Publication Cover
Numerical Heat Transfer, Part A: Applications
An International Journal of Computation and Methodology
Volume 70, 2016 - Issue 1
106
Views
9
CrossRef citations to date
0
Altmetric
Original Articles

Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system

, , , , &
Pages 103-116 | Received 04 Aug 2015, Accepted 01 Dec 2015, Published online: 02 May 2016
 

ABSTRACT

Adaptive Neuro-Fuzzy Inference System (ANFIS) opens a new gateway in understanding the complex behaviors and phenomena for different fields such as heat transfer in nanoparticles. The ANFIS method is a shortcut to find a nonlinear relation between input and output and results in valid outcomes, especially in engineering phenomena, which is used here for determining the convective heat transfer coefficient. Using the ANFIS, the critical parameters in heat transfer including convective heat transfer coefficient and pressure drop are determined. To realize this issue, the thermophysical properties of non-covalently and covalently functionalized multiwalled carbon nanotubes-based water nanofluid were investigated experimentally. The results of simulation and their comparison with the experimental results showed an excellent evidence on the validity of the model, which can be expanded for other conditions. The proposed method of ANFIS modeling may be applied to the optimization of carbon-based nanostructure-based water nanofluid in a circular tube with constant heat flux.

Nomenclature

q=

heat flux (W·m−2)

Q=

heat transfer rate (W)

A=

cross section of the tube (m2)

I=

current (A)

V=

voltage (V)

D=

diameter (m)

L=

tube length (m)

E=

error

f=

output of the fuzzy model

=

water mass flow rate (kg·s−1)

Cp=

specific heat of water (J·kg−1·K−1)

h=

heat transfer coefficient (W·m−2·K−1)

T=

temperature (°C)

U=

velocity (m·s−1)

Oi=

calculated output value

p, q, r=

linear parameters in the consequent parts of the fuzzy rules

x, y=

inputs of the fuzzy model

ΔP=

pressure drop (Pa)

µ=

viscosity (Pa.s)

μAi(x)=

membership function of the corresponding linguistic label

μBj(x)=

membership function of the corresponding linguistic label

σ=

isotropic spread of Gaussian basis function

wi=

weight function of layer 4

=

normalized the weight function

Subscripts=
Nf=

nanofluid

B=

bulk fluid

W=

wall

Nomenclature

q=

heat flux (W·m−2)

Q=

heat transfer rate (W)

A=

cross section of the tube (m2)

I=

current (A)

V=

voltage (V)

D=

diameter (m)

L=

tube length (m)

E=

error

f=

output of the fuzzy model

=

water mass flow rate (kg·s−1)

Cp=

specific heat of water (J·kg−1·K−1)

h=

heat transfer coefficient (W·m−2·K−1)

T=

temperature (°C)

U=

velocity (m·s−1)

Oi=

calculated output value

p, q, r=

linear parameters in the consequent parts of the fuzzy rules

x, y=

inputs of the fuzzy model

ΔP=

pressure drop (Pa)

µ=

viscosity (Pa.s)

μAi(x)=

membership function of the corresponding linguistic label

μBj(x)=

membership function of the corresponding linguistic label

σ=

isotropic spread of Gaussian basis function

wi=

weight function of layer 4

=

normalized the weight function

Subscripts=
Nf=

nanofluid

B=

bulk fluid

W=

wall

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 716.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.