Artificial neural networks application on friction factor and heat transfer coefficients prediction in tubes with inner helical-finning

Thermo-hydraulic performance optimization of a disk-shaped microchannel heat sink applying computational fluid dynamics, artificial neural network, and response surface methodology

“…The ANN is employed as a computational method for replicating the actions of neuron-based systems and constructing representations of intricate nonlinear functions [ 51 , 52 ]. This method generally performs controlled learning tasks, building knowledge from data sets where the correct response is presented forward [ 53 , 54 ]. The networks are then trained to detect the correct response, enhancing the precision of their anticipations [ 55 ].…”

Recent advances in the applications of machine learning methods for heat exchanger modeling—a review

“…In the context of pressure drop predictions discussed thus far, it is of considerable importance to recognize the frictional pressure drop as a major component contributing to the overall pressure losses. Some studies have focused on investigating the frictional pressure drop in heat exchangers, such as Najafi et al (Najafi et al, 2021), Xie et al (Xie et al, 2022), Skrypnik et al (Skrypnik et al, 2022), Peng and Ling et al (Peng and Xiang, 2015) and Du et al (X. Du et al, 2020), introduced the estimation model of the friction factor using different machine learning methods.…”

Validation of a new methodological approach for the selection of wire-coil inserts in thermal equipment

“…More recently, Akhavan-Behabadi [1] and Roy and Saha [24] proposed their own friction factor correlations. The correlation proposed by Akhavan-Behabadi was established for 2 mm thickness wire coils and was just a function of Reynolds numbers from [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. Roy and Saha presented a generalized correlation as a function of the helix wire-coil angle and the dimensionless thickness for Re but they only tested three wirecoils.…”

“…Wang et al [36] explore experimentally the effect of turbulent pulsatile flow in a serpentine channel with an innovative winglike turbulators for Re = 10000 and finally, Verma et al [34] carried out a numerical study to predict the transient thermal performance of a PCM embedded parallel flow solar air heater during a 24 h working cycle. Artificial intelligence methods have been employed to predict the thermal and hydraulic characteristics in enhanced heat exchangers [32]. For twisted-tapes the use of response surface method (RSM), genetic algorithm, and Taguchi approach has also been applied [16].…”