Numerical Study of Supersonic Film Cooling in Diverging Section of Nuclear Rocket Laval Nozzle
Bibliographic record
Abstract
Nuclear rockets with specific impulse have obvious advantages by greatly reducing the mass of the propellant and potentially decreasing the cost of launching material from the earth’s surface. Nuclear thermal rockets use hydrogen propellant with coolant exit temperature of near 3000 K, which is very high, so the cooling of airframe surfaces in the vicinity of the exhaust is needed, of which film cooling is an effective method. Most of previous studies mainly focus on the film cooling effectiveness using two dimensional backward-facing step model, however, the nuclear rocket exhaust using the converging-diverging Laval nozzle, so the film cooling would be different. The present study numerically investigated the influence of coolant Mach number, coolant inlet height on supersonic film cooling in the diverging section of Laval nozzle, while keeping the coolant mass flow rate constant, with the results showing that: increasing the coolant inlet Mach number and the coolant inlet height can increase the film cooling effectiveness; for the same coolant mass flow rate, reducing the coolant inlet height and increasing the inlet Mach number improves film cooling effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".