Simulating Head Losses in Hydraulic Drop Structures in SWMM, with a Little Help from CFD
Bibliographic record
Abstract
The City of Portland, Oregon has cause to model instances of street flooding in the vicinity of hydraulic drop structures that feed into the Willamette combined sewer overflow tunnel system.Reproducing the events with a collection system model based on the U.S. EPA's Storm Water Management Model (SWMM) requires the representation of friction losses in the vortex generator structures.A modeling study was undertaken to better understand flows and losses in these structures, leading to enhancements to the City's collection system model to benefit the assessment of mitigation alternatives.A conceptual approach to representing the head losses in vortex structure generators within a SWMM-based modeling framework was developed, and the general applicability of the approach was tested through a series of computational fluid dynamics (CFD) model simulations covering a range of flow conditions.The CFD modeling demonstrated that the conceptual approach could represent head losses under the conditions of primary interest, which were fully submerged conditions with the potential to cause surface flooding.When the approach was implemented in SWMM, the observed flooding events were qualitatively reproduced.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".