Determination of ultraviolet sensor location for sensor set-point monitoring using computational fluid dynamics
Bibliographic record
Abstract
A study was performed to investigate the use of computational fluid dynamics (CFD) coupled with a fluence rate model to determine ultraviolet (UV) sensor placement for a "set point" monitoring approach to UV reactor operation. Simulations were performed in a two-lamp closed conduit reactor using two fluence rate models: RADial line source integration (RAD-LSI) and multiple segment source summation (MSSS). In addition, simulations were performed assuming first order inactivation kinetics with two rate constants representing a high UV sensitive and a low UV sensitive microorganism. The optimal sensor location was determined by calculating the linearity of the reduction equivalent fluence (REF) as a function of the local fluence rate regardless of the UV transmissivity (UVT). Results showed that a small range of possible locations exist where the sensor can be placed to achieve a single REF for each fluence rate value. However, the predicted optimal location was a function of the selected fluence rate model and the target microorganism. The determination of the sensor location for sensor set point monitoring should be performed with the MSSS approach, which includes refraction, reflection, shadowing, and sensor characteristics. Moreover, simulations performed with the more resistant microorganism produced a narrower spatial range of optimal sensor locations than with the less resistant microorganism. Key words: model, CFD, UV disinfection, monitoring, fluence rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".