HEAT TRANSFER, ENERGY, AND EXERGY EFFICIENCY ENHANCEMENT OF NANODIAMOND/WATER NANOFLUIDS CIRCULATE IN A FLAT PLATE SOLAR COLLECTOR
Bibliographic record
Abstract
The thermodynamic relations of exergy efficiency, exergy destruction, thermal and friction entropy generation, Bejan number, and collector efficiency was evaluated experimentally by considering water-based nanodiamond (ND) nanofluids circulating in a flat plate collector (FPC) at different particle loadings (φ = 0.2% to φ = 1.0%) and Reynolds number (5000-13,000). Additionally, heat transfer, pumping power, and friction factor was also evaluated. Thermophysical properties were measured experimentally and developed regression correlation models to obtain the thermal conductivity, viscosity, specific heat, and density of nanofluids. Experiments indicate that the collector thermal efficiency for water is 53%; however, it is increased to 74% for 1.0% volume concentration of ND/water nanofluid in the FPC. The exergy efficiency is increased to 7.21%; exergy destruction and thermal entropy generation is decreased to 5.14% and 5.81%, and the frictional entropy generation is increased to 23% at 1.0% particle loading and Reynolds number of 10,098.1, against the water data. The Nusselt number is enhanced to 32.31% at 1.0% vol. concentration of nanofluid at Reynolds number of 10,098.1, with friction factor penalty of 26.77% compared to water. Furthermore, collector cost, energy, and environmental analyses are also performed for water and ND/water nanofluids. Relevant regression equations are proposed to evaluate the Nusselt number and friction factor.
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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) | 0.000 | 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 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".