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Record W2889727308 · doi:10.2495/dne-v13-n3-294-306

Adaptive design: water curtains for wayout finding in hub spaces

2018· article· en· W2889727308 on OpenAlexvenueno aff
F.A. Ponziani, A. Tinaburri, V. Ricci

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectural engineeringComputer scienceEngineeringCivil engineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.265
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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