Characteristic effectiveness curves for falling-film drain water heat recovery systems
Bibliographic record
Abstract
Falling-film drain water heat recovery systems have proven to be a cost-effective and reliable class of heat exchanger for reducing primary energy consumption in residential and commercial buildings and in industrial buildings and processes. It is fitting, therefore, that regulatory bodies are preparing standards by which various products can be characterized, both for rating purposes and to provide data for building energy simulation. Unfortunately, standards development is progressing in the absence of measured performance data that characterize how these heat exchangers perform. The intention of the current work is to examine drain water heat recovery performance at various equal-flow conditions. The effectiveness of three drain water heat recovery systems was examined in a counter flow as a function of volumetric flow rate. The drain water heat recovery systems represented products from two manufacturers and two lengths. One of the drain water heat recovery systems was also tested in parallel flow. While the performance characteristics generally mirrored theoretical performance, there were some key differences. For the units tested, there was a clear transition region occurring between flow rates of 5 and 10 L/min (1.3 and 2.6 gpm). While the presence of this region did not impact the proposed rating process, it could significantly impact the applicability of the data analysis to building simulation. It was also shown that the number of transfer units for the collector changed significantly with flow rate, but in a predictable manner. By fitting the number of transfer units versus flow rate data, and using this correlation in conjunction with theoretical ϵ–number of transfer units equations, the drain water heat recovery performance could be well predicted over the entire range of operation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".