HVAC filters clogging detection using electrospun sensory membrane
Bibliographic record
Abstract
Abstract Clogged air filters increase pressure drop, thereby raising energy consumption in heating ventilating and air conditioning (HVAC) systems. Monitoring this pressure drop enables real-time detection of clogging. This study focuses on developing a thermoplastic polyurethane (TPU) membrane designed for strain sensing to be used as a pressure drop sensor in ventilation systems. TPU nanofiber membranes were electrospun into a honeycomb structure to minimize the pressure drop caused by the sensor itself, with a carbon sensor ink printed in a zigzag pattern to enable signal detection. Structured collectors with porous honeycomb patterns were used during electrospinning to create these honeycombed membranes. The effect of honeycomb hole size (0.5 cm and 1.5 cm, with corresponding membranes labeled TPU-0.5 and TPU-1.5) on electrical resistance and pressure drop was assessed in a lab-scale ventilation tunnel at various airflow velocities representative of those used in HVAC systems. The TPU-0.5 membrane showed higher sensitivity (ΔR/R up to 28%) but resulted in a significantly higher-pressure drop compared to the TPU-1.5 membrane (134.3 ± 7.6 Pa vs. 34.0 ± 2.4 Pa at 3.83 m s−1). Cyclic testing revealed resistance drift, indicating the need for stabilization cycles to ensure consistent results. The findings indicate that the TPU-1.5 membrane achieves a better balance between sensitivity (10%–15% resistance change at 3.83 m s−1 of velocity) and pressure drop (maximum 32–38 Pa at 3.83 m s−1 of velocity), making it more suitable for real-world applications in air handling units, particularly for monitoring filter clogging.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".