A coupled CFD-evaporation model to estimate drying rates across laminate-lined sanitation systems
Bibliographic record
Abstract
ABSTRACT Decentralized sanitation systems such as eco-vapor toilets (EVTs) use hydrophobic laminate-lined containers to collect and dry fecal matter. Removing water in EVTs is critical to their success. While computational fluid dynamics (CFD) modeling of drying can guide EVT designs and site selection, it is expensive. To develop a simpler modeling approach, bench-scale experiments quantified the constant-rate drying period over a wide range of environmental conditions using deionized water as a surrogate for fecal sludge. Then, two modeling approaches were tested using these data: a CFD model, and a simple evapotranspiration model, the Food and Agriculture Organization of the United Nations (FAO) - Penman Monteith (PM) model. The model-predicted drying rates for 29 experiments resulted in a coefficient of efficiency (E, optimal value = 1.0) of 0.47 and −0.05 for the CFD and FAO-PM models, respectively. To reduce the level of effort for a complete CFD model but increase the accuracy of a FAO-PM model alone, a new CFD-Evaporation model is proposed. The FAO-PM and the CFD-Evaporation model applied to 40 L EVTs in an 11-day field test resulted in an E of −1.38 and 0.15, respectively. The CFD-Evaporation model is useful where computational efficiency is more critical than absolute accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".