Computational fluid dynamics simulation and parametric study of an open channel ultra-violet wastewater disinfection reactor
Bibliographic record
Abstract
The disinfection characteristics of an open channel ultra-violet (UV) disinfection reactor is investigated numerically. The computational fluid dynamics (CFD) model used in this study is based on the volume of fluid (VOF) method to capture the water–air interface. The Lagrangian particle tracking method is used to calculate the microbial particle trajectory and the discrete ordinate (DO) model is used to calculate the UV intensity field inside the reactor. A commercial CFD software package ANSYS FLUENT is used to solve the governing equations. Custom user defined functions (UDFs) are developed to calculate the UV doses. A post-processor is developed in MATLAB to implement the inactivation kinetics of the microbes. The post-processor provides the probabilistic dose distribution and reduction equivalent dose (RED) values achievable in the reactor. The numerical predictions are compared with available experimental data to validate the CFD model. A parametric study is performed to understand the effects of different parameters on disinfection performance of the reactor. The low/high dosed particle trajectories, which can provide an insight for hydraulic and optical characteristics of the reactor for possible design improvements, are identified.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".