CFD Modeling of High-Enthalpy Hypersonic Flows for FIRE-II Re-Entry Vehicle
Bibliographic record
Abstract
Hypersonic flows pose significant challenges in aerospace engineering and atmospheric sciences. While accurate physical modeling and numerical prediction of hypersonic flows are crucial for the design and analysis of this class of vehicles, the extreme conditions make this class of numerical simulations challenging to converge to a stable and steady solution. The authors’ previous study utilized a “two-temperature model” to simulate the hypersonic flow and thermal features of the FIRE-II re-entry vehicle. This previous work demonstrated basic trends for surface heat flux, temperature field, ionization of air, etc., at five operating points along the vehicle’s re-entry trajectory. The present work is a higher-fidelity investigation and validation performed on the FIRE II vehicle. In particular, the work aims at comparing the effects on the thermal environment of (1) two different chemical reaction mechanisms, and of (2) two wall catalysis models, full and partial-catalytic wall (PCW). Both chemical mechanisms, the Park and the Gupta models, account for the recombination of the ionized species and have been proven to predict reasonably well the heating trends on the surface of the capsule. The results show that the use of the catalytic wall model improves the prediction of surface heat flux on the vehicle across all the operating points as compared to previous studies. While trends in heat flux are captured well for different altitudes, the absolute values and the match with the experiment still present some discrepancies. These discrepancies are in large part attributable to the presence of a large uncertainty in the instantaneous flight conditions, in the assumption of steady-state conditions at each altitude as well as unknown catalytic conditions of the capsule’s thermal protection system (TPS). The study demonstrates how the Ansys Fluent density-based solver and the associated workflows and best practices from this study can be applied to more complex hypersonic applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".