The Impact of the Incoming Non-Equilibrium Flow on the Hypersonic Wind Tunnel Heat Flux Prediction of A Re-Entry Body
Bibliographic record
Abstract
In this work, a numerical investigation of the vibrational non-equilibrium effects on the surface heat flux predictions of a re-entry body in hypersonic wind tunnels is performed.For this purpose, air mixture with five neutral species (N2, O2, NO, N and O) resulting from a 23.8 MJ/kg flow expansion from a plasma wind tunnel is considered to pass around a double-cone geometry.Non-equilibrium Navier-Stokes-Fourier equations within a density-based algorithm is here employed in the OpenFOAM framework.The numerical model was validated using data from experimental tests carried out at LENS-XX Expansion Tunnel facility and from past numerical results and proved to be very accurate with an excellent agreement with the experiments in terms of the size of the separation zone and an improvement of the heat flux peak value in 11% in comparison with past numerical results.It was found that when the freestream is in vibrational non-equilibrium, the shock waves/boundary layer interactions that occur in the flow field are felt more strongly in the test body surface then when the freestream is in equilibrium, resulting in an increase on the surface peak heat flux of 5%.It was also found a decrease in the thermal shock layer for non-equilibrium freestream condition, leading to higher temperature gradients in the flow field and consequently higher heat fluxes at the body surface.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".