Investigation of flow and thermal performance of water-based nanofluid in gravity heat pipe
Bibliographic record
Abstract
• Performance of gravity heat pipe using various aqueous nanofluids is compared. • GO nanofluid reduces thermal resistance by 5.2 % and enhance startup speed. • Optimum heating power maximizes convective heat transfer coefficient. • Suitable nanofluid enable stable heat-flow fields, especially for higher power. This study presents a numerical investigation into the flow and thermal performance of gravity heat pipes, employing the Volume of Fluid (VOF) multiphase flow model in conjunction with a User-Defined Function (UDF) to simulate evaporation and condensation processes. The results demonstrate that the graphene oxide nanofluid heat pipe achieves a 33 % faster startup, forming a stable condensate film in 2 s compared to 3 s for pure water, and exhibits superior thermal performance. When the heating power increases from 10 W to 40 W, the total thermal resistance decreases by approximately 42 % for the pure water heat pipe, but only by 5.2 % for the graphene oxide (GO) nanofluid pipe, indicating its more stable performance across a power range. At 40 W, the GO nanofluid (0.2 wt%) enhances the equivalent convective heat transfer coefficient by 5 % and reduces the total thermal resistance by up to 5.2 % compared to pure water. Most notably, at the optimal power of 50 W, the GO nanofluid achieves a maximum reduction in thermal resistance of 7.8 % and an enhancement in the convective heat transfer coefficient of 4.5 %, while maintaining a more stable flow field, thereby extending the operational limit beyond 50 W.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".