Modeling of heat transfer and fluid flow characteristics of helicoidal double-pipe heat exchangers using Adaptive Neuro-Fuzzy Inference System (ANFIS)

International Communications in Heat and Mass Transfer

et al. 2016

Desirable effective viscosity behaviour is an essential transport property required for the effective utilisation of nanofluids in industrial systems as well as other applications. Viscosity influences significantly the pumping power and heat transfer effectiveness in a thermal system since Reynolds and Prandtl numbers are functions of viscosity. In this study, the optimum energy required for the preparation of MgO-ethylene glycol (MgO-EG) nanofluids was determined by varying the ultrasonication energy input into the preparation process. The uniformly dispersed nanofluids were characterised and the viscosity measurements were carried out as a function of temperature (20 to Therefore, new correlation is proposed using the method of dimensional analysis and considering essential factors, including nanoparticle size, volume fraction temperature, capping layer thickness, viscosity of the base fluid, the density of base fluid and the density of nanofluid as input parameters.Furthermore, genetic algorithm-polynomial neural network (GA-PNN) and fuzzy C-means clusteringbased adaptive neuro-fuzzy inference system (FCM-ANFIS) was used to model the effective viscosity of the MgO-EG nanofluids considering the parameters mentioned above. The results of all the modelling techniques showed good agreement with the experimental data.

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Section: Fcm-anfis Modelling Technique and Ga-pnn Hybrid Systemmentioning

confidence: 99%

Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques

Adio

International Communications in Heat and Mass Transfer

et al. 2016

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“…The third layer performs normalisation of membership functions, the fourth layer is the conclusive part of fuzzy rules and the last layer calculates network outputs. Detailed information about ANFIS network structure and each layer function is given in Mehrabi et al [13].…”

Section: Fcm-based Neuro-fuzzy Inference System Modelling Techniquementioning

confidence: 99%

“…The neurofuzzy method uses learning approaches derived from an artificial neural network in order to find the appropriate fuzzy membership functions and fuzzy rules. An adaptive neuro-fuzzy inference system (ANFIS) is one of the neuro-fuzzy systems in which a learning algorithm is aligned with an integrated learning approach [13]. Mehrabi et al [14] developed two different models based on an FCM-based neuro-fuzzy inference system (FCM-ANFIS) and a genetic algorithm-polynomial neural network (GA-PNN) approach to model the thermal conductivity ratio of Al 2 O 3 -water nanofluids as function of particle size, volume concentration and temperature.…”

Section: Introductionmentioning

confidence: 99%

Viscosity of nanofluids based on an artificial intelligence model

International Communications in Heat and Mass Transfer

Meyer

2013

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“…The third layer performs normalisation of membership functions and the fourth layer is the conclusive part of fuzzy rules and the last layer calculates network outputs. Detailed information about ANFIS network structure and each layer function is given in Mehrabi et al [8].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina–water nanofluids

International Communications in Heat and Mass Transfer

Meyer

2012

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