Convective Heat Transfer in a Three-Dimensional Tubular Exchanger Filled with Pure/Hybrid Water-Based Nanofluid and Exposed to the Magnetic Field Effects
Bibliographic record
Abstract
The aim of this study is to evaluate, numerically, the effect of different nanoparticle volume fractions, on a heat transfer in a tubular heat exchanger.The main objective is to control this process under the effect of a magnetic field.The nanoparticles used for this analysis include water-based pure (alumina: Al2O3 and copper Cu) and hybrid (Al2O3-Cu) nanoparticles.This work is considered for laminar and stationary conditions in co-current mode flow.The computational analysis is performed under the CFD/Fluent code.The magnetic induction used is around [0.1 to 0.6] Tesla, and is applied in conjunction with the exchanger axis.The comparative study shows that copper nanofluids have a significant effect on heat transfer compared with alumina because: in co-current mode and for B=0T, Re=50 and =1% volume fraction of nanoparticles, the efficiency of copper reached 88.25%, while alumina was 87.12%.In addition, the heat transfer coefficient and the friction factor can be controlled by the magnetic field because curves h(B) and Cf(B) show, under the growth of a magnetic field B, the heat transfer coefficient increases autonomously, while the friction coefficient decreases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".