A novel CFD-experimental analysis for enhancing air distribution and indoor air quality in existing buildings using high induction diffusers
Bibliographic record
Abstract
Between 2000 and 2021, Canada’s energy use rose by 10%, though efficiency gains prevented a 21% increase. Space heating and cooling dominate consumption, making up 63% in homes and 61% in commercial buildings. Ceiling-based ventilation worsens thermal stratification, raising energy use and lowering indoor air quality. This study explores high-induction diffusers for better ventilation without increasing heating and cooling loads. A combined computational fluid dynamic (CFD) and experimental approach was employed to evaluate the ventilation effectiveness of high-induction diffusers in comparison to conventional diffuser designs. The study utilized ASHRAE Standards 62.1 and 129 to quantify ventilation effectiveness (Ez), while evaluating the local mean age of air. Experimental measurements were conducted at the Indoor Environment Research Facility (IERF), assessing air distribution performance under controlled conditions using tracer gas decay methods with sulfur hexafluoride (SF6). Results indicate that high-induction diffusers significantly improve air mixing, reducing thermal stratification and localized discomfort. Enhanced entrainment leads to a higher Ez value, thereby optimizing airflow distribution and mitigating the necessity for supplementary heating or cooling devices. In retrofitted systems, increased Ez improves IAQ without increasing ventilation rates, whereas in new HVAC designs, it enables reductions in outdoor air requirements, minimizing system oversizing and reducing energy consumption. Findings suggest that integrating high-induction diffusers in ventilation systems can enhance occupant comfort, lower HVAC operational costs, and support energy conservation efforts up to 25%. This study contributes to advancing HVAC design by demonstrating that optimizing air diffusion strategies can achieve both energy efficiency and improved thermal comfort in commercial and residential buildings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".