Modelling the Steady-State Performance of Coiled Falling-Film Drain Water Heat Recovery Systems Using Rated Data
Bibliographic record
Abstract
Falling-film drain water heat recovery (DWHR) systems are heat exchangers used to recover energy from warm water travelling down vertical drainpipes. DWHR systems are rated at constant inlet temperatures at multiple flow rates while maintaining an equal flow rate through both sides of the heat exchanger. The outcome of the rating system is an effectiveness value that is the main metric used to sell DWHR systems to the public. Unfortunately, the rated conditions may not be representative of what occurs during operation in a typical house. The present work aims to bridge this gap by presenting several semi-empirical correlations that are combined into a model capable of predicting the steady-state performance of a DWHR system at variable temperatures and flow rates, based on data generated during the rating process. This model is then validated experimentally for eight different DWHR systems for a total of 135 validation cases. The results show that the model is very effective at estimating the performance of DWHR systems for validation cases, and the mean absolute percentage error for the model predictions versus the experimental results is less than 3%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".