Hydrogen/Air Supersonic Combustion Modelling and Validation for Scramjet Applications
Bibliographic record
Abstract
In the field of air-breathing hypersonic scramjet vehicles design and development, the Italian Aerospace Research Centre -CIRA contributed to an international project, called Stratospheric Flying Opportunities for High-Speed Propulsion Concepts -STRATOFLY, in collaboration with several European organizations, coordinated by Politecnico diTorino under the EC Horizon 2020 programme financial support.The aim of this project was the improvement of enabling technologies for the realization of a commercial hypersonic aircraft, able to fly at Mach 8, at 30÷35 km of altitude, for at least 4 hours with a minimum environmental impact and especially low NOx emissions.Understanding the complex, supersonic, turbulent, combustion processes occurring during scramjet operations is of fundamental importance.For this purpose, a thorough 0D kinetic assessment was carried out by means of the open-source Cantera software for the identification of the most suitable kinetic mechanisms, able to predict with satisfactory accuracy both the ignition delay times and the NOx emissions at the relevant scramjet operating conditions.Several kinetic schemes were investigated and the computed results were compared with the literature available shock tubes experimental data.In the entire investigated operative box, the best agreement, in terms of induction times, was achieved using the kinetic mechanism developed by Zettervall and Fureby with the exception of the ignition tests carried out in presence of argon as diluent bath gas, where the kinetic schemes by Kéromnès and CRECK together with the Aramco-II full mechanism provide the best matching.Moreover, for considering the generation of NOx, the three fundamental thermal route reactions by Zel'dovič, were added.Furthermore, full 3D CFD simulations were carried out by means of Ansys ® Fluent in order to compare experimental data and evaluate the predictivity and accuracy of the chemical/kinetic sub-model conceived in this way when coupled with mixing, turbulence and fluid dynamic interactions arising from the simulations of the whole propulsive system.For the sake of comparison, the experiments carried out on the small-scale scramjet vehicle of LAPCAT-II by the HEG (DLR) were rebuilt.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".