Pore-scale evaluation on hydrothermal performance in a microtube with homogeneous microporous media by lattice Boltzmann method
Bibliographic record
Abstract
• Heat transfer and flow behavior of a laminar nanofluid microporous flow. • 3D Lattice Boltzmann Method was employed. • Inserting spherical objects as microporous media. • Microporous structure improves the heat transfer performance in the microtubes. • Undesirable impacts with lower number of spheres hence the larger spheres size. This study investigates the heat transfer and flow behavior of Al 2 O 3 -water nanofluids in micro-scale systems, using the lattice Boltzmann method (LBM) for numerical simulations. The research focuses on a three-dimensional microtube (500 μm diameter, 6000 μm length) subjected to a uniform wall heat flux, with Reynolds numbers between 40 and 100. Spherical particles of varying sizes and amounts are inserted into the flow path to examine the impact of porosity on thermophysical properties. The study also explores the relatively unexplored application of the LBM for curved boundaries. Results show that introducing 6–10 spherical objects at Re = 40 increases average Nusselt numbers by 23.61 % and 25.83 %, respectively, while larger spheres in smaller quantities had minimal or negative effects on heat transfer. The lattice Boltzmann method is gaining popularity in fluid dynamics, but its application to curved boundaries remains limited. This study advances the field by investigating flow dynamics in a microtube with spherical inserts, incorporating curved boundaries, nanofluids, and porous structures, which offers valuable insights into thermophysical studies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".