Isogeometric variational multiscale modeling of stably stratified flow over complex terrains
Bibliographic record
Abstract
Numerical modeling of stratified boundary layer over complex terrain has been an ongoing challenge in the field of environmental fluid dynamics. In this work, we present a computational framework aiming to tackle that challenge. The key components of the framework are residual-based variational multiscale method, isogeometric analysis, and weak imposition of Dirichlet boundary condition. The framework is validated against a laboratory experiment on strongly stratified flow past a three-dimensional bell-shaped hill. Good agreement is observed for qualitative flow physics, with the predicted occurrences of flow separation, recirculation, and hydraulic jump closely matching those in the experiment. In addition, the dividing-streamline height and wavelength of lee wave computed from the present framework compare well to the theoretical predictions. We show that the present framework is able to tackle various degrees of stratifications. The effect of weak imposition of Dirichlet boundary condition on the performance of the framework is also examined. This paper is concluded with an outlook toward applying the present framework to modeling microscale stratified flow past real-world terrains by simulating stratified flow past a two-dimensional environmental terrain.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".