New Parent Flowfield for Streamline-Traced Intakes
Bibliographic record
Abstract
The prespecified flowfield is essential in the inverse design method for generating inward-turning streamline-traced intakes, i.e., the parent flowfield. The internal conical flow C (ICFC) flowfield shows superiority over other parent flowfields in terms of performance and length. A drawback to the ICFC flowfield is the existence of the expansion zone, which creates a second reflected shock and decreases the flow uniformity. Hence, this paper proposes a new basic flowfield called the internal conical flow M (ICFM) to overcome the shortcoming of the ICFC flowfield. For the proposed basic flowfield, a new methodology has been devised for connecting the M-flow and truncated Busemann flowfields. This methodology merges the singular line of the M-flow with the truncated ray of Busemann flowfield in order to reduce the flow inclination angle difference effectively. The concept of the basic flowfield is reviewed, and the new ICFM basic flowfield is calculated using the Taylor–Maccoll equations. Complete details of the new merging procedure and calculation of the ICFM flowfield are presented. The characteristics of the new basic flowfield with design conditions of Mach 4.0, 5.0, 6.0, and 7.0 are examined at different outflow conditions. A comparison with the ICFC flowfield indicates that the new ICFM basic flowfield demonstrates better inviscid performance and a shorter length. Besides, the expansion region is significantly reduced, and the second reflected shock wave is eradicated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".