Characteristics of Control Laws Tested on the Semi-Span Super-Sonic Transport (S4T) Wind-Tunnel Model
Bibliographic record
Abstract
The Semi-Span Supersonic Transport (S4T) is an aeroelastically scaled wind-tunnel model built to test active controls concepts for large flexible supersonic aircraft in the transonic flight regime. It is one of several models constructed in the 1990's as part of the High Speed Research (HSR) Program. Control laws were developed for the S4T by M4 Engineering, Inc. and by Zona Technologies, Inc. under NASA Research Announcement (NRA) contracts. The model was tested in the NASA-Langley Transonic Dynamics Tunnel (TDT) four times from 2007 to 2010. The first two tests were primarily for plant identification. The third entry was used for testing control laws for Ride Quality Enhancement, Gust Load Alleviation, and Flutter Suppression. Whereas the third entry only tested FS subcritically, the fourth test demonstrated closed-loop operation above the open-loop flutter boundary. The results of the third entry are reported elsewhere. This paper reports on flutter suppression results from the fourth wind-tunnel test. Flutter suppression is seen as a way to provide stability margins while flying at transonic flight conditions without penalizing the primary supersonic cruise design condition. An account is given for how Controller Performance Evaluation (CPE) singular value plots were interpreted with regard to progressing open- or closed-loop to higher dynamic pressures during testing.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".