Numerical Investigation of Methane and Air Mixing in a Shcramjet Inlet
Bibliographic record
Abstract
The performance of gaseous methane-air mixing in the inlet of a shock-induced combustion ramjet is investigated in this paper. The two oblique shocks mixed-compression inlet configuration is selected to ensure the flow travels in a parallel direction to the internal mixin g duct. Cantilevered ramp injectors, designed to deliver rapid mixing in a high enthalpy flow, are incorporate d and strategically positioned at the external ramp. The inlet is designed to enable vehicles to travel at the Mach 8 flight speed and follow the 67032Pa flight dynamic pressure path at 28.6km altitude. The objective of this study is to evaluate the impact of inlet geometrical parameters, fuel injection properties, and injector dimensions on the mixing efficiency. The study also measures the inlet performance of other fuels commonly used for hypersonic vehicles such as kerosene and hydrogen. The analysis of three-dimensional steady state flowfields is undertaken numerically via the Window Allocatable Resolver for Propulsion (WARP) code. WARP solves the multispecies Favre-averaged Navier-Stokes equations, which are closed by the Wilcox k − ω turbulence model. The numerical results indicate that an air-based mixing efficiency of up to 0.85 can be achieved with a low risk of premature ignition. Both inlet wedge angle and fuel tank stagnation temperature are identified as having a significant influence on inlet performance. The applicability of the cantilevered ramp injector to the Mach 8 hydrocarbon inlet is validated and justified. Lastly, it is concluded that methan e delivers the best mixing performance among the 3 fuels, and the mixing efficiencies of the kerosene and hydrog en inlets are found to be comparable despite the difference in their molecular weights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".