Drag Prediction Using the Euler/Navier-Stokes Code FANSC
Bibliographic record
Abstract
Aerodynamic drag predictions using the block-structured Euler/Navier-Stokes flow solver FANSC, developed at Bombardier Aerospace for the analysis of the flow around complete aircraft configurations, are presented in this paper. To this end, the traditional far-field method, complemented with semi-empirical relations, is used for evaluating induced, form and viscous drag on a complete aircraft configuration from Euler/boundary-layer flow solutions. Recent advances in Navier-Stokes CFD methods technology are also used to yield near-field integration of the aerodynamic forces. Theoretical developments are briefly discussed on the numerical methods: the basic flow solver (discretization, time-integration, etc…), Euler/boundary-layer coupling methods (direct, semi-inverse and quasi-simultaneous) and Navier-Stokes method. The far-field and near-field drag prediction methods are discussed with emphasis on the relationship they carry with the CFD flow solution. Numerical results with comparisons to experimental data are presented on several aerodynamic geometries (flat-plate, airfoil, isolated nacelles, aircraft configurations).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".