Aerothermal/Ablation Analysis of a Projectile in High Speed Flow Conditions
Bibliographic record
Abstract
This paper will present the results of a study that was performed to predict the aerothermal heating and ablation of the nose of a projectile traveling at a speed of Mach 6 using Fluent and Ideas-TMG. The approach used relies on decoupling the CFD solution from the thermal solution by using 2 distinct solvers. CFD solutions are performed to provide the pressure, velocity fields and heating fluxes around the projectile. These fluxes are in turn mapped to the projectile in TMG and used as boundary conditions for a time step. The TMG solutions yields new wall temperatures and a new nose shape. This information is mapped back to the CFD solver by TMG and is used as a wall temperature boundary conditions to get heat fluxes for the next time step. This process is repeated for a discrete number of time steps over the initial part of the flight, where the temperature increase of the projectile, changes in the heat flux distribution and nose recession are most important. The paper will describe the CFD and thermal analysis performed and the process used to march in time and pass boundary conditions between the solvers. The new ablation and mapping capabilities that were developed in TMG for this project will also be presented.
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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 | 0.000 |
| 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 | 0.000 | 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".