Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML Approaches
This study examines heat transfer characteristics by employing a combined augmentation technique that utilises nozzle-type inserts to induce swirling in water/graphene nanofluids at different concentrations. The assessment evaluates its influence on heat transfer, Nusselt number, and thermal perform...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/1/77 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549287450738688 |
|---|---|
| author | Javed Syed |
| author_facet | Javed Syed |
| author_sort | Javed Syed |
| collection | DOAJ |
| description | This study examines heat transfer characteristics by employing a combined augmentation technique that utilises nozzle-type inserts to induce swirling in water/graphene nanofluids at different concentrations. The assessment evaluates its influence on heat transfer, Nusselt number, and thermal performance factor, emphasising its applicability in industrial contexts. This research aims to create a numerical model designed to improve the performance of heat exchangers by employing passive techniques, particularly through the implementation of a convergent–divergent nozzle insert, without the need for experimental validation. The accuracy of the model is confirmed through experimental data, and it is subsequently employed to simulate various Reynolds numbers, generating datasets for training and testing machine learning models. This study also highlights the potential aggregation and flow resistance limitations when combining nanoparticles with passive inserts. The experimental outcomes for the convergent nozzle insert are employed to validate the supervised machine learning model. Subsequently, a numerical analysis of the convergent–divergent nozzle insert is conducted using approximately 220 samples for training and testing purposes. The convergent–divergent nozzle insert improves heat transfer efficiency in heat exchangers by generating high-velocity flow and enhancing temperature gradients. Optimising nozzle geometry through numerical simulations can determine the ideal dimensions for better heat transfer rates. Nanofluids show a thermal performance factor increase of up to 13.2% at higher inlet temperatures than water. The thermal performance factor for nanofluid at inlet higher temperatures is 8.5%, 9.3%, 11.6%, 12.8%, and 13.2% compared to water. |
| format | Article |
| id | doaj-art-97b1ff516bcc451ca05754e8e2b51e6c |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-97b1ff516bcc451ca05754e8e2b51e6c2025-01-10T13:17:02ZengMDPI AGEnergies1996-10732024-12-011817710.3390/en18010077Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML ApproachesJaved Syed0Department of Mechanical Engineering, King Khalid University, Abha 61421, Saudi ArabiaThis study examines heat transfer characteristics by employing a combined augmentation technique that utilises nozzle-type inserts to induce swirling in water/graphene nanofluids at different concentrations. The assessment evaluates its influence on heat transfer, Nusselt number, and thermal performance factor, emphasising its applicability in industrial contexts. This research aims to create a numerical model designed to improve the performance of heat exchangers by employing passive techniques, particularly through the implementation of a convergent–divergent nozzle insert, without the need for experimental validation. The accuracy of the model is confirmed through experimental data, and it is subsequently employed to simulate various Reynolds numbers, generating datasets for training and testing machine learning models. This study also highlights the potential aggregation and flow resistance limitations when combining nanoparticles with passive inserts. The experimental outcomes for the convergent nozzle insert are employed to validate the supervised machine learning model. Subsequently, a numerical analysis of the convergent–divergent nozzle insert is conducted using approximately 220 samples for training and testing purposes. The convergent–divergent nozzle insert improves heat transfer efficiency in heat exchangers by generating high-velocity flow and enhancing temperature gradients. Optimising nozzle geometry through numerical simulations can determine the ideal dimensions for better heat transfer rates. Nanofluids show a thermal performance factor increase of up to 13.2% at higher inlet temperatures than water. The thermal performance factor for nanofluid at inlet higher temperatures is 8.5%, 9.3%, 11.6%, 12.8%, and 13.2% compared to water.https://www.mdpi.com/1996-1073/18/1/77passive techniquegraphene nanofluidheat transferthermal performance factorindustrial application |
| spellingShingle | Javed Syed Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML Approaches Energies passive technique graphene nanofluid heat transfer thermal performance factor industrial application |
| title | Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML Approaches |
| title_full | Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML Approaches |
| title_fullStr | Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML Approaches |
| title_full_unstemmed | Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML Approaches |
| title_short | Enhancement of Heat Transfer Using Water/Graphene Nanofluid and the Impact of Passive Techniques—Experimental, Numerical, and ML Approaches |
| title_sort | enhancement of heat transfer using water graphene nanofluid and the impact of passive techniques experimental numerical and ml approaches |
| topic | passive technique graphene nanofluid heat transfer thermal performance factor industrial application |
| url | https://www.mdpi.com/1996-1073/18/1/77 |
| work_keys_str_mv | AT javedsyed enhancementofheattransferusingwatergraphenenanofluidandtheimpactofpassivetechniquesexperimentalnumericalandmlapproaches |