Natural Frequency Based Topology Optimization of an Aircraft Engine Support Frame
Bibliographic record
Abstract
Abstract The multi-stage design space refinement (MSDSR) technique increases the likelihood of convergence of topology optimization (TO) with large volume fraction constraints. This work considers MSDSR TO of an aircraft engine support frame with a natural frequency-based objective function. The problem statement maximized the first natural frequency, effectively maximizing the stiffness to mass ratio of the frame. The problem statement considered natural frequency constraints, which eliminated all natural frequencies within 5% of the engine excitation frequency times a safety factor of two. The design space did not consider the initial geometry; therefore, allowing for the determination of the optimal stiffener location on the initial geometry. The results of this work increased the first natural frequency of the engine support frame by 25.9%, eliminated all natural frequencies within 11.3% of the engine excitation frequency, and added only 0.253 kg of mass to the frame. The results of this work further demonstrate the advantages of MSDSR TO and the impact that it can have on the aerospace industry. Specifically, the design space considered in this work allows for the structural reinforcement of a pre-existing design, which is easier to implement and easier to regulate than similar results from the literature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".