Computational Flow Analysis in Aerospace, Energy and Transportation Technologies with the Variational Multiscale Methods
Bibliographic record
Abstract
With the recent advances in the variational multiscale (VMS) methods, computational ow analysis in aerospace, energy, and transportation technologies has reached a high level of sophistication. It is bringing solutions in challenging problems such as the aerodynamics of parachutes, thermo-fluid analysis of ground vehicles and tires, and fluid-structure interaction (FSI) analysis of wind turbines. The computational challenges include complex geometries, moving boundaries and interfaces, FSI, turbulent flows, rotational flows, and large problem sizes. The Residual-Based VMS (RBVMS), Arbitrary Lagrangian-Eulerian VMS (ALE-VMS) and Space-Time VMS (ST-VMS) methods have been successfully serving as core methods in addressing the computational challenges. The core methods are supplemented with special methods targeting specific classes of problems, such as the Slip Interface (SI) method, MultiDomain Method, and the ST-C data compression method. We provide and overview of the core and special methods. We present, as examples of challenging computations performed with these methods, aerodynamic analysis of a ramair parachute, thermo-fluid analysis of a freight truck and its rear set of tires, and aerodynamic and FSI analysis of two back-to-back wind turbines in atmospheric boundary layer flow. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".