Bibliographic record
Abstract
The mixing of fuel with air in the inlet of a shock-induced combustion ramjet (shcramjet) is presented. The study is limited to nonreacting hydrogen/air mixing in an external-compression inlet at a flight Mach number of 11 and at a dynamic pressure of 1400 psf (67,032 Pa), with use of an array of cantilevered ramp injectors. Results are obtained using the WARP code solving the Favre-averaged Navier‐Stokes equations closed by the Wilcox kω turbulence model and the Wilcox dilatational dissipation correction, discretized by the Yee‐Roe flux-limited scheme. Because of the fuel being injected at a very high speed, fuel injection in the inlet is found to increase the thrust potential considerably, with a gain exceeding the losses by 40‐120%. Losses due to skin friction are seen to play a significant role in the inlet, because they are estimated to make up as much as 50‐70% of the thrust potential losses. The use of a turbulence model that can predict the wall shear stress accurately is, hence, crucial in assessing the losses accurately in a shcramjet inlet. Substituting the second inlet shock by a Prandtl‐Meyer compression fan is encouraged because it decreases the thrust potential losses and reduces the risk of premature ignition by reducing the static temperature, while decreasing the mixing efficiency by a mere 6%. One approach that is observed to be successful at increasing the mixing efficiency in the inlet is alternating the injection angle along the injector array. The use of two injection angles of 9 and 16 deg is seen to result in a 32% increase in the mixing efficiency at the expense of a 14% increase in the losses when compared to a single injection angle of 10 deg. When alternating injection angles are used, the mixing efficiency reaches as much as 0.47 at the inlet exit.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".