PresentationPDF Available

USING DESIGN OF EXPERIMENTS “DOE” IN MODELING OF EFFECTIVE THERMAL CONDUCTIVITY OF NANOFLUIDS

November 2017

DOI:10.13140/RG.2.2.18174.46401

Conference: 4th International Conference on Pure and Applied Sciences
At: Gelisim University, Istanbul, Turkey.

Authors:

University of Guelma

The application of nanoﬂuids in energy systems is developing day by day. Before using a nanoﬂuid in an energy system, it is necessary to measure properties of nanoﬂuids. Many researchers have developed empirical correlations to estimate nanofluids thermal conductivity. This paper focuses on designing a design of experiments (DOE) approach which can predict thermal conductivity ratio (TCR) from input experimental data including temperature, volume fraction and nanoparticle diameter. Three types of nanofluides namely Al2O3-Water, CUO-Water and TiO2-Water are used to evaluate the effectiveness of the proposed mathematical model. The created model predicts and results which have been compared to experimental data are showing a good accordance, with the RSME of 0.0247 and R2 –value of 1.

figure1: Design of Experiments An experimental design method was adopted to optimize the thermal conductivity of nanofluides. The factorial design helps to study the effects caused by independent factors and interactions between those self-governing factors.

Actual (1) and predicted (2) values for TCR models

The TRC predict versus X1(φ), X2(T), X3(dp) and X4 (L) [red lines: predicted TRC; blue Lines: 95% conﬁdence interval of the mean TRC]

Comparison of the measured thermal conductivity ratio with the proposed Correlation and data reported by various researchers

Figures - uploaded by Wassila Grine

Content may be subject to copyright.

Content uploaded by Wassila Grine

Content may be subject to copyright.

ResearchGate has not been able to resolve any citations for this publication.

ResearchGate has not been able to resolve any references for this publication.

Poster

Prediction of thermal Conductivity of Alumina Water-Based Nanofluids Using Experimental Data and Art...

December 2018

M E Hocine Benhamza

Recently, many empirical correlations have been developed to estimate nanofluids thermal conductivity and that in order to increase heat transfer enhancement. This work uses Artificial Neural Network approach for predicting thermal conductivity ratio (TCR), in relation to volume fraction, temperature and nanoparticle diameter. Three types of nanofluides namely Al2O3-Water, CuO-Water and ... [Show full abstract] TiO2-Water are used to evaluate the effectiveness of the proposed mathematical model. The predicted model shows a good agreement compared to a set of experimental data as well as to other mathematical model results. Thus, confirming the validity and the practicability of the adopted model.

Poster

Full-text available

Thermal conductivity modeling of Oxide-Water nanofluids using JMP

April 2016

The application of nanoﬂuids in energy systems is developing day by day. Before using a nanoﬂuid in an energy system, it is necessary to measure the properties of nanoﬂuids. Many researchers have developed empirical correlations of nanofluids' thermal conductivity as a function of temperature, volume fraction and nanoparticle size to estimate nanofluids' thermal conductivity. The aims of the ... [Show full abstract] present study are to find an adequate model to estimate the Oxide-Water nanofluides thermal conductivity ratio (TCR) as a function of temperature, volume fraction and nanoparticle diameter, using Design of Experiments “JMP”. The model predictions have been compared to other models such as Yu-Choi and Mintsa et al. The theoretical predictions are compared with experimental data.

Poster

Full-text available

Predictive models of the thermal conductivity of the nanoparticles for simulation of the nanofluides

April 2014

Poster

Full-text available

Application of artiﬁcial neural network (ANN) for the prediction of nanoﬂuids thermal conductivity /...

November 2019

Wassila Grine

Poster

Full-text available

Predicting the effective thermal conductivity of Alumina/water and Alumina/Ethylene glycol nanoﬂuids...

March 2019

The aims of the present study are to develop and validate an artiﬁcial neural network (ANN) approach to estimate the thermal conductivity ratio (TCR) of Alumina/water and Alumina /EG nanofluids as a function of temperature, volume fraction and diameter of the nanoparticle. For the purpose of evaluating the accuracy of the proposed model by ANN for both of Alumina /water and Alumina /EG ... [Show full abstract] nanofluids, root mean square error (RMSE) and regression coefﬁcient (R2) are utilized. The presented correlation relationship estimates empirical data very well. However, the artiﬁcial neural network for Alumina /EG has a higher regression coefﬁcient and lower error compared to Alumina /water nanofluid. The results indicate that these two new models have great ability to predict thermal conductivity and show excellent agreement with the experimental results.