output is the temperature and the Nusselt number for the nanofluid-filled annulus. Profiting from the proven ability of dealing with pixel-like data, the convolutional neural network (CNN)-based predictor enables an accurate end-to-end mapping from the geometry input and the desired nanofluid physical field. Taking the computational fluid dynamics (CFD) calculation as the basis of our approach, the obtained results show that the average accuracy of the predicted temperature field and the coefficient of determination R2 are more than 99.9% and 0.998 accurate for single-inner cylinder nanofluid-filled annulus; while for the more complex case of double-inner cylinder, the results are still very close, higher than 99.8% and 0.99, respectively. Furthermore, the predictor takes only 0.038 s for each nanofluid field prediction, four orders of magnitude faster than the numerical simulation. The high accuracy and the fast speed estimation of the proposed predictor show the great potential of this approach to perform efficient inner cylinder configuration design and optimization for nanofluid-filled annulus.

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Section: Introductionmentioning

confidence: 99%