Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

• Published in: Heat and mass transfer Vol. 54; no. 6; pp. 1707 - 1719
• Main Authors: Ghasemi, Nahid, Aghayari, Reza, Maddah, Heydar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2018; Springer Nature B.V
• Subjects: Aluminum oxide; Artificial neural networks; Basis functions; Correlation coefficients; Efficiency; Engineering; Engineering Thermodynamics; Exergy; Fluid flow; Heat and Mass Transfer; Heat exchangers; Industrial Chemistry/Chemical Engineering; Mathematical models; Nanocomposites; Nanofluids; Neural networks; Optimization; Original; Parameters; Pipes; Predictions; Radial basis function; Reynolds number; Thermodynamics; Titanium dioxide; exergetic efficiency; radial basis function; nanofluid

The present study aims at predicting and optimizing exergetic efficiency of TiO 2 -Al 2 O 3 /water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Functi...