Laboratory performance of new and used residential HVAC filters: Comparison to field results (RP-1649)
Bibliographic record
Abstract
Particle filters are used in heating, ventilating, and air-conditioning (HVAC) systems to protect equipment and reduce exposure to airborne particles. Filtration standards such as ANSI/ASHRAE Standard 52.2 are used to evaluate filter performance in a laboratory setting. In this work, we examined the lab-tested performance of new filters with different nominal efficiencies as determined by ASHRAE Standard 52.2 and compared these results to the lab-tested and in-situ performance of filters deployed in 21 occupied residential environments. The lab-tested results comparison shows that the dust loading and conditioning procedure in ASHRAE Standard 52.2 provides a reasonable range of efficiencies for the used filters, but the target final pressure drop of 250 Pa is an overestimation of the realistic pressure drops. Moreover, the specified test dust was not a good representation of the dust in this sample of residential environments. The lab-tested and in-situ results comparison suggests that even for the same filter, its lab-tested performance could differ greatly from its in-situ performance because of variations in system and loading conditions, which are not captured in the laboratory setting. Overall, the lab-tested results are an overestimation of the in-situ efficiency and an underestimation of the in-situ pressure drop for both new and used filters.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".