ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel

Javad Mohammadpour; Zhaleh Ghouchani; Fatemeh Salehi; Ann Lee

doi:10.1007/978-3-030-93456-9_21

ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel

Javad Mohammadpour^*, Zhaleh Ghouchani, Fatemeh Salehi, Ann Lee

^*Corresponding author for this work

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Citations (Scopus)

Abstract

In this paper, integrating different cooling techniques into a microchannel heat sink (MCHS) is evaluated to enhance the heat transfer rate. A parametric study is conducted using the computational fluid dynamics simulation consisting of the discrete phase model (CFD-DPM) to understand the effect of operational and geometrical parameters on cooling performance. The CFD-DPM simulations for varying different influential parameters are conducted to obtain the local temperature over the silicon wafer wall, creating a large set of samples. The artificial neural network (ANN) method is then employed to discover an accurate model to predict the local temperature. The ANN model includes two hidden layers and 24 neurons in each layer, showing a precise estimation of temperature with an overall mean square error (MSE) value and correlation coefficient (R) of 6.974810 ^{- 7} and 0.9952, respectively. The temperature distributions along the silicon wall predicted by both CFD-DPM and ANN models verify that smaller sizes of alumina nanoparticles improve heat transfer remarkably compared to larger particles.

Original language	English
Title of host publication	Multiphase flow dynamics
Subtitle of host publication	a perspective from the Brazilian Academy and Industry
Editors	Marcio Ferreira Martins, Rogério Ramos, Humberto Belich
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Chapter	21
Pages	231-241
Number of pages	11
ISBN (Electronic)	9783030934569
ISBN (Print)	9783030934552
DOIs	https://doi.org/10.1007/978-3-030-93456-9_21
Publication status	Published - 2022
Event	Multiphase Flow Journey (6th : 2021) - Virtual Duration: 17 May 2021 → 20 May 2021 Conference number: 6th

Publication series

Name	Lecture Notes in Mechanical Engineering
Publisher	Springer
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	Multiphase Flow Journey (6th : 2021)
Abbreviated title	JEM 2021
City	Virtual
Period	17/05/21 → 20/05/21

Keywords

Computational fluid dynamics (CFD)
Artificial neural network (ANN)
Discrete phase model (DPM)
Nanofluid heat transfer
Synthetic jet (SJ)

Access to Document

10.1007/978-3-030-93456-9_21

Cite this

Mohammadpour, J., Ghouchani, Z., Salehi, F., & Lee, A. (2022). ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel. In M. Ferreira Martins, R. Ramos, & H. Belich (Eds.), Multiphase flow dynamics: a perspective from the Brazilian Academy and Industry (pp. 231-241). (Lecture Notes in Mechanical Engineering). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-93456-9_21

Mohammadpour, Javad ; Ghouchani, Zhaleh ; Salehi, Fatemeh et al. / ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel. Multiphase flow dynamics: a perspective from the Brazilian Academy and Industry. editor / Marcio Ferreira Martins ; Rogério Ramos ; Humberto Belich. Cham, Switzerland : Springer, Springer Nature, 2022. pp. 231-241 (Lecture Notes in Mechanical Engineering).

@inbook{0f35f6948a1040e2a0b303c8dba59ec7,

title = "ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel",

abstract = "In this paper, integrating different cooling techniques into a microchannel heat sink (MCHS) is evaluated to enhance the heat transfer rate. A parametric study is conducted using the computational fluid dynamics simulation consisting of the discrete phase model (CFD-DPM) to understand the effect of operational and geometrical parameters on cooling performance. The CFD-DPM simulations for varying different influential parameters are conducted to obtain the local temperature over the silicon wafer wall, creating a large set of samples. The artificial neural network (ANN) method is then employed to discover an accurate model to predict the local temperature. The ANN model includes two hidden layers and 24 neurons in each layer, showing a precise estimation of temperature with an overall mean square error (MSE) value and correlation coefficient (R) of 6.974810 - 7 and 0.9952, respectively. The temperature distributions along the silicon wall predicted by both CFD-DPM and ANN models verify that smaller sizes of alumina nanoparticles improve heat transfer remarkably compared to larger particles.",

keywords = "Computational fluid dynamics (CFD), Artificial neural network (ANN), Discrete phase model (DPM), Nanofluid heat transfer, Synthetic jet (SJ)",

author = "Javad Mohammadpour and Zhaleh Ghouchani and Fatemeh Salehi and Ann Lee",

year = "2022",

doi = "10.1007/978-3-030-93456-9_21",

language = "English",

isbn = "9783030934552",

series = "Lecture Notes in Mechanical Engineering",

publisher = "Springer, Springer Nature",

pages = "231--241",

editor = "{Ferreira Martins}, Marcio and Rog{\'e}rio Ramos and Humberto Belich",

booktitle = "Multiphase flow dynamics",

address = "United States",

note = "Multiphase Flow Journey (6th : 2021), JEM 2021 ; Conference date: 17-05-2021 Through 20-05-2021",

}

Mohammadpour, J, Ghouchani, Z, Salehi, F & Lee, A 2022, ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel. in M Ferreira Martins, R Ramos & H Belich (eds), Multiphase flow dynamics: a perspective from the Brazilian Academy and Industry. Lecture Notes in Mechanical Engineering, Springer, Springer Nature, Cham, Switzerland, pp. 231-241, Multiphase Flow Journey (6th : 2021), Virtual, 17/05/21. https://doi.org/10.1007/978-3-030-93456-9_21

ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel. / Mohammadpour, Javad; Ghouchani, Zhaleh; Salehi, Fatemeh et al.
Multiphase flow dynamics: a perspective from the Brazilian Academy and Industry. ed. / Marcio Ferreira Martins; Rogério Ramos; Humberto Belich. Cham, Switzerland: Springer, Springer Nature, 2022. p. 231-241 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel

AU - Mohammadpour, Javad

AU - Ghouchani, Zhaleh

AU - Salehi, Fatemeh

AU - Lee, Ann

N1 - Conference code: 6th

PY - 2022

Y1 - 2022

N2 - In this paper, integrating different cooling techniques into a microchannel heat sink (MCHS) is evaluated to enhance the heat transfer rate. A parametric study is conducted using the computational fluid dynamics simulation consisting of the discrete phase model (CFD-DPM) to understand the effect of operational and geometrical parameters on cooling performance. The CFD-DPM simulations for varying different influential parameters are conducted to obtain the local temperature over the silicon wafer wall, creating a large set of samples. The artificial neural network (ANN) method is then employed to discover an accurate model to predict the local temperature. The ANN model includes two hidden layers and 24 neurons in each layer, showing a precise estimation of temperature with an overall mean square error (MSE) value and correlation coefficient (R) of 6.974810 - 7 and 0.9952, respectively. The temperature distributions along the silicon wall predicted by both CFD-DPM and ANN models verify that smaller sizes of alumina nanoparticles improve heat transfer remarkably compared to larger particles.

AB - In this paper, integrating different cooling techniques into a microchannel heat sink (MCHS) is evaluated to enhance the heat transfer rate. A parametric study is conducted using the computational fluid dynamics simulation consisting of the discrete phase model (CFD-DPM) to understand the effect of operational and geometrical parameters on cooling performance. The CFD-DPM simulations for varying different influential parameters are conducted to obtain the local temperature over the silicon wafer wall, creating a large set of samples. The artificial neural network (ANN) method is then employed to discover an accurate model to predict the local temperature. The ANN model includes two hidden layers and 24 neurons in each layer, showing a precise estimation of temperature with an overall mean square error (MSE) value and correlation coefficient (R) of 6.974810 - 7 and 0.9952, respectively. The temperature distributions along the silicon wall predicted by both CFD-DPM and ANN models verify that smaller sizes of alumina nanoparticles improve heat transfer remarkably compared to larger particles.

KW - Computational fluid dynamics (CFD)

KW - Artificial neural network (ANN)

KW - Discrete phase model (DPM)

KW - Nanofluid heat transfer

KW - Synthetic jet (SJ)

UR - http://www.scopus.com/inward/record.url?scp=85128522559&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-93456-9_21

DO - 10.1007/978-3-030-93456-9_21

M3 - Chapter

AN - SCOPUS:85128522559

SN - 9783030934552

T3 - Lecture Notes in Mechanical Engineering

SP - 231

EP - 241

BT - Multiphase flow dynamics

A2 - Ferreira Martins, Marcio

A2 - Ramos, Rogério

A2 - Belich, Humberto

PB - Springer, Springer Nature

CY - Cham, Switzerland

T2 - Multiphase Flow Journey (6th : 2021)

Y2 - 17 May 2021 through 20 May 2021

ER -

Mohammadpour J, Ghouchani Z, Salehi F , Lee A. ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel. In Ferreira Martins M, Ramos R, Belich H, editors, Multiphase flow dynamics: a perspective from the Brazilian Academy and Industry. Cham, Switzerland: Springer, Springer Nature. 2022. p. 231-241. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-3-030-93456-9_21

ANN and CFD-DPM modeling of alumina-water nanofluid heat transfer in a double synthetic jet microchannel

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this