On Further Development of an Unstructured Space-Marching Technique
Bibliographic record
Abstract
Space marching methods are widely used to calculate steady supersonic internal and external flows in view of their superior efficiency as compared to time marching approaches. A very general space-marching procedure was proposed by Nakahashi and Saitoh in 1996. The method can be used with any explicit time-marching procedure, allows for embedded subsonic regions, and is well-suited for unstructured grids, enabling a maximum of geometric flexibility. The flow field is only updated in the so-called active domain. Once the residual has fallen below a preset tolerance, the active domain is shifted. The present work is aimed at further refining of the Nakahashi and Saitoh’s method. This is achieved via the following new approaches: (a) A non-reflecting boundary condition is implemented at the exit of the active domain, thus eliminating non-physical reflections propagating upstream; (b) A general, user-independent procedure for partitioning of the computational domain into a set of active domains is proposed. These allow to minimize the size of the active domain and the residual monitor region, thus contributing to the method’s efficiency; (c) Local grid adaptation is performed in the course of space marching. These ideas are verified and tuned, at first, on two simple test problems (supersonic flows over compression and expansion corners). Finally, the results of the application of the improved method to supersonic flows in air-breathing engine inlets and ram accelerators are presented.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".