Computational fluid dynamics simulation of pressure drop and macromixing in LL microreactors
Bibliographic record
Abstract
Abstract A computational fluid dynamics (CFD) model was developed to simulate single‐phase flow in LL microreactors using the open‐source software OpenFOAM. The k‐ω SSTLM turbulence model was implemented to account for the impact of small‐scale temporal and spatial fluctuations that emerge in the base LL mixing module (LLM) on the flow field and transport of a passive scalar. The CFD model was successfully validated based on excellent conformance to experimental pressure loss ( R 2 > 0.997) and residence time distribution (RTD) data ( R 2 > 0.97) at flow rates ranging from 10‐100 g/min. Streamlines, velocity, pressure, and turbulent viscosity profiles were mapped across the 3D domain of repeating reactor segments to analyze local fluid dynamics. In a continuous series of LLMs, the flow field becomes fully developed after the second module. A drastic change in flow behaviour in the LLM was identified at 30 g/min ( Re = 643) based on the emergence of advective recirculation zones and significant turbulent dispersion. Recirculation zones in the LLM grow larger from 20‐50 g/min and are equal in size from 50‐100 g/min. Four LL reactor plates with differing configurations of spacing between LLMs were compared extensively by analyzing pressure drop and RTD. Plates with continuous or equally‐spaced LLMs demonstrated a near plug‐flow profile with minor dispersion ( Pe > 100), albeit at a greater power dissipation. Plates with longer residence‐time channels between LLMs yielded a broader and right‐skewed RTD due to the intermittent attenuation of chaotic flow patterns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".