Performance Evaluation of a Two-Phase Closed Thermosyphon Having a Converging-Diverging Pipe Body
Bibliographic record
Abstract
The present study focuses on improving the heat transfer performance of a two-phase closed thermosyphon (TPCT) by altering the geometry of a typical straight pipe thermosyphon body to have a converging-diverging section.The flow path of the liquid-vapor fluid mixture is augmented with this new design to induce thermal boundary layer mixing thereby enhancing the convection heat transfer within the system during operation.A multiphase numerical simulation model has been developed to simulate the fluid phase change and wall temperature distribution of a two-phase closed thermosyphon.The Lee model is used to calculate mass transfer source terms during the condensation and evaporation phase change processes and the Volume of Fluid (VOF) method is employed to track liquid-vapor interface movement during the simulations.Wall temperature distributions as well as overall thermal resistance values are compared with experimental values available in the literature in order to validate the simulation model for a straight pipe geometry.Two additional model geometries are then used for comparative study where the converging-diverging (CD) section is positioned within the adiabatic section and condenser section respectively.The numerically simulated wall temperature distribution and overall thermal resistance results indicate that the most significant impact can be made when the CD section is positioned in the adiabatic section and condenser section exhibiting reduction of 1.7% and 3.4%, respectively, in overall thermal resistance.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".