Integerated optimization and advanced thermal assesment of nanofluid-assisted and dimple-enhanced shell-and-tube heat exchangers
Bibliographic record
Abstract
This study investigates the combined impact of CuO-water nanofluids and teardrop-shaped dimples on the hydrodynamic and thermal performance of shell-and-tube heat exchangers (STHEs). Three STHE configurations, differing mainly in their length and number of baffles, are analyzed using a segment-by-segment approach within the P-NTU framework. Water is used as the base fluid on both tube and shell sides to accommodate nanoparticle suspensions of 0% mass fraction, 1% mass fraction, and 2% mass fraction CuO. The results highlight that a 1 wt% concentration strikes an optimal balance, enhancing thermal conductivity up to 24% while minimizing pumping-power penalties. Incorporating teardrop dimples on the tube surface significantly increases heat transfer, about 116%, but also results in higher tube-side pressure loss. However, combining dimples with nanoparticles yields only marginal additional improvements beyond those provided by dimples alone. Performance evaluation criteria ( PEC ) are employed to weigh thermal gains against aerodynamic losses. Moreover, while enlarging the heat exchanger by increasing its length and number of baffles may improve the thermal performance, it leads to a considerable rise in weight and size, which is often a critical feature in aerospace and other weight-sensitive applications. Overall, the study provides practical insights for optimizing STHE design by implementing an enhanced balance between heat transfer improvement and fluid flow penalties.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".