Illinois Transient Model: Simulating the Flow Dynamics in Combined Storm Sewer Systems
Bibliographic record
Abstract
This chapter describes the capabilities and features of the recently developed Illinois transient model (ITM) for simulating the flow dynamics (transient and non-transient conditions) in combined storm sewer systems, ranging from dry bed flows, to gravity flows, to partly gravity-partly surcharged flows (mixed flows), to fully pressurized flows (water hammer flows).ITM, which was originally developed at the University of Illinois at Urbana-Champaign, is a finite volume (FV) model that can handle complex boundary conditions such as drop shafts, reservoirs, closing and opening of gates as a function of time, and junctions with any number of connecting pipes and any types of horizontal and vertical alignment.ITM is open source code that is in constant development and its releases are made available on a regular basis.In the current version of ITM (v.1.3, September 2010), the free surface region is modeled using the one dimensional (1-D) Saint-Venant equations.The pressurized region is modeled using the 1-D compressible water hammer equations.Open channel-pressurized flow (mixed flow) interfaces are modeled by enforcing mass, momentum and energy relations across the interfaces together with Riemann solvers at the sides of mixed flow interfaces.This version of ITM is referred to as the two equation model.The current version of ITM is superior to other models of its kind because it is robust, can simulate mixed flows (simultaneous occurrence of free surface and pressurized flows) when using actual pressure wave celerities (~1 000 m/s), and because no Preissmann slot assumption is made to simulate pressurized flows (water hammer flows).ITM has been applied to
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".