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Record W40812983

Airflow Prediction in Buildings for Natural Ventilation Design: Wind Tunnel Measurements and Simulation

2008· dissertation· en· W40812983 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2008
Typedissertation
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
FundersNatural Resources CanadaConcordia University
KeywordsNatural ventilationWind tunnelAirflowVentilation (architecture)InflowParticle image velocimetryEngineeringThermal comfortMarine engineeringComputational fluid dynamicsParametric statisticsSimulationEnvironmental scienceMeteorologyMechanical engineeringAerospace engineeringTurbulence
DOInot available

Abstract

fetched live from OpenAlex

Natural/hybrid ventilation systems with motorized operable windows, designed and controlled to utilize the potential for cross-ventilation, represent an area of significant interest in sustainable building design as they can substantially reduce energy consumption for cooling and ventilation. Presently, there is a need for accurate prediction models that can contribute to the improvement of indoor environmental quality and energy performance of buildings, and the increased use of low energy, naturally driven cooling systems. In this regard, the present research aims to enhance airflow prediction accuracy for natural ventilation design of buildings considering advanced experimental and simulation methods. The study considers a Boundary Layer Wind Tunnel (BLWT) approach to investigate the wind-induced driving forces and ventilation flow rates in various building models subject to cross-ventilation. The Particle Image Velocimetry (PIV) technique was used for the first time to evaluate accurately the air velocity field for various cross-ventilation configurations. Detailed measurements were performed to determine mean and fluctuating internal pressures since they affect airflow prediction, occupants' thermal comfort, as well as cladding and structural wind load design of buildings with operable windows. PIV data for the inflow velocity were compared with those by using conventional techniques (e.g., hot-film anemometry) and results show differences, between the two methods, up to a factor of 2.7. This clearly indicates that accuracy can be enhanced with carefully conducted PIV experiments. The study provides guidelines for implementation of cross-ventilation in design practice. These guidelines were developed on the basis of parametric experimental investigations, which quantify the impact of relative inlet-to-outlet size and location on ventilation airflow rates and thermal comfort of building occupants. The study develops a novel simulation methodology combined with a sensitivity analysis focused on modelling issues, such as the impact of zoning assumptions, to predict the envelope pressures and related air-exchange rates in buildings due to wind, stack, and mechanical system effects. An integrated simulation tool (ESP-r) was used to model the airflow/energy interactions in an existing high-rise residential building, and simulation results agree well with monitoring data.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.277
Teacher spread0.233 · 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