The Effect of Transient Characteristics on Optimization of Fixed-Bed Regenerators
Bibliographic record
Abstract
Abstract Fixed-bed regenerators (FBRs) have high sensible effectiveness, making them an energy-efficient air-to-air energy recovery exchanger (AAEE) to reduce energy consumption for ventilation in buildings. FBRs operate by alternately storing and releasing heat in fixed exchangers, which result in outlet temperature that varies with time during both heating and cooling periods. This variation in FBR's outlet temperature adds a new optimization variable that needs to be considered when designing FBRs. For example, in heating, ventilating, and air conditioning (HVAC) systems, careful design is required to prevent large variations in FBR’s outlet temperature (temperature swing (TS)), which might deteriorate occupant thermal comfort and introduce a variable load on the HVAC system. In this paper, a correlation for TS is developed as a function of FBR design parameters. FBRs optimization is performed considering TS as an additional objective to the traditional parameters of exchanger effectiveness, pressure drop, payback period (PBP), and mass. A selection procedure (decision-making procedure) is also integrated into the optimization process to select the optimized FBRs from Pareto fronts. The results show that when TS is included as an additional objective to the optimization and selection process, the selected optimized FBRs have higher mass and effectiveness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".