Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio
The present study is a pure experimental investigation of the viscosity and rheological properties of the Al2O3-Fe2O3 hybrid nanofluid and the development of a new correlation. The main purpose of the study is to evaluate the effect of the Al2O3-Fe2O3 mixture ratio on the viscosity property and deve...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Elsevier B.V.
2024
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-34256 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-342562024-10-14T11:18:40Z Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio Vicki Wanatasanappan V. Kumar Kanti P. Sharma P. Husna N. Abdullah M.Z. 58093867000 57216493630 58961316700 58093980000 31567537400 Al<sub>2</sub>O<sub>3</sub>-Fe<sub>2</sub>O<sub>3</sub> ANN Bayesian optimization Hybrid nanofluid Machine learning Rheological behaviour Viscosity Alumina Aluminum oxide Bayesian networks Ethylene Ethylene glycol Hematite Machine learning Nanofluidics Neural networks Newtonian liquids Rheology Al2O3-fe2O3 Bayesian optimization Hybrid nanofluid Machine-learning Mixture ratio Nanofluids New correlations Rheological behaviour Rheological property Viscosity properties Viscosity The present study is a pure experimental investigation of the viscosity and rheological properties of the Al2O3-Fe2O3 hybrid nanofluid and the development of a new correlation. The main purpose of the study is to evaluate the effect of the Al2O3-Fe2O3 mixture ratio on the viscosity property and develop a correlation for the viscosity prediction. The Al2O3 and Fe2O3 were first characterized using XRD diffraction and the FESEM technique. The nanofluid was prepared using a two-step method using base fluid consisting of water and ethylene glycol mixture at 60/40 ratios. Five different Al2O3-Fe2O3 nanoparticle compositions were investigated experimentally for the viscosity and rheological properties at temperatures between 0 and 100 �C. The experimental data shows that the Al2O3-Fe2O3 composition of 40/60 resulted in the highest viscosity value at all temperatures investigated, while the 60/40 composition recorded the lowest viscosity value. Besides, the increase in temperature of nanofluid shows a maximum viscosity reduction of 87.2 % as the temperature is increased from 0 to 100 �C. Also, the rheological analysis on a hybrid nanofluid for all compositions of Al2O3-Fe2O3 indicates a Newtonian fluid characteristic. The experimental research data was utilized to create an artificial neural network (ANN)-based architecture. An autoregressive method called the Bayesian approach was adopted for training hyperparameters. During model training, the autoregressive technique assisted in achieving outstanding correlation values of more than 99.99 % with minimal mean squared errors as low as 0.000036. � 2023 Elsevier B.V. Final 2024-10-14T03:18:40Z 2024-10-14T03:18:40Z 2023 Article 10.1016/j.molliq.2023.121365 2-s2.0-85147547914 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147547914&doi=10.1016%2fj.molliq.2023.121365&partnerID=40&md5=9f923f5b261293787bbeb0e28cf2e61b https://irepository.uniten.edu.my/handle/123456789/34256 375 121365 Elsevier B.V. Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| topic |
Al<sub>2</sub>O<sub>3</sub>-Fe<sub>2</sub>O<sub>3</sub> ANN Bayesian optimization Hybrid nanofluid Machine learning Rheological behaviour Viscosity Alumina Aluminum oxide Bayesian networks Ethylene Ethylene glycol Hematite Machine learning Nanofluidics Neural networks Newtonian liquids Rheology Al2O3-fe2O3 Bayesian optimization Hybrid nanofluid Machine-learning Mixture ratio Nanofluids New correlations Rheological behaviour Rheological property Viscosity properties Viscosity |
| spellingShingle |
Al<sub>2</sub>O<sub>3</sub>-Fe<sub>2</sub>O<sub>3</sub> ANN Bayesian optimization Hybrid nanofluid Machine learning Rheological behaviour Viscosity Alumina Aluminum oxide Bayesian networks Ethylene Ethylene glycol Hematite Machine learning Nanofluidics Neural networks Newtonian liquids Rheology Al2O3-fe2O3 Bayesian optimization Hybrid nanofluid Machine-learning Mixture ratio Nanofluids New correlations Rheological behaviour Rheological property Viscosity properties Viscosity Vicki Wanatasanappan V. Kumar Kanti P. Sharma P. Husna N. Abdullah M.Z. Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio |
| description |
The present study is a pure experimental investigation of the viscosity and rheological properties of the Al2O3-Fe2O3 hybrid nanofluid and the development of a new correlation. The main purpose of the study is to evaluate the effect of the Al2O3-Fe2O3 mixture ratio on the viscosity property and develop a correlation for the viscosity prediction. The Al2O3 and Fe2O3 were first characterized using XRD diffraction and the FESEM technique. The nanofluid was prepared using a two-step method using base fluid consisting of water and ethylene glycol mixture at 60/40 ratios. Five different Al2O3-Fe2O3 nanoparticle compositions were investigated experimentally for the viscosity and rheological properties at temperatures between 0 and 100 �C. The experimental data shows that the Al2O3-Fe2O3 composition of 40/60 resulted in the highest viscosity value at all temperatures investigated, while the 60/40 composition recorded the lowest viscosity value. Besides, the increase in temperature of nanofluid shows a maximum viscosity reduction of 87.2 % as the temperature is increased from 0 to 100 �C. Also, the rheological analysis on a hybrid nanofluid for all compositions of Al2O3-Fe2O3 indicates a Newtonian fluid characteristic. The experimental research data was utilized to create an artificial neural network (ANN)-based architecture. An autoregressive method called the Bayesian approach was adopted for training hyperparameters. During model training, the autoregressive technique assisted in achieving outstanding correlation values of more than 99.99 % with minimal mean squared errors as low as 0.000036. � 2023 Elsevier B.V. |
| author2 |
58093867000 |
| author_facet |
58093867000 Vicki Wanatasanappan V. Kumar Kanti P. Sharma P. Husna N. Abdullah M.Z. |
| format |
Article |
| author |
Vicki Wanatasanappan V. Kumar Kanti P. Sharma P. Husna N. Abdullah M.Z. |
| author_sort |
Vicki Wanatasanappan V. |
| title |
Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio |
| title_short |
Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio |
| title_full |
Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio |
| title_fullStr |
Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio |
| title_full_unstemmed |
Viscosity and rheological behavior of Al2O3-Fe2O3/water-EG based hybrid nanofluid: A new correlation based on mixture ratio |
| title_sort |
viscosity and rheological behavior of al2o3-fe2o3/water-eg based hybrid nanofluid: a new correlation based on mixture ratio |
| publisher |
Elsevier B.V. |
| publishDate |
2024 |
| _version_ |
1814061047806427136 |
| score |
13.222552 |