Numerical and experimental investigation into the impact of cu/al2o3 hybrid nanofluid and higher concentration alumina nanofluids on heat transfer in a two and three-channel heat exchanger
Bibliographic record
Abstract
The following work was conducted both numerically and experimentally with two nanofluid types being investigated. The first being a Cu/Al2O3 2O3 hybrid nanofluid with an aluminum oxide nanostructure decorated in copper oxide nanostructures, and the latter being two higher concentration alumina nanofluids, 1% vol and 2% vol created through dilution of a stock fluid in distilled water. Both nanofluids were tested in a fluid flow system filled with an open-cell foam metal. The porous media is comprised of a 6061-T6 aluminum with a permeability of 9.54788× 10−7 m2 for the hybrid nanofluid and a permeability of 2.3869 x 10-7 m2 for the high concentration alumina nanofluid, with both porous media blocks used in the investigation having a porosity of 0.91. The experiments were conducted with varying heat flux. The performance of the nanofluids was evaluated by examining changes in the Nusselt number. The copper oxide/alumina nanocomposite in conjunction with the porous media, resulted in a significant enhancement of 6-11% compared to the commercially available alumina nanofluid, The high concentration alumina saw an average thermal enhancement of 15.6% of the 1% vol nanofluid over the 2% vol.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".