The numerical investigation of combustion performance of scramjet combustor with variation in angle of attack
Bibliographic record
Abstract
The present article involves computational investigation of parallel fuel injection based scramjet combustor. The wedge shaped strut face is used as a fuel injector. Selected geometry is first validated and then further investigation is performed. Steady-state, two dimensional, scramjet combustor model has been chosen to complete the numerical convergence through ANSYS Fluent software. Grid independence analysis has also been performed. Reynolds Average Navier Stokes equation in addition with k-epsilon turbulence modelling have been utilised to reach the convergence at a lower computational cost. Finite rate eddy-dissipation based Species transport modelling is chosen to solve the chemical kinetics between hydrogen and air. The incoming boundary condition of free-stream air has been optimized to improved combustion efficiency. There are three selected model is utilised for comparison i.e. zero degree, positive five and negative five degree air angle of attack model. To change the angle of attack of the incoming air, a Modified isolator is added ahead of the combustor with constant length. It is observed that the behaviour of shock waves and flow properties are dependent on the angle of attack. Comparative observation has been analysed with monitoring the combustion efficiency graph. Maximum combustion efficiency reaches up to 93% in negative five degree air angle of attack model moreover, mixing is also improved by 4%. It can be summarized that the scramjet combustor performance is primarily highly influenced by the geometry configuration and the nature of flow of incoming air.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".