Computational aerodynamics with isogeometric analysis
Bibliographic record
Abstract
Abstract The superior accuracy isogeometric analysis (IGA) brought to computations in fluid and solid mechanics has been yielding higher fidelity in computational aerodynamics. The increased accuracy we achieve with the IGA is in the flow solution, in representing the problem geometry, and, when we use the IGA basis functions also in time in a space–time (ST) framework, in representing the motion of solid surfaces. It is of course as part of a set of methods that the IGA has been very effective in computational aerodynamics, including complex-geometry aerodynamics. The set of methods we have been using can be categorized into those that serve as a core method, those that increase the accuracy, and those that widen the application range. The core methods are the residual-based variational multiscale (VMS), ST-VMS and arbitrary Lagrangian–Eulerian VMS methods. The IGA and ST-IGA are examples of the methods that increase the accuracy. The complex-geometry IGA mesh generation method is an example of the methods that widen the application range. The ST Topology Change method is another example of that. We provide an overview of these methods for IGA-based computational aerodynamics and present examples of the computations performed. In computational flow analysis with moving solid surfaces and contact between the solid surfaces, it is a challenge to represent the boundary layers with an accuracy attributed to moving-mesh methods and represent the contact without leaving a mesh protection gap.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".