Experimental investigation for enhancement of heat transfer and friction factor through using V-pattern ribs along with semi-circular perforated manner
Bibliographic record
Abstract
• V-pattern ribs with semi-circular perforations were used to enhance heat transfer. • Nusselt number ( Nu ) and friction factor ( f ) showed significant improvement. • Nu increased by 11.44 %, and f decreased by 5.24% vs non-perforated ribs. • The highest thermal performance factor ( TPF ) of 2.53 was achieved. • Correlation equations predict nu and f with errors under 8.89 % and 4.46 %. The installation of ribs to alter fluid flow patterns and produce swirling flow, which disrupts the thermal boundary layer, is a well-established method for enhancing heat transfer in solar air heaters. However, this approach often leads to pressure loss in the flow channel and heat accumulation at the rib base. To address these issues, perforated ribs have been introduced. This research experimentally investigates the heat transfer enhancement of V-pattern ribs, where the base of the ribs was perforated with semi-circular holes of varying sizes and numbers. The Nusselt number ( Nu ), friction factor ( f ) and thermal efficiency ( η eff ) were calculated, and the optimization of the rib configurations was evaluated using the thermal performance factor ( TPF ). In the experiment, air was used as the working fluid in a fully turbulent regime, with a constant irradiation flux provided by a solar simulator. The results demonstrated that semi-circular hole perforations at the rib base effectively reduced pressure drops in the flow channel by allowing smoother fluid flow through the perforations. Simultaneously, heat transfer was enhanced due to flow separation, reattachment, swirling, and jet formation, which disrupted the thermal boundary layer. Compared to non-perforated V-pattern ribs, Nu and η eff increased by 11.44 % and 15.06 %, respectively, f decreased by 5.24 %, and TPF rose by 15.89 %, achieving a maximum value of 2.53. Furthermore, the developed correlation equations showed strong agreement with the experimental data, accurately predicting both Nu and f .
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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) | 0.000 | 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".