Optimization of a Supersonic Rocket-Based Combined Cycle Inlet Using Differential Evolution
Bibliographic record
Abstract
A differential evolution optimization algorithm is proposed for an airbreathing rocket inlet called the exchange inlet at supersonic flight conditions. A five-parameter fitness function is used, which includes variables representing the ingested air mass flow, total pressure drop through the inlet, and shear layer area. Using a differential weight of 0.85, a population size of 75, and a crossover probability of 0.3, it is shown that the algorithm yields a design with a genome within 10% of the most likely global optimum 93% of the time. Single-point optimization is performed at flight Mach numbers of 1.5, 2.5, and 3.5 to establish a Pareto front of optimal designs. From these Pareto fronts a single optimum is chosen and evaluated over a range of off-design flight Mach numbers from 1.3 to 4.0. In terms of air mass flow and total pressure, the Mach 2.5 optimal design is shown to outperform the other designs between Mach 2.0 and 3.2, while yielding an air mass flow within 12% of the others at all other flight conditions considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".