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Record W6990452088

Development and Comparison of 3D Dynamic Models of Golf Clubhead-Ball Impacts

2023· dissertation· en· W6990452088 on OpenAlexafffund

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsBlackberry (Canada)
FundersUniversity of Waterloo
KeywordsBall (mathematics)Mathematical modelImpact assessmentSimulation modelingModel validationPredictive modellingExperimental data
DOInot available

Abstract

fetched live from OpenAlex

The scientific understanding and modeling of impacts has led to major advancements in golf equipment performance. The latest generation of drivers allow golfers to hit the ball farther compared to previous models. Simultaneously, new clubs are more forgiving, resulting in poor shots to land closer to the target. Impact models are an important tool as they allow for computer simulations and optimizations to inform design decisions. Several driver impact models have been published for this purpose. Impulse-momentum (IM) and other analytical contact models offer fast simulation times. While finite element (FE) models are used to design clubs, they were not considered here as they require orders of magnitude more compute time. Many existing impact models have no or limited experimental validation to support their conclusions. From a literature review, several impact models were considered to develop golf-specific impact models. A significant portion of this research was identifying model parameters and quantifying the accuracy of these models. This was achieved using experimental data consisting of driver and 7-iron shots by elite players. This allowed for the accuracy of these models to be compared against each other.
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\nThree IM-based impact models were used to predict the initial ball conditions for driver and 7-iron shots. The first model was referred to as the standard IM model as it followed the general methodology in the literature. The second IM-based model adds a small amount of mass to the clubhead and moves the ball centre of gravity slightly downwards to improve accuracy. These adjustments are intended to compensate for neglecting the shaft and ball deformation. The third IM-based model replaces the pure rolling assumption to allow a prescribed amount of slip at the contact point. This was motivated from experiments in the literature. The standard and adjusted IM models have been used to predict driver impacts. However, no published IM-based models exist to predict iron shots.
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\nThree continuous analytical models were also used to model golf impacts. The first model was a volumetric normal force contact model with a two-layer ball and velocity based friction model. This model captures the tangential force reversal observed in experimental studies and FE models. From a literature review, two other non-FE models were considered as they also predict this tangential force reversal. These models were extended to be suitable for modeling golf impacts. A damping parameter was added to the normal force equations to make the predicted collisions inelastic. These models were originally developed for 2D collisions against a rigid plate. Here they were extended to a 3D multibody dynamics model to predict the collision between a golf club and ball.
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\nFor modeling driver impacts, the adjusted IM-based model was the most accurate overall. Ball speed was the most accurate launch condition with a mean absolute error (MAE) of 1%. The MAE for vertical launch angle and backspin was less than 10%. However, the MAE for sidespin and horizontal launch angle (azimuth) was between 30%-50%. The relative error of these two launch conditions was considerably greater for all impact models considered. For modeling iron shots, the two-layer ball with a volumetric normal force model was the most accurate of the models considered. The MAE for ball speed was 2%. The remaining relative errors were similar to the driver model. 
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\nFrom separate experimental testing, it was shown that amateur golfers hit the ground before the ball on nearly one-third of iron shots hit off the ground. Soil mechanics models were applied to the clubhead to extend the iron impact model to predict this type of mishit. This new model with ground reaction forces was used to predict the behaviour of iron strikes for off-centre hits. This model shows that irons follow the same trends as a driver for off-centre shots. Including the ground model results in a greater distance loss and the shot being significantly offline compared to the no ground model. While no rigorous experimental validation was performed for the ground model, the decrease in ball speed is consistent with experimental observations in the literature. This new iron impact model allows for design optimizations, previously performed for drivers, to be completed with iron type clubs.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.208
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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