Modelling of drag reduction of silica nanofluid in single-phase flow of water through horizontal pipelines using support vector regression optimized by genetic algorithm and comparison between the model results and experimental data

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

GHAEDI A.M.; POURANFARD A.R.; VAFAEI A.; RAMEZANI N.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Applied Research in Chemistry Year:2020 | Volume:13 | Issue:4 Page(s): 101-111

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

414

Download:

Cites:

Information Journal Paper

Title

Modelling of drag reduction of silica nanofluid in single-phase flow of water through horizontal pipelines using support vector regression optimized by genetic algorithm and comparison between the model results and experimental data

Author(s)

GHAEDI A.M. | POURANFARD A.R. | VAFAEI A. | RAMEZANI N. | Issue Writer Certificate

Keywords

DragQ1

Support vector regressionQ1

Genetic algorithmQ2

NanofluidQ1

Horizontal pipelinesQ1

WaterQ1

Abstract

Drag reduction prediction plays an important role in oil and gas industries. Due to the nonlinearity and instability of Drag reduction, the precision of the commonly used conventional methods, including regression analyses, has been limited. A prediction model based on Support vector regression (SVR) is presented in this paper to predict Drag reduction by Nanofluids in single-phase flow of Water through horizontal pipes. To construct an effective SVR model, the SVR parameters must be set carefully. This study proposes a hybrid approach, known as Support vector regression-Genetic algorithm (SVR-GA), which searches for the optimal SVR parameters using GA, and accepts the optimal parameters to create the SVR models. The results indicated that the obtained Drag reduction values by the proposed model are in good agreement with the experimental data. The performance of the SVR-GA model was compared with multiple linear regression (MLR). The coefficient of determination (R2) of 0. 9485 and 0. 8740; mean square error (MSE) of 0. 01177 and 0. 01772, for experimental and predicted data by SVR-GA and MLR models were obtained, respectively. This result shows that SVR-GA can be applied as an effective approach to predict Drag reduction.

Cites

No record.

References

No record.

Cite

APA: Copy

GHAEDI, A.M., POURANFARD, A.R., VAFAEI, A., & RAMEZANI, N.. (2020). Modelling of drag reduction of silica nanofluid in single-phase flow of water through horizontal pipelines using support vector regression optimized by genetic algorithm and comparison between the model results and experimental data. JOURNAL OF APPLIED RESEARCHES IN CHEMISTRY (JARC), 13(4 ), 101-111. SID. https://sid.ir/paper/180159/en

Vancouver: Copy

GHAEDI A.M., POURANFARD A.R., VAFAEI A., RAMEZANI N.. Modelling of drag reduction of silica nanofluid in single-phase flow of water through horizontal pipelines using support vector regression optimized by genetic algorithm and comparison between the model results and experimental data. JOURNAL OF APPLIED RESEARCHES IN CHEMISTRY (JARC)[Internet]. 2020;13(4 ):101-111. Available from: https://sid.ir/paper/180159/en

IEEE: Copy

A.M. GHAEDI, A.R. POURANFARD, A. VAFAEI, and N. RAMEZANI, “Modelling of drag reduction of silica nanofluid in single-phase flow of water through horizontal pipelines using support vector regression optimized by genetic algorithm and comparison between the model results and experimental data,” JOURNAL OF APPLIED RESEARCHES IN CHEMISTRY (JARC), vol. 13, no. 4 , pp. 101–111, 2020, [Online]. Available: https://sid.ir/paper/180159/en

Related Journal Papers