Dustin Brekke’s scientific contributions

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Publications (4)


Combining Physics and Deep Learning Models to Simulate the Flight of a Golf Ball
  • Conference Paper

June 2023

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53 Reads

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2 Citations

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Dustin Brekke

Figure 1. Gear effect for a deep center of gravity (CG) putter when impacted towards the toe.
Figure 2. Launch direction as a function of CG depth for a 15 mm toe impact.
Figure 3. Launch direction () for 15 mm toe impact based on discretionary mass placement. Final clubhead mass is 360 g. (a) Scenario 1; (b) Scenario 2; (c) Scenario 3; (d) Scenario 4.
Figure 4. Adjustable putter used for physical testing. (a) Configuration A ( = 11.2 mm, , = 3997 g cm , , = 360 g); (b) Configuration B ( = 25.2 mm, , = 4790 g cm , , = 360 g).
Exploration of Center of Gravity, Moment of Inertia, and Launch Direction for Putters with Ball Speed Normalizing Face Properties
  • Article
  • Full-text available

June 2020

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1,451 Reads

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1 Citation

The forgiveness of golf putters is traditionally achieved through weight distribution. Putters are often designed with large footprints, which help to increase the moment of inertia (MOI), but consequently move the center of gravity (CG) farther behind the face. The use of higher MOI putters will result in less ball speed loss on impacts away from the sweet spot (i.e., more forgiveness). It has been shown that certain face properties, such as milling patterns, grooves, or soft inserts, can be leveraged to have a similar effect. This paper explores the relationships between impact location, MOI, CG depth, discretionary mass placement, and launch direction for these putters. A novel design strategy is proposed. Minimizing CG depth for putters with ball speed normalizing face properties, even at the expense of MOI, can result in more consistent launch direction and distance control for the average player.

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Figure 1. COR map overlaid across a driver face. 
Figure 2. Example scatter plot of impact data. 
Figure 3. Expected COR, drivers A-D, weight functions 1-2. 
Development of a New Performance Metric for Golf Clubs Using COR Maps and Impact Probability Data

February 2018

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189 Reads

Golf clubs are often evaluated by many methods to determine their performance. For drivers in particular, the primary performance indicator is ball speed, which is directly related to distance. Golf club manufacturers make many claims about how extra ball speed is achieved. For example, these may include: design features, face thickness, materials, center of gravity location, and moments of inertia. This paper proposes a new method to evaluate the performance of golf clubs from a single metric. With the benefits of modern launch monitors, accurate impact location data can be captured very efficiently. Using this, along with COR (coefficient of restitution) map testing, the Expected Value of COR can be calculated for a golfer or population of golfers. The “Expected COR” metric takes into account many engineering properties of a particular club that affect ball speed, along with the impact variation of the golfer, to give a single score for rating.


Variable Face Milling to Normalize Putter Ball Speed and Maximize Forgiveness

February 2018

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1,214 Reads

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1 Citation

The forgiveness of golf putters is traditionally achieved through weight distribution. Higher MOI (moment of inertia) putters will show less ball speed loss on impacts away from the sweet spot. A very large MOI putter, however, may not be desired by a golfer due to weight or appearance. The relationship between ball speed and impact location is affected by the mass properties of the putter (i.e., CG location, mass, moments of inertia, products of inertia) and the putter face. It has been shown that certain face properties, such as milling patterns, grooves, or soft inserts, can have small effects on ball speed. This paper proposes a method to normalize the ball speed on laterally miss-hit putter impacts using a “model-specific” milling pattern of variable depth and pitch, resulting in the largest possible region of the face providing consistent putt distances, thus improving performance given the average player’s impact pattern.

Citations (3)


... While deep learning and physical modeling are usually investigated separately [7], [23], there are few researches exploring their combined use. In our context of estimating ball trajectories and their associated kinematic parameters, one can mention the work of McNally et al. [12]. The authors propose a golf ball flight model associating deep learning with equations of motion to generate realistic simulated golf ball trajectories and to model the relationship between aerodynamic coefficients and the golf ball's state. ...

Reference:

Predicting 3D Projectile Motion in Table Tennis Using Computer Vision and Physics-Informed Neural Network
Combining Physics and Deep Learning Models to Simulate the Flight of a Golf Ball
  • Citing Conference Paper
  • June 2023

... From experiments, he observed that face treatments such as inserts and grooves changed the contact forces but, for a given putt length, they had no effect on the ball skidding distance. Lambeth et al. used experiments and a 2D IM model (in the horizontal plane) to conclude that deviations from the desired ball launch direction for offcenter impacts are minimized by maximizing the vertical MOI and minimizing the distance from the putter face to the CoM (CoM "depth") [131]. ...

Exploration of Center of Gravity, Moment of Inertia, and Launch Direction for Putters with Ball Speed Normalizing Face Properties

... Efforts have been made by manufacturers to exploit this to improve ball speed consistency, with varying success. Other approaches to normalize ball speed have been considered as well, including a flexible impact surface [7] and a variable milling pattern [8]. ...

Variable Face Milling to Normalize Putter Ball Speed and Maximize Forgiveness