Adaptive design: water curtains for wayout finding in hub spaces
Bibliographic record
Abstract
The aim of this study is to explore some features of a complex system arising from the interactions of a fire stream in a hub space layout, with fire protection through water curtains issued by edge nozzles activated by smoke detectors.The hub layout represents the landside part of an airport terminal, made of clusters of semi-enclosed isles open to the inter-connected enclosed spaces that form a series of longitudinal paths with services and utilities.Once a fire source emits matter (smoke) and energy (enthalpy) out of one of the isle, in the absence of any fire protection barrier, the stream wanders following its buoyancy and the boundary conditions filling the available spaces inside the hub, making the occupants' conditions untenable.The design of water curtains that are activated in response to the fire onset may help to limit the dangerous spread of the fire stream and to support in the unfolding of protected paths for egress.While the activation of the water curtains in the proximity of the fire source once a threshold value is reached is the classical approach, a different design strategy is here investigated with CFD modelling based on a sequence of adaptive responses of the hub layout to the fire stream.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".