A Preliminary Investigation of a Hydrogen Injection Wedge Strut for a Scramject Combustor Application
Bibliographic record
Abstract
This M.Eng Report is a preliminary investigation of the strut geometry in a strut-based hydrogen scramjet combustor and assesses flow characteristics in an inviscid and viscid flow with no hydrogen injection, a viscid flow with hydrogen injection, and a viscid flow with hydrogen injection and combustion model. This investigation is performed using ANSYS Fluent. The viscid cases utilize the Shear Stress Transport (SST) k-omega model for turbulence, with the volumetric eddy dissipation model for combustion case. Results in this study are validated against the same wedge strut geometry and results from the Deutsches Zentrum für Luftund Raumfahrt (DLR) scramjet, by Waidmann [1], and a grid independence study is conducted for completeness. Performance of the different geometries are quantified by mixing efficiency, combustion efficiency, and total pressure recovery. The 2-dimensional results of this paper are comparable to the Schlieren images (shadow graphs) of the DLR combustor. The viscid case with no hydrogen injection is compared to the inviscid case with no hydrogen stream, resulting in the presence of boundary layers and shear layers. This increased the strength and quantity of shock/expansion wave reflections downstream of the strut. The addition of the hydrogen stream increased the thickness of the central wake region along the combustor, further increasing strength of the shock reflections, as well as creating a symmetric recirculation region downstream of the strut. The eddy-dissipation combustion model is applied, resulting in a larger, non-symmetric recirculation region. The combustion efficiency reached 78.42% with complete mixing at 245 mm along the combustor, whereas the non-combusting case achieved complete mixing at 225 mm along the combustor. The high entropy caused by the irreversible processes across several strong shocks in the combustion case attributed to a total pressure loss of 46%.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".