Computational and Experimental Characterization of a Novel Nonlinear Magnetic Shock Absorber with Applications to Ground Vehicles
Bibliographic record
Abstract
The aim of this thesis is to propose a novel magnetic nonlinear shock absorber and to study the feasibility of the system in real-world applications.This shock absorber consists of an array of identical repelling magnets with an electromagnetic coil energy harvesting system.The nonlinear dynamics of the shock absorber were developed analytically and are implemented in a simulation environment.A parametric study was conducted to study the effects of the magnet mass, repelling force, inter-lattice equilibrium distance, and coil damping on the response of the system.Additionally, to experimentally validate the nonlinear dynamic modelling of the novel magnetic shock absorber, a magnetic shock absorber prototype was developed.The unknown parameters of the shock absorber, such as the magnetic repelling force relationship, were identified.The static and transient responses of the simulation were compared with those of the manufactured magnetic shock absorber prototype and it was shown that the simulation and the experimental results are in agreement.The feasibility of a novel nonlinear magnetic shock absorber in road vehicle applications was also investigated.The magnetic shock absorber was implemented in quarter and half car models and their nonlinear dynamics were developed analytically.The developed models were implemented in a simulation environment and the results were validated using a quarter car experimental setup.Furthermore, a parametric study was conducted to understand the effects of varying certain parameters such as the number of magnets, coil damping intensity, etc. on the response of the system.The performance of the novel magnetic shock absorber was evaluated and also compared to a conventional linear one.In response to a continuous rough road surface, the ride quality of the magnetic shock absorber was shown to be similar to the linear one.Further, the road holding capabilities were demonstrated to be superior I would like to express my deepest gratitude and regards to my supervisors, Dr. Fidel Khouli, Dr. Robert Langlois, and Dr. Fred Afagh, for their excellent guidance, patience, and willingness to provide support whenever it was needed during the course of this project.Their wealth of knowledge and experience brought support and guidance that exceeded my expectations.
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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 | 0.000 |
| 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) | 0.003 | 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".