Optimization of Helicopter Air Intake Scoop Design
Bibliographic record
Abstract
In this paper, a global optimization technique based on the Adaptive Response Surface Method (ARSM) is integrated with a Control Volume Finite Element Method (CVFEM) for thermofluid optimization. The objective of the optimization is to improve the thermal effectiveness of an aircraft de-icing strategy by re-designing the cooling bay surface shape. By optimizing objective function in terms of the de-icing strategy and shape of the intake scoop, the best performance of the helicopter engine is achieved. This design problem is implemented on two different physical models. One model involves a heat conduction finite element analysis (FEA) process and the other combines the heat conduction and potential fluid flow FEA processes. Based on the comparison between the ARSM predicted results and the plotted objective function, it is observed that the integrated technique provides an effective method for thermofluid optimization. It also shows that the ARSM has a good flexibility to work with the computationally intensive process, e.g. CVFEM, and, potentially, could be developed and applied to the multidisciplinary design optimization (MDO) due to its open structure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".