Improved Heat Transfer in W-Baffled Air-Heat Exchangers with Upper-Inlet and Lower-Exit
Bibliographic record
Abstract
In the current study, this way was adopted numerically in order to optimize the performance of a HEC through the use of extended solid sections in the form of 'W' (W-baffles: WBs). All limit conditions of the channel have been defined, with all the thermo-physical properties of the HTF (heat transfer fluid) used. The FVM (Finite-Volume-Method) has been adopted with some necessary numerical schemes in order to give the numerical solution, which allows us to visualize dynamically the flow filed and to deduce all the energetic characteristics contained by this HE. Dynamically, the HTF flow velocity at the HEC outlet section reached about 1.812 m/s, in the case of the lowest Re value. While, it passed 4.8 m/s in the case of the largest value of the same variable, i.e. 1.726 to 4.648 times better than the Uin within the limits of Re numbers used. Thermally, areas with very hight TGs (temperature gradients) were observed near the top deflector’s sides, which reflects the effect of the W-baffles. This highlights the importance of the adopted obstacles in changing characteristics of the HEC to the best.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".