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Record W7116112896 · doi:10.82417/599g-ss41

Passive ventilation via origami-driven stack effect in cold regions

2025· other· en· W7116112896 on OpenAlexaboutno aff

Bibliographic record

VenueEspace ÉTS (ETS) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsStack effectChimney (locomotive)Stack (abstract data type)Ventilation (architecture)Duct (anatomy)AirflowSolar chimney

Abstract

fetched live from OpenAlex

Ventilation in cold regions presents significant challenges for achieving sustainable, low-energy building and infrastructure systems, often requiring energy-intensive solutions. In underground mining operations across Northern Canada, ventilation can constitute up to 50% of operating expenditures (OPEX). Similarly, remote communities seek low-cost, energy-efficient systems. This study introduces a novel passive ventilation concept driven by the stack effect and enhanced by origami-inspired structures. The proposed system utilizes a deployable extension made of stacked Kresling elements (SKE) retrofitted atop chimneys or exhaust ducts with little structural modification. These elements can expand or contract on demand, thereby modulating chimney height and controlling airflow rates with minimal energy input.To evaluate system performance, we developed full-scale three-dimensional computational models based on the Reynolds-Averaged Navier-Stokes (RANS) equations, where buoyancy is governed by temperature-dependent air density (ideal gas model). The geometric intricacy of the stacked Kresling elements is fully integrated into the model to capture their effect on airflow dynamics. In parallel, an analytical model employing the first law of thermodynamics is derived to estimate ventilation rates as a function of indoor-outdoor temperature differentials. Various geometric configurations are explored, with a focus on the number of panels and the aspect ratio of individual elements.Results show that integrating SKEs can significantly enhance and regulate ventilation. In cold-region scenarios with temperature differences exceeding 20?°C, doubling or tripling the duct height via SKEs leads to ventilation rate increases of approximately 30% and 50%, respectively. Owing to their tunable geometry, SKEs also offer high adaptability: a configuration with six panels and an aspect ratio of 0.75 can achieve a fivefold variation in ventilation rate between its fully expanded and contracted states. From a fluid dynamic perspective, increasing the element aspect ratio or number of panels reduces viscous forces in the airflow. In these low-friction scenarios, the ventilation rate can be reasonably approximated using the analytical model, particularly under low temperature differences.Overall, this work demonstrates the potential of adaptive origami structures to passively regulate ventilation in cold climates, offering advantages for both mining and residential applications. Future research will focus on optimizing SKE geometry to minimize flow resistance and integrating the system with passive heat recovery ventilation—such as heat pipes—for combined thermal and airflow control.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.264
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2025
Admission routes1
Has abstractyes

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