Methodologies for Predicting the Effectiveness of Full-Scale Fixed-Bed Regenerators From Small-Scale Test Data
Bibliographic record
Abstract
Abstract Fixed-bed regenerators (FBRs) are air-to-air energy exchangers (AAEEs) used to reduce energy consumption in heating, ventilation, and air conditioning (HVAC) systems. Since energy savings are directly related to the effectiveness of FBRs, testing is essential to determine the effectiveness of FBRs for quality assurances and during product development. However, testing of full-scale FBRs has disadvantages such as requiring full-scale prototypes, a high volume of conditioned airflow, long tests, and large testing laboratories. The disadvantages are especially crucial during product development and can be overcome by small-scale testing provided the test data can be used to evaluate accurately full-scale FBRs. The major contribution of this paper is two new methodologies (one direct method and one predictive method) to determine the sensible effectiveness of full-scale FBRs from small-scale test data. In the direct method, the effectiveness of the full-scale FBR is determined directly from the small-scale test data, whereas in the predictive method the effectiveness is determined using the Wilson plot technique and a numerical model in addition to the small-scale test data. Both methods are shown to have uncertainties within the specified uncertainty limits required by testing standards and are applied to evaluate the influence of geometrical parameters (corrugation angle and corrugation depth) on the effectiveness of FBRs. The test methods and results will be useful in the design and development of FBRs for HVAC applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".