A COMPUTATIONAL STUDY TO PREDICT THE COMBINED EFFECTS OF SURFACE ROUGHNESS AND HEAT FLUX CONDITIONS ON CONVERGING-NOZZLE FLOWS
Bibliographic record
Abstract
Critical design parameters on the performance prediction of converging nozzles are the geometric features and the operating conditions, which include the stagnant properties at the inlet, frictional and heat transfer behaviors on the nozzle wall; where the latter two are hard to handle together in compressible high-speed flows. This paper presents a recent computational model, that integrates the axisymmetric continuity, momentum and energy equations, to predict the combined effects of surface roughness and heat flux conditions on the flow and heat transfer characteristics of compressible flows through converging nozzles. To build a comprehensive overview, analyses are conducted at convergence half angles from 0° to 9° and inlet stagnation to back pressure ratios ranged from 1.01 to 2, covering both the un-choked and choked cases. Non-dimensional surface roughness and surface heat flux values are in the order of 0.0025-0.05 and 20-2000 kW/m2 respectively. The influences of the model parameters on the nozzle performance are discussed through the streamwise variations of Mach number, shear stress, discharge coefficient and Nusselt number; to verify the validity of the model comparisons are made with the numerical and experimental data available in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".