UV Reactor Performance Modeling by Eulerian and Lagrangian Methods
Bibliographic record
Abstract
A study was performed to investigate the influence of hydrodynamics on the performance of ultraviolet (UV) reactors. Two general UV disinfection models were developed by integrating fluence rate models and inactivation kinetics within a commercial computational fluid dynamics (CFD) software package to predict reactor performances. Both a particle tracking (Lagrangian) random walk model and a volumetric reaction rate based (Eulerian) model were implemented. Simulations were performed for two characteristic annular single-lamp UV reactor configurations, with inlets concentric (L-shape) and normal (U-shape) to the reactor axis. Two fluence rate models, the infinite line source assumption and the finite line or multiple point source summation (MPSS), were used. First-order inactivation kinetics was assumed for disinfection, with rate constants from MS2 bacteriophage assays. The simulation results provided detailed information on the velocity profiles, reaction rates, range of absorbed dose, and areas of short circuiting of the UV reactors. Model predictions based on both the Lagrangian dose distribution and Eulerian concentration distribution were in good agreement with each other at high flow rates but showed some discrepancies at lower flow rates. Experimental verification of the general models was performed by simulating the disinfection performance of an industrial prototype UV reactor. Results from both integration approaches were shown to be in good agreement with the provided biodosimetry data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".