Dynamics analysis and multi-objective optimization for a dry friction damper
Bibliographic record
Abstract
• Modeled a dry friction damper featured with a double layer stator and dual rub-impacts. • Developed semi-analytical solutions to correlate parameters with responses. • Proposed a metric to quantify damping ring wear based on solution stability. • Established multi-objective optimization to enhance damping and reduce component wear. • Implemented damper design adjustments guided by metaheuristic algorithm results. This paper presents the optimal design of a dry friction damper for a helicopter tail-rotor driveline, focusing on achieving superior vibration suppression around the first critical speed and enhanced component reliability. The optimization is driven by efficient fitness value calculations and response-based assessments of component wear. A detailed model is developed for the damper featuring a double-layer stator with dual rub-impacts, capturing complex interactions through derived semi-analytical solutions that enable precise and efficient dynamic response analysis. To assess durability, a novel wear metric is proposed, based on solution stability and differentiation of rub-impact patterns. Simulations are conducted to analyze multi-stage damping effects, dual rub-impact response characteristics, and parameter influences on these responses. Using a multi-objective optimization framework that incorporates both the semi-analytical solutions and wear metric, Pareto optimal solutions are generated through two metaheuristic algorithms, identifying distinct parameter regions to guide the development of two optimized damper configurations. These parameter recommendations are implemented through targeted adjustments and structural optimization, resulting in dampers that demonstrate significant improvements in both damping performance and wear reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".