Frequency Response Based Optimization of an Aircraft Hydraulic Pump Support Structure
Bibliographic record
Abstract
Abstract Free-size optimization is an effective method for determining the optimal thickness of the elements in a finite element model given a set of constraints and objectives. This work aims to show that size optimization can be used as a tool to reduce the structural vibrations transmitted from non-structural components on-board an aircraft into the cabin. A test structure of a hydraulic pump support frame is analyzed using a frequency response analysis. The output velocity is measured for three independent dynamic input loads across a 20–4000 Hz bandwidth. The maximum frequency response velocity for each input load was used to define the upper bounds of the velocity for the optimized model. The design space had a minimum thickness equal to the original thickness and a maximum thickness of 1.5 times the original thickness. The objective was to determine the optimal locations for new material to be added, so that the original design was not significantly changed. This was done to ensure the structural integrity of the frame and increase the ease of implementing the design while still reducing the transmission of structural vibrations. The optimized model showed an average reduction of 6% in the maximum frequency response velocity for the three loading directions, while increasing the mass by 9%.
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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) | 0.000 | 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 | 0.001 | 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".