Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system
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, espe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Savari, Maryam [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: Copyright © Taylor & Francis Group, LLC 2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Numerical heat transfer / A - Washington, DC : Taylor & Francis, 1989, 70(2016), 1, Seite 103-14 |
|---|---|
| Übergeordnetes Werk: |
volume:70 ; year:2016 ; number:1 ; pages:103-14 |
| Links: |
|---|
| DOI / URN: |
10.1080/10407782.2016.1139974 |
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OLC1980211558 |
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| 520 | |a 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. | ||
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Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system |
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Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system |
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Savari, Maryam |
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Numerical heat transfer / A |
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Numerical heat transfer / A |
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eng |
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2016 |
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Savari, Maryam |
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Savari, Maryam |
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10.1080/10407782.2016.1139974 |
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620 530 |
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hydrodynamic and thermal performance prediction of functionalized mwnt-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system |
| title_auth |
Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system |
| 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. |
| abstractGer |
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. |
| abstract_unstemmed |
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. |
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Hydrodynamic and thermal performance prediction of functionalized MWNT-based water nanofluids under the laminar flow regime using the adaptive neuro-fuzzy inference system |
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http://dx.doi.org/10.1080/10407782.2016.1139974 http://www.tandfonline.com/doi/abs/10.1080/10407782.2016.1139974 http://search.proquest.com/docview/1805785825 |
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Rashidi, Sajjad Amiri, Ahmad Shanbedi, Mehdi Zeinali Heris, Saeed Kazi, S. N |
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Rashidi, Sajjad Amiri, Ahmad Shanbedi, Mehdi Zeinali Heris, Saeed Kazi, S. N |
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2024-07-04T02:38:06.742Z |
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