Computational fluid dynamics for predicting performance of ultraviolet disinfection sensitivity to particle tracking inputs
Bibliographic record
Abstract
A three-dimensional (3-D) computational fluid dynamic model that predicts the performance of a full-scale medium-pressure lamp ultraviolet (UV) reactor for disinfection of drinking water is described. The model integrates velocity field, fluence rate distribution, and particle trajectory calculations with a microorganism inactivation kinetic model to arrive at predictions of reduction equivalent dose and microorganism inactivation for MS2 coliphage. A rational approach to determining an appropriate number of fluid particles that would generate the required computational precision is presented. Predictions of inactivation and equivalent dose were found to be sensitive to computational mesh geometry (hexahedral versus tetrahedral) but were less sensitive to the value of the Lagrangian empirical constant used in the random walk model and to choice of turbulence model (κ – εε versus Reynolds stress). Non-steady-state (dynamic) simulations produced results that were similar to those of steady-state simulations. Utility of the model for evaluating different lamp operating modes and alternative physical arrangements of the baffles and lamps was demonstrated.Key words: ultraviolet, UV reactor, disinfection, water, computational fluid dynamics, modeling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".