Modelling and simulation of trickle‐bed reactors using computational fluid dynamics: A state‐of‐the‐art review
Bibliographic record
Abstract
Abstract Trickle‐bed reactors (TBRs), which accommodate the flow of gas and liquid phases through packed beds of catalysts, host a variety of gas–liquid–solid catalytic reactions, particularly in the petroleum/petrochemical industry. The multiphase flow hydrodynamics in TBRs are complex and directly affect the overall reactor performance in terms of reactant conversion and product yield and selectivity. Non‐ideal flow behaviours, such as flow maldistribution, channelling or partial catalyst wetting may significantly reduce the effectiveness of the reactor. However, conventional TBR modelling approaches cannot properly account for these non‐ideal behaviours owing to the complex coupling between fluid dynamics and chemical kinetics. Recent advances in the application of computational fluid dynamics (CFD) to three‐phase TBR systems have shown promise of achieving a deeper understanding of the interactions between multiphase fluid dynamics and chemical reactions. This study is intended to give a state‐of‐the‐art overview of the progress achieved in the field of CFD simulation of TBRs over the past two decades. The fundamental modelling framework of multiphase flow in TBRs, advances in important constitutive models, and the application of CFD models are discussed in detail. Directions for future research are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".