Experimental and numerical investigation of the dynamic seat comfort in aircrafts.
Bibliographic record
Abstract
This research focuses on the dynamic seat comfort in aircrafts specifically during takeoff, landing and cruise through turbulence flight conditions. The experiments are performed using a multi axis shaker table in the Automotive Centre of Excellence (ACE) at the University of Ontario Institute of Technology subjected to sample takeoff, landing and cruise vibration recordings obtained onboard of an actual flight. The input vibrations introduced to the aircraft seats during actual flight conditions and during the experiments in the ACE are compared and it is concluded that the given flight conditions were successfully replicated for the interest of this thesis.\nThe experiments are conducted with two different aircraft seats, economy class and business class. Furthermore, to investigate the importance of seat cushion characteristics in addition to economy and business class seat cushions, three laboratory made cushions were included in the investigation as well. Moreover, the effect of passenger weight is also discussed by conducting the experiments with 1 and 2 identical dummies.\nIt is concluded that static seat properties play a significant role in the comfort perception level as well as flight conditions. Among the three flight condition, landing appeared to be the most uncomfortable case comparing to takeoff and cruise.\nIn addition to experimental work, a numerical study to simulate the flight conditions is undertaken with the initial work of CAD modelling. The simulated responses of the seat is partially matching with experimental results due to unknown parameters of the cushion and the connections of the aircraft seat that cannot be created in the CAD model due to unknown manufacturing processes.
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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) | 0.002 | 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".