Numerical Investigation Of The Impingement Of A Planar Jet of Nanofluids On A V-Shaped Plate
Bibliographic record
Abstract
An effective way to enhance the heat dissipation in industrial heat transfer devices is impinging of the fluid jet. Due to the higher dissipation heat flux, jet flows can be used for to control the temperature of high intensity heat sources. Traditional fluids such as water, ethylene and propylene glycol, and oils offer heat transfer capabilities that are adequate for many applications. There are several options to increase the effectiveness of the heat transfer characteristics for these fluids, for instance, using jet flows, and increasing the surface area of the heat transfer object. However, with the advances in nanotechnology and material science, nanofluids offer an attractive alternative option. Nanofluids refer to a dispersion of metallic or non-metallic particles with dimensions smaller than 100 nm in a base fluid like water, ethylene and propylene glycol, oil. Nanofluids have been shown to have an enhanced heat transfer characteristic, because of their high thermal conductivity. In this Project, Heat transfer enhancement of an impinging liquid jet on a V-shape target plate cooling system, has been investigated numerically, by replacing the base fluid, water, with Al2O3–water nanofluid. To conduct the research, literature review on nanofluid heat transfer enhancement, jet impingement, and nanofluids jet impingement, has been conducted. Numerical model has been built using ANSYS Workbench 16.0. After validating the numerical code with the previous experimental data, the effect of nanoparticles volume fraction, jet-surface distance and jet’s Reynolds number on the heat transfer enhancement has been investigated
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".