Matching Items (4)
Description
During the downswing all golfers must roll their forearms and twist the club handle in order to square the club face into impact. Anecdotally some instructors say that rapidly twisting the handle and quickly closing the club face is the best technique while others disagree and suggest the opposite. World class golfers have swings with a range of club handle twist velocities (HTV) from very slow to very fast and either method appears to create a successful swing. The purpose of this research was to discover the relationship between HTV at impact and selected body and club biomechanical characteristics during a driver swing. Three-dimensional motion analysis methods were used to capture the swings of 94 tour professionals. Pearson product-moment correlation was used to determine if a correlation existed between HTV and selected biomechanical characteristics. The total group was also divided into two sub-groups of 32, one group with the fastest HTV (Hi-HTV) and the other with the slowest HTV (Lo-HTV). Single factor ANOVAs were completed for HTV and each selected biomechanical parameter. No significant differences were found between the Hi-HTV and Lo-HTV groups for both clubhead speed and driving accuracy. Lead forearm supination velocity at impact was found to be significantly different between groups with the Hi-HTV group having a higher velocity. Lead wrist extension velocity at impact, while not being significantly different between groups was found to be positive in both groups, meaning that the lead wrist is extending at impact. Lead wrist ulnar deviation, lead wrist release and trail elbow extension velocities at maximum were not significantly different between groups. Pelvis rotation, thorax rotation, pelvis side bend and pelvis rotation at impact were all significantly different between groups, with the Lo-HTV group being more side bent tor the trail side and more open at impact. These results suggest that world class golfers can successfully use either the low or high HTV technique for a successful swing. From an instructional perspective it is important to be aware of the body posture and wrist/forearm motion differences between the two techniques so as to be consistent when teaching either method.
ContributorsCheetham, Phillip (Author) / Hinrichs, Richard (Thesis advisor) / Ringenbach, Shannon (Committee member) / Dounskaia, Natalia (Committee member) / Crews, Debra (Committee member) / Arizona State University (Publisher)
Created2014
Description
There are many computer aided engineering tools and software used by aerospace engineers to design and predict specific parameters of an airplane. These tools help a design engineer predict and calculate such parameters such as lift, drag, pitching moment, takeoff range, maximum takeoff weight, maximum flight range and much more. However, there are very limited ways to predict and calculate the minimum control speeds of an airplane in engine inoperative flight. There are simple solutions, as well as complicated solutions, yet there is neither standard technique nor consistency throughout the aerospace industry. To further complicate this subject, airplane designers have the option of using an Automatic Thrust Control System (ATCS), which directly alters the minimum control speeds of an airplane.
This work addresses this issue with a tool used to predict and calculate the Minimum Control Speed on the Ground (VMCG) as well as the Minimum Control Airspeed (VMCA) of any existing or design-stage airplane. With simple line art of an airplane, a program called VORLAX is used to generate an aerodynamic database used to calculate the stability derivatives of an airplane. Using another program called Numerical Propulsion System Simulation (NPSS), a propulsion database is generated to use with the aerodynamic database to calculate both VMCG and VMCA.
This tool was tested using two airplanes, the Airbus A320 and the Lockheed Martin C130J-30 Super Hercules. The A320 does not use an Automatic Thrust Control System (ATCS), whereas the C130J-30 does use an ATCS. The tool was able to properly calculate and match known values of VMCG and VMCA for both of the airplanes. The fact that this tool was able to calculate the known values of VMCG and VMCA for both airplanes means that this tool would be able to predict the VMCG and VMCA of an airplane in the preliminary stages of design. This would allow design engineers the ability to use an Automatic Thrust Control System (ATCS) as part of the design of an airplane and still have the ability to predict the VMCG and VMCA of the airplane.
This work addresses this issue with a tool used to predict and calculate the Minimum Control Speed on the Ground (VMCG) as well as the Minimum Control Airspeed (VMCA) of any existing or design-stage airplane. With simple line art of an airplane, a program called VORLAX is used to generate an aerodynamic database used to calculate the stability derivatives of an airplane. Using another program called Numerical Propulsion System Simulation (NPSS), a propulsion database is generated to use with the aerodynamic database to calculate both VMCG and VMCA.
This tool was tested using two airplanes, the Airbus A320 and the Lockheed Martin C130J-30 Super Hercules. The A320 does not use an Automatic Thrust Control System (ATCS), whereas the C130J-30 does use an ATCS. The tool was able to properly calculate and match known values of VMCG and VMCA for both of the airplanes. The fact that this tool was able to calculate the known values of VMCG and VMCA for both airplanes means that this tool would be able to predict the VMCG and VMCA of an airplane in the preliminary stages of design. This would allow design engineers the ability to use an Automatic Thrust Control System (ATCS) as part of the design of an airplane and still have the ability to predict the VMCG and VMCA of the airplane.
ContributorsHadder, Eric Michael (Author) / Takahashi, Timothy (Thesis advisor) / Mignolet, Marc (Committee member) / White, Daniel (Committee member) / Arizona State University (Publisher)
Created2016
Description
In this experiment, three cats walked freely in four different conditions (walking on a flat surface in the dark, walking on a flat surface in the light, along a horizontal ladder, and a stone-cluttered pathway) while gaze was recorded. Four gaze behaviors were identified based upon head and eye velocity parameters relative to the walking velocity of the cat: constant gaze, fixation, gaze shift away, and gaze shift toward (see Methods). The objective of the study was to determine whether speed influences the phase that these gaze behaviors occur, where phase is defined as the degree from 0-360 of the step cycle. In the step cycle, 0 degrees is defined as the start of swing of the right forelimb. Additionally, speed’s influence on the uniformity of gaze behaviors to the step cycle was investigated in the three cats. The cats performed complex walking tasks, or conditions, as well as simple tasks to determine if speed has a greater effect on gaze behavior timing when walking terrain was difficult. I hypothesized that 1) gaze-stride coordination would be influenced by speed, 2) faster steps would show improved gaze behavior uniformity between subjects, and 3) fast steps during complex walking tasks would show further improvement of gaze behavior uniformity between subjects. To, this end, recorded steps were first split into fast and slow steps based upon step duration parameters (see Methods). These fast and slow steps were confirmed as significantly different from one another using a one-way ANOVA test on a linear mixed effects model (Table 3). Then, a linear mixed effects model was made per walking condition to account for subject effects, and a two-way ANOVA test was performed on the model to compare the phases of gaze behaviors to the speed when they occurred. It was found that speed does not influence the phase that gaze behaviors occur, except for walking on a flat surface in the dark. However, post-hoc tests could not be run to determine which behaviors were affected by speed. (see Discussion). The insignificance of speed suggests that speed is accounted for by the visual center responsible for the control of gaze behavior (see Discussion). Aside from speed’s influence on phase, uniformity was examined using standard deviation (Figure 3 ). It was found that faster steps tend to adopt a “gaze stepping” behavior described in a previous paper (Rivers et al. 2014). In future studies, it would be useful to increase the number of subjects for a similar experiment to improve the robustness of the results to determine if the relationship between speed and gaze behaviors reported in this paper is accurately depicted.
ContributorsJohnson, Justin (Author) / Honeycutt, Claire (Thesis director) / Hamm, Thomas (Thesis director) / N/A, N/A (Committee member) / Dean, W.P. Carey School of Business (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
OP50 Esherichia coli is a Gram-negative bacterium with a fast replication rate and can be easily manipulated, making it a model species for many science disciplines. To probe this bacterium’s search strategy, cultures were starved and the cell velocity was probed at various points later in time after perturbing the buffer in which the bacteria were located. To start, we added E.coli OP50 filtrate. In yet another experiment filtrate from a Bdellovibrio bacteriovorus (Gram-negative predator) culture was added to monitor the OP50’s differential response to cues from its environment. Using MATLAB code, thousands of E.coli tracks were measured.
ContributorsSanchez, Alec Jesus (Author) / Presse, Steve (Thesis director) / Gile, Gillian (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05