Developing a predictive model for low-temperature Laval nozzles with applications in chemical kinetics
Bibliographic record
Abstract
Laval nozzles are used in the CRESU (“Cinétique de Réaction en Écoulement Supersonique Uniforme”) method to generate a collimated low temperature (5–200 K), low pressure (30–500 Pa), high Mach number (1 < M < 20) supersonic jet. Laval nozzles have been designed using the Method of Characteristics (MOC) since the development of CRESU, which is an analytical method that assumes inviscid, isentropic flow, and is routinely used to design nozzle profiles for a particular gas and temperature with a uniform shock free exit. This study aims to provide a robust computational framework to overcome the limitations of the MOC while also providing recommendations on the numerical model setup required to model a low-temperature supersonic jet. It also discusses the blockage effects when using the Pitot tube method for flow characterization, the influence of inlet turbulence and reservoir size. Numerical results are validated using two different experimental apparatuses from research groups at the University of Leeds and the University of Birmingham. Finally, a MATLAB framework was developed and has been provided as an open source toolbox to allow any user to perform computational fluid dynamics on any Laval nozzle, with the ability to change nozzle geometry, operating conditions and bath gas. The toolbox has been rigorously tested against many benchmark cases, which shows that steady-state Reynolds-averaged Navier–Stokes with the k-omega-shear stress transport turbulence model can be used to accurately predict global quantities, such as average temperature in the stable region of the supersonic jet.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".