Analysis and Optimisation of an Ejector Ramjet using CFD and a 1D Control Volume Solver
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3302.vid Analysis and optimisation of an ejector ramjet using computational fluid dynamics and a quasi-one-dimensional control volume solver are presented. Results of the quasi one-dimensional solver (with combustion modelled using volume heat sources) are compared to experimental results. The quasi-one-dimensional solver is able to capture the primary physics involved in an ejector ramjet with reasonable accuracy while maintaining a short computation time. The quasi-one-dimensional solver was then paired with a non-dominant sorting genetic algorithm to perform a multi-objective optimisation to maximise the engine thrust and ISP with a fixed combustor diameter of 0.254 m. Results are promising, with engine thrust ranging from 100 N to 1500 N for ISPs ranging from 1400 s to 400 s for ethane and propane fuels. Quasi one-dimensional solver results for one optimised engine are compared to CFD results, which show that the results from the quasi-one-dimensional solver are reasonably accurate. The performance of two optimised engines is presented over a given flight profile to observe thrust and ISP from take-off to cruise. Finally, a sensitivity analysis of thrust and ISP to optimisation variables was conducted. Engine thrust and ISP were found to be most sensitive to fuel nozzle exit area to throat area, fuel jet stagnation pressure, fuel jet nozzle exit area, combustor diameter, and engine nozzle exit diameter.
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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".