The Performance of the hairpin type U Shape Double pipe heat exchanger type under effect of using Passive and Active Techniques.
Bibliographic record
Abstract
The process of improving and developing heat exchanger performance has received a lot of attention, and efforts are still being made by specialized researchers and engineers with a huge investigations to increase rate of heat transfer to lessen the volume size and price cost of the factories apparatus accordingly. In this experimental study, a suitable heat exchanger equipped with flow meters and thermocouples for measuring flow rates and temperatures was used with the U shape hairpin type exchanger. The bending and angle of curvature of the tubes causes vortex flow, which greatly aids to attractive the rate of heat transfer process and increase the performance, The effect of active and passive techniques with different positions of the U shape Exchanger like position (U shape and Inverse U shape ) as parallel coupling with tube liquid in series is investigated during this study. passive technique represented using the O ring fin type. and an active technique represented by the injection of an air bubble by a small compressor through a special air diffuser. The results show that the best application was with inverse U shape (∩) and the performance enhanced about (19.1%) in the case of active techniques while and (11.1%) with passive techniques and by applying both techniques together, the overall enhancement was (30.272%), So this study provides new visions for further studies.
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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".