A comparative study on best configuration for heat enhancement using nanofluid
Bibliographic record
Abstract
Nanofluid is a class of fluid which enhances the heat extraction from a hot surface. The concentration of nanoparticles enhances the conductivity of the fluid; however, it may change the fluid from Newtonian to a non-Newtonian. In addition, the type of base fluid plays a significant role in heat extraction. In this present study, three different configurations (porous block, porous straight channel and porous wavy channels) setups were investigated numerically using four different types of nanofluids mainly, 0.5% vol Al2O3/Water, 0.5% vol TiO2/Water, 0.5% vol Al2O3/Ethylene Glycol and 0.5% vol TiO2/Ethylene Glycol. Different parameters were assessed such as the local Nusselt number, the friction coefficient, the pressure drop, the temperature uniformity and the efficiency index. It was found that each nanofluid have a different performance for different configuration. If one relies on the efficiency index which combine the Nusselt number and the pressure drop, the nanofluid of 0.5% vol Al2O3 in water base provided the highest efficiency index.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".