Bibliographic record
Abstract
This paper provides a state-of-the-art of computational fluid dynamics (CFD) in the building industry. Two methods were used to find this new knowledge: a series of interviews with leading architecture, engineering, and software professionals; and a series of tests in which CFD software was evaluated using comparable criteria. The paper reports findings in technology, workflows, projects, current unmet needs, and future directions. In buildings, airflow is fundamental for heating and cooling, as well as occupant comfort and productivity. Despite its importance, the design of airflow systems is outside the realm of much of architectural design practice; but with advances in digital tools, it is now possible for architects to integrate air flow into their building design workflows (Peters and Peters 2018). As Chen (2009) states, “In order to regulate the indoor air parameters, it is essential to have suitable tools to predict ventilation performance in buildings.” By enabling scientific data to be conveyed in a visual process that provides useful analytical information to designers (Hartog and Koutamanis 2000), computer performance simulations have opened up new territories for design “by introducing environments in which we can manipulate and observe” (Kaijima et al. 2013). Beyond comfort and productivity, in recent months it has emerged that air flow may also be a matter of life and death. With the current global pandemic of SARS-CoV-2, it is indoor environments where infections most often happen (Qian et al. 2020). To design architecture in a post-COVID-19 environment will require an in-depth understanding of how air flows through space.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".