Statistical analysis of CFD results for missile surface pressures
Bibliographic record
Abstract
An international computational aerodynamics study under the auspices of The Technical Cooperation Program (TTCP) Weapons Technology Panel 2 (APN-TP-2) involving participants from defense research laboratories of the United States, United Kingdom, Canada, and Australia was recently completed. The purpose of this study was to examine computational predictive technologies for finned missile shapes by comparing Navier-Stokes predictions to experimental data. Experimental data consisting of surface pressures on the body and fins, flow field pitot pressures, and force measurements were available for comparison to the computational results. The computational results for this study established an extensive database for evaluation and comparison. The fall database consists of results from six Navier-Stokes codes obtained by seven multi-block patched and unstructured grids for five distinct test cases. The statistical analysis techniques developed to help provide an evaluation of the predictive techniques are described. Quantitative results of the analysis of the differences between computational and experimental results are presented graphically and quantitatively in terms of medians, standard deviation, and a figure of merit to assist in the overall evaluation of the study results. The good performance achieved using the Spalart-Allmaras turbulence model and multi-block patched and unstructured grid techniques are noted in the findings.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".