Thermal Performance of Conventional-Metal and Novel-Plastic Drain Water Heat Recovery Device
Bibliographic record
Abstract
Drain water heat recovery (DWHR) systems are effective for reducing energy consumption by capturing waste heat from drain water and using it to preheat the primary water supply in buildings. However, traditional DWHR devices, with copper helical tubes wrapped around a central pipe, can be expensive for residential use, deterring homeowners. This study introduces an innovative 3D-printed DWHR design using Nylon PA material. This design eliminates contact and conduction resistances of helical tubes, resulting in reduced thermal resistance, weight, manufacturing cost, and improved performance. Experimental testing compares the new plastic DWHR device with the conventional metal one under steady and transient conditions. In addition, detailed heat transfer models and thermal networks are given for both devices. The plastic device consistently outperforms the metal one, achieving higher effectiveness within the first few minutes of operation across various water flow rates. This advantage is particularly beneficial for short-term drain water usage like sinks and showers. Additionally, the novel DWHR device maintains a 16–20% higher steady-state effectiveness at high flow rates. This innovative approach promises cost-effective and efficient heat recovery, making DWHR technology more accessible for residential and commercial applications.
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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) | 0.002 | 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".