Bibliographic record
Abstract
A three-dimensional (3D) predictive golfer model can be a valuable tool for investigating the golf swing and designing new clubs. A forward dynamic model for simulating golfer drives is presented, which includes: (1) a four degree of freedom golfer model, (2) a flexible shaft model based on Rayleigh beam theory, (3) an impulse-momentum impact model, (4) and a spin rate controlled ball trajectory model. The input torques for the golfer model are provided by parameterized joint torque generators that have been designed to \nmimic muscular inputs. These joint torques are optimized to produce the longest ball carry distance for a given set of golf club design parameters. The flexible shaft model allows for continuous bending in the transverse directions, axial twisting of the club and variable shaft stiffness along its length. The completed four-part model is used for examining the following parameters of interest in club design by performing simulation experiments: clubhead \nmass, clubhead centre of mass location, clubhead moment of inertia, shaft flexibility, and clubhead and shaft aerodynamics. \n \nAnalysis of the experiments led to the following recommendations for golf club design: \n1. The clubhead mass should continue to be around 200g. \n2. The centre of mass of the clubhead should be as close to the face as possible. \n3. Shaft flexibility should be tuned for an individual golfer, depending on their particular swing. \n4. Clubhead and shaft aerodynamic drag have a significant effect on the ball carry and clubhead orientation, and should be minimized during the club design process. \n \nFinally, suggestions are made for future research which can be performed in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".