Investigation of the Feasibility of Deploying Electro-rheological Dampers in a Drop Tower to Replicate the Impact Profiles of High Speed Craft
Bibliographic record
Abstract
High-speed craft (HSC) occupants are subjected to a harsh environment due to repeated shock loading and vibration which results in serious potential for injury to the occupants.Various suspension seats have been developed to attenuate such shocks.In order to study the shock mitigating effectiveness of suspension seats and the injury mechanisms associated with shock loading, it is important that these seats be studied and characterized in the laboratory by reproducing the impacts that HSC are subjected to at sea.The motivation of this work is to investigate the feasibility of deploying electro-rheological (ER) dampers in a drop tower in order to replicate the impact profiles that HSC and their seats experience at sea.A Matlab code was developed to simulate the impact profiles using ER dampers.Simulations were performed to understand the effect of system parameters (e.g., impact mass, drop height, and damping force) of the drop tower to obtain the impact profiles of HSC.Furthermore, the system parameters were optimized using a suitable Matlab optimization algorithm to reproduce the impact profiles of HSC as accurately as possible.Next, available ER dampers were characterized at high velocities which involved dynamic testing using the drop tower apparatus and identifying the parameters of a Bingham plastic model to represent the characteristics of the damper.The characterization results were analyzed in order to assess the potential of using ER dampers to replicate impact profiles of HSC.The available ER dampers did not show the expected increase in damping force with 166 Comparison of force-time graph between the Bingham model and experimental results (45 cm, 3.6 V). . . . . . . . . . . . . . . . . . . .
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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 | 0.000 | 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".