A parametric study of subcooled flow boiling of Al2O3/water nanofluid using numerical simulation and artificial neural networks. Issue 2 (3rd July 2022)

• Record Type: Journal Article
• Authors: Alimoradi, Hasan; Eskandari, Erfan; Pourbagian, Mahdi; Shams, Mehrzad
• Is Part Of: Nanoscale and microscale thermophysical engineering. Volume 26:Issue 2/3(2022)
• Page Start: 129
• Page End: 159
• Publication Date: 2022-07-03
• Subjects: Nanofluid subcooled flow boiling -- numerical simulation -- bubble dynamics -- artificial neural network -- deep learning; Heat -- Transmission -- Periodicals; Heat -- Transmission; Electronic journals; Periodicals; 621.402
• DOI: 10.1080/15567265.2022.2108949
• Languages: English
• ISSNs: 1556-7265

Abstract

ABSTRACT: Utilizing an Euler-mixture three-dimensional numerical simulation for Al2 O3 /water nanofluid subcooled flow boiling in a mini channel, we study the effects of pressure, heat flux, nanoparticle concentration, surface roughness, and subcooled temperature on heat transfer quantities (average and local heat transfer coefficient, average and local vapor volume fraction, and average and local wall temperature) and bubble dynamics quantities (bubble departure diameter, bubble detachment frequency, bubble detachment waiting time, and nucleation site density). The numerical results demonstrate that the nanoparticles particularly impact the bubble dynamics significantly by increasing wettability and decreasing contact angle. In order to reduce the computational burden of such an expensive multiphase flow simulation, we also present a machine learning approach based on artificial neural networks (ANN). The numerical experiments show that using the ANN model, we can achieve highly accurate results with much less computational time and resources.