Machine-learning aided calibration and analysis of porous media CFD models used for rotating packed beds Article Swipe
Ahmed M. Alatyar
,
Abdallah S. Berrouk
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijft.2024.100845
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijft.2024.100845
The proposed research is an attempt to advance the state-of-the-art of the numerical modelling of RPB by combining Computational Fluid Dynamics and Machine Learning approaches. The latter creates an accurate framework that should help quantify the potential of Rotating Packed Beds (RPB) technology to intensify conventional CO2 capture processes. To this end, a direct sensitivity analysis is detailed to supplement a machine-learning (ML) algorithm built for calibrating resistance coefficients needed for porous media modelling. The algorithm is used to improve CFD predictions of dry pressure drop in rotating packed beds (RPBs) for a wide range of operating conditions. The sensitivity derivatives with respect to the packing resistance coefficients are demonstrated for the first time in RPBs, which is not available in the current CFD open source and commercial codes. In this regard, sensitivity differential equations are derived from three-dimensional Navier-Stokes equations for porous media in a rotating reference frame. These sensitivity equations are discretized using a finite volume scheme and solved to obtain the sensitivity pressure drop differences at the packing edges. The results are validated against the predictions of the analytical sensitivity analysis and the finite difference approximation. After, the Newton – Gauss method that employs the sensitivity pressure drop derivatives, is used to minimize the error (cost function) between the pressure drop obtained from CFD simulations and the available experimental data. This is achieved by tuning the packing resistance coefficients to the RBPs' operating conditions (gas flowrate and rotating speed) and correlate them using an artificial neural network (ANN). The results of the proposed approach show a significant improvement in porous media-based CFD predictions of RPBs' pressure drop across a wide range of operating conditions and this over conventional porous media-based CFD models. This is necessary for CFD models to be reliably used as a tool that can efficiently improve existing RPBs' designs and/or participate in RPBs' design innovation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ijft.2024.100845
- OA Status
- gold
- Cited By
- 2
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402095583
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402095583Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ijft.2024.100845Digital Object Identifier
- Title
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Machine-learning aided calibration and analysis of porous media CFD models used for rotating packed bedsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-31Full publication date if available
- Authors
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Ahmed M. Alatyar, Abdallah S. BerroukList of authors in order
- Landing page
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https://doi.org/10.1016/j.ijft.2024.100845Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.ijft.2024.100845Direct OA link when available
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Computational fluid dynamics, Calibration, Porous medium, Packed bed, Porosity, Computer science, Materials science, Mechanics, Chromatography, Chemistry, Physics, Composite material, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(ANN). | 250 |
| abstract_inverted_index.(RPBs) | 90 |
| abstract_inverted_index.After, | 189 |
| abstract_inverted_index.Newton | 191 |
| abstract_inverted_index.Packed | 39 |
| abstract_inverted_index.across | 270 |
| abstract_inverted_index.and/or | 305 |
| abstract_inverted_index.codes. | 128 |
| abstract_inverted_index.design | 309 |
| abstract_inverted_index.direct | 53 |
| abstract_inverted_index.edges. | 171 |
| abstract_inverted_index.finite | 156, 186 |
| abstract_inverted_index.frame. | 148 |
| abstract_inverted_index.latter | 26 |
| abstract_inverted_index.method | 194 |
| abstract_inverted_index.models | 290 |
| abstract_inverted_index.needed | 69 |
| abstract_inverted_index.neural | 248 |
| abstract_inverted_index.obtain | 162 |
| abstract_inverted_index.packed | 88 |
| abstract_inverted_index.porous | 71, 142, 262, 281 |
| abstract_inverted_index.scheme | 158 |
| abstract_inverted_index.should | 32 |
| abstract_inverted_index.solved | 160 |
| abstract_inverted_index.source | 125 |
| abstract_inverted_index.speed) | 241 |
| abstract_inverted_index.tuning | 227 |
| abstract_inverted_index.volume | 157 |
| abstract_inverted_index.Machine | 22 |
| abstract_inverted_index.advance | 7 |
| abstract_inverted_index.against | 176 |
| abstract_inverted_index.attempt | 5 |
| abstract_inverted_index.between | 210 |
| abstract_inverted_index.capture | 47 |
| abstract_inverted_index.creates | 27 |
| abstract_inverted_index.current | 122 |
| abstract_inverted_index.derived | 136 |
| abstract_inverted_index.designs | 304 |
| abstract_inverted_index.employs | 196 |
| abstract_inverted_index.improve | 79, 301 |
| abstract_inverted_index.models. | 284 |
| abstract_inverted_index.network | 249 |
| abstract_inverted_index.packing | 105, 170, 229 |
| abstract_inverted_index.regard, | 131 |
| abstract_inverted_index.respect | 102 |
| abstract_inverted_index.results | 173, 252 |
| abstract_inverted_index.Dynamics | 20 |
| abstract_inverted_index.Learning | 23 |
| abstract_inverted_index.Rotating | 38 |
| abstract_inverted_index.accurate | 29 |
| abstract_inverted_index.achieved | 225 |
| abstract_inverted_index.analysis | 55, 183 |
| abstract_inverted_index.approach | 256 |
| abstract_inverted_index.detailed | 57 |
| abstract_inverted_index.existing | 302 |
| abstract_inverted_index.flowrate | 238 |
| abstract_inverted_index.minimize | 205 |
| abstract_inverted_index.obtained | 214 |
| abstract_inverted_index.pressure | 84, 165, 199, 212, 268 |
| abstract_inverted_index.proposed | 1, 255 |
| abstract_inverted_index.quantify | 34 |
| abstract_inverted_index.reliably | 293 |
| abstract_inverted_index.research | 2 |
| abstract_inverted_index.rotating | 87, 146, 240 |
| abstract_inverted_index.algorithm | 63, 75 |
| abstract_inverted_index.available | 119, 220 |
| abstract_inverted_index.combining | 17 |
| abstract_inverted_index.correlate | 243 |
| abstract_inverted_index.equations | 134, 140, 151 |
| abstract_inverted_index.framework | 30 |
| abstract_inverted_index.function) | 209 |
| abstract_inverted_index.intensify | 44 |
| abstract_inverted_index.modelling | 13 |
| abstract_inverted_index.necessary | 287 |
| abstract_inverted_index.numerical | 12 |
| abstract_inverted_index.operating | 96, 235, 275 |
| abstract_inverted_index.potential | 36 |
| abstract_inverted_index.reference | 147 |
| abstract_inverted_index.validated | 175 |
| abstract_inverted_index.analytical | 181 |
| abstract_inverted_index.artificial | 247 |
| abstract_inverted_index.commercial | 127 |
| abstract_inverted_index.conditions | 236, 276 |
| abstract_inverted_index.difference | 187 |
| abstract_inverted_index.modelling. | 73 |
| abstract_inverted_index.processes. | 48 |
| abstract_inverted_index.resistance | 67, 106, 230 |
| abstract_inverted_index.supplement | 59 |
| abstract_inverted_index.technology | 42 |
| abstract_inverted_index.approaches. | 24 |
| abstract_inverted_index.calibrating | 66 |
| abstract_inverted_index.conditions. | 97 |
| abstract_inverted_index.derivatives | 100 |
| abstract_inverted_index.differences | 167 |
| abstract_inverted_index.discretized | 153 |
| abstract_inverted_index.efficiently | 300 |
| abstract_inverted_index.improvement | 260 |
| abstract_inverted_index.innovation. | 310 |
| abstract_inverted_index.media-based | 263, 282 |
| abstract_inverted_index.participate | 306 |
| abstract_inverted_index.predictions | 81, 178, 265 |
| abstract_inverted_index.sensitivity | 54, 99, 132, 150, 164, 182, 198 |
| abstract_inverted_index.significant | 259 |
| abstract_inverted_index.simulations | 217 |
| abstract_inverted_index.coefficients | 68, 107, 231 |
| abstract_inverted_index.conventional | 45, 280 |
| abstract_inverted_index.demonstrated | 109 |
| abstract_inverted_index.derivatives, | 201 |
| abstract_inverted_index.differential | 133 |
| abstract_inverted_index.experimental | 221 |
| abstract_inverted_index.Computational | 18 |
| abstract_inverted_index.Navier-Stokes | 139 |
| abstract_inverted_index.approximation. | 188 |
| abstract_inverted_index.machine-learning | 61 |
| abstract_inverted_index.state-of-the-art | 9 |
| abstract_inverted_index.three-dimensional | 138 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.73160535 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |