Solutal separation in a binary nanofluid due to thermodiffusion
Bibliographic record
Abstract
Transport phenomena in porous media have received considerable attention due to an increasing interest in geothermal processes, chemical catalytic reactors, waste storage (especially geological or ocean storage of carbon dioxide), etc. Among others, oil industry has shown an increasing interest in studying diffusion phenomenon. Nanofluid is a term used to describe the suspension of low concentration of metallic and non-metallic nanoparticles in a base fluid. The size of a nanoparticle ranges from 10 to 100nm, and the conventional fluids used are water, ethylene glycol (C2H6O2) or engine oil. Various studies have proven that nanoparticles improve the heat transfer of a base fluid. However, using various nanofluids it has been shown that the results could vary depending on different initial concentrations. The main objective of this paper is to study the diffusion and the thermodiffusion effect in a nanofluid for different fluid/porous media configurations. In this configuration, a liquid layer surrounds a porous layer. The full Brinkman equation coupled with the heat and mass transfer equations have been solved numerically for the porous layer using the finite element technique. The full Navier stokes equation coupled with heat and mass transfer equations have been solved for the liquid layer using the finite element method. A constraint between the liquid and porous layer has been applied to ensure heat flow and mass transfer continuity is maintained. A square cavity filled with hydrocarbon nanofluid of a mixture of fullerene-toluene with varying concentration of fullerene has been subject to different heating conditions. The entire cavity has been considered to be fully wetted with nanofluid. Results have confirmed that in the presence of a nanofluid a heat transfer enhancement is present up to certain initial concentration of the fullerene. The heat convection coefficient has been found to be 16% higher when a nanofluid is used as the working fluid.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".