CFD—ANN study on axial dispersion in helical and serpentine millimetric coils.
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| Title: | CFD—ANN study on axial dispersion in helical and serpentine millimetric coils. |
|---|---|
| Authors: | Sen, Nirvik1,2 (AUTHOR) nirvik@barc.gov.in, Singh, K.K.1,2 (AUTHOR), Shenoy, K.T.3 (AUTHOR) |
| Source: | Chemical Engineering Communications. 2025, Vol. 212 Issue 1, p1-20. 20p. |
| Subjects: | Artificial neural networks, Tubular reactors, Pressure drop (Fluid dynamics), Serpentine, Ionic liquids |
| Abstract: | CFD simulations are reported to compare hydrodynamic/axial dispersion in a classical helical-shaped and planar serpentine-shaped tubular reactor at a millimetric scale (1-5 mm) for the first time. A two-step simulation approach is validated against experimental data on residence time distribution in serpentine coils. The method is then used for parametric analysis to explore the impact of geometric parameters on the coiling factor and axial dispersion coefficient. Dean vortices in the coiling region are visualized. The findings from the parametric analysis are utilized to develop empirical correlations and Artificial Neural Network (ANN) models for estimating coiling factors and axial dispersion coefficients in both serpentine and helical coils. The absolute average relative error (AERR) in predicting the coiling factor is 3.5% for helical coils and 4.6% for serpentine coils. The AERR of the ANN model for predicting the axial dispersion coefficient is 14% for helical coils and 10% for serpentine coils. The study reveals that both axial dispersion coefficient and pressure drop are higher in serpentine coils compared to helical coils. Axial dispersion is found to be relatively unaffected by pitch (helical) or bend diameter (serpentine) and tube diameter but shows significant reduction with a decrease in pitch circle diameter (helical) or gap between bends (serpentine). The developed correlations and ANN models can aid in the precise sizing of tubular reactors designed as helical or serpentine coils. This is exemplified through a case study involving the synthesis of an ionic liquid, where the helical configuration demonstrated a performance advantage of approximately 6% over the serpentine configuration. [ABSTRACT FROM AUTHOR] |
| Copyright of Chemical Engineering Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 180649645 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CFD—ANN study on axial dispersion in helical and serpentine millimetric coils. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sen%2C+Nirvik%22">Sen, Nirvik</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> nirvik@barc.gov.in</i><br /><searchLink fieldCode="AR" term="%22Singh%2C+K%2EK%2E%22">Singh, K.K.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shenoy%2C+K%2ET%2E%22">Shenoy, K.T.</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Chemical+Engineering+Communications%22">Chemical Engineering Communications</searchLink>. 2025, Vol. 212 Issue 1, p1-20. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Tubular+reactors%22">Tubular reactors</searchLink><br /><searchLink fieldCode="DE" term="%22Pressure+drop+%28Fluid+dynamics%29%22">Pressure drop (Fluid dynamics)</searchLink><br /><searchLink fieldCode="DE" term="%22Serpentine%22">Serpentine</searchLink><br /><searchLink fieldCode="DE" term="%22Ionic+liquids%22">Ionic liquids</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: CFD simulations are reported to compare hydrodynamic/axial dispersion in a classical helical-shaped and planar serpentine-shaped tubular reactor at a millimetric scale (1-5 mm) for the first time. A two-step simulation approach is validated against experimental data on residence time distribution in serpentine coils. The method is then used for parametric analysis to explore the impact of geometric parameters on the coiling factor and axial dispersion coefficient. Dean vortices in the coiling region are visualized. The findings from the parametric analysis are utilized to develop empirical correlations and Artificial Neural Network (ANN) models for estimating coiling factors and axial dispersion coefficients in both serpentine and helical coils. The absolute average relative error (AERR) in predicting the coiling factor is 3.5% for helical coils and 4.6% for serpentine coils. The AERR of the ANN model for predicting the axial dispersion coefficient is 14% for helical coils and 10% for serpentine coils. The study reveals that both axial dispersion coefficient and pressure drop are higher in serpentine coils compared to helical coils. Axial dispersion is found to be relatively unaffected by pitch (helical) or bend diameter (serpentine) and tube diameter but shows significant reduction with a decrease in pitch circle diameter (helical) or gap between bends (serpentine). The developed correlations and ANN models can aid in the precise sizing of tubular reactors designed as helical or serpentine coils. This is exemplified through a case study involving the synthesis of an ionic liquid, where the helical configuration demonstrated a performance advantage of approximately 6% over the serpentine configuration. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Chemical Engineering Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00986445.2024.2391852 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Tubular reactors Type: general – SubjectFull: Pressure drop (Fluid dynamics) Type: general – SubjectFull: Serpentine Type: general – SubjectFull: Ionic liquids Type: general Titles: – TitleFull: CFD—ANN study on axial dispersion in helical and serpentine millimetric coils. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sen, Nirvik – PersonEntity: Name: NameFull: Singh, K.K. – PersonEntity: Name: NameFull: Shenoy, K.T. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00986445 Numbering: – Type: volume Value: 212 – Type: issue Value: 1 Titles: – TitleFull: Chemical Engineering Communications Type: main |
| ResultId | 1 |
