A multisegment dynamic model of ski jumping

This paper presents a planar, four-segment, dynamic model for the flight mechanics of a ski jumper. The model consists of skis, legs, torso and head, and anns. Inputs include net joint torques that are used to vary the relative body configurations of the jumper during fiight. The model also relies on aerodynamic data from previous wind tunnel tests that incorporate the effects of varying body configuration and orientation on lift, drag, and pitching moment. A symbolic manipulation program, "Macsyma," is used to derive the equations of motion automatically. Experimental body segment orientation data during the fiight phase arc presented for three ski jumpers which show how jumpers of varying ability differ in flight and demonstrate tlie need for a more complex analytical model than that previously presented in the literature. Simulations are presented that qualitatively match the measured trajectory for a good jumper. The model can be used as a basis for the study of optimal jumper behavior in fiight which maximizes jump distance

Davis Instrumented Bicycle Experiment Raw Data

<p>This data set contains all of the raw data collected with the Davis Instrumented Bicycle during the trials described in:</p> <p>Moore, J. K. "Human Control of a Bicycle", 2012.</p> <p>See the README.TRIAL.rst for more details.</p

Instrumented Bicycle Raw Data HDF5

<p>This HDF5 file includes all of the raw data collected with the Davis Instrumented Bicycle in:</p> <p>Moore, J. K., Human Control of a Bicycle. University of California, Davis. 2012</p> <p>This material is partially based upon work supported by the National Science Foundation under Grant No. 0928339. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.</p> <p></p

Davis Instrumented Bicycle Corrupt Trial Log

<p>This comma separated text file contains additional metadata for the trials collected in:</p> <p>Moore, J.K. "Human Control of a Bicycle", 2012.</p> <p>This metadata was collected from observations during the trials and from review of the videos of the trials. The columns are as follows:</p> <p>- runid: The integer value corresponding to the trial identification number.</p> <p>- corrupt: A boolean (TRUE) specifying if the data from the trial should be discarded.</p> <p>- warning: A boolean (TRUE) specifying if the data is questionable and should be carefully reviewed before using.</p> <p>- knee: A semi-colon separated list of integers (starts at 1) specifying the perturbation numbe in which rider's knees disengaged from the bicycle frame.</p> <p>- handlebar: A semi-colon separated list of integers (starts at 1) specifying the perturbation number in which the handlebars of the bicycle touched or collided with the treadmill handrails.</p> <p>- trailer: A semi-colon separated list of integers (starts at 1) specifying the perturbation number in which the trailer of the bicycle touched or collided with the treadmill side guards.</p> <p>- reason: A string specifying why the data was marked as it was in plain ole English.</p

Davis Instrumented Bicycle Calibration Raw Data

<p>This data set contains binary Matlab .mat files, one for each sensor calibration, for the instrumented bicycle experiments described in:</p> <p>Moore, J. K. "Human Control of a Bicycle", 2012</p

Modeling the Manually Controlled Bicycle Source Code

<p>Human control of a bicycle.</p

Bicycle Steer Torque Magnitude Measurement Dataset

<p>A torque wrench was fixed to the stem of the bicycle fork and two riders performed a set of controlled maneuvers at various speeds. The torque wrench, handlebars and speedometer were recorded from a camera affixed to the bicycle frame. The speed was maintained by an electric hub motor (i.e. no pedaling). The file "data.txt" includes the run number that corresponds to the video number, the rider's estimate of the speed after the run in miles per hour, the maximum reading from the torque needle after the run in inch-lbs, the rider's name, the maneuver, the minimum speed seen on the video footage in miles per hour, the maximum speed seen on the video footage in miles per hour, the maximum torque seen on the video footage in inch-lbs, the minimum torque seen on the video footage in newton-meters, and the rotation sense for each run (+ for clockwise [right turn] and - for counter clockwise [left turn]). There were seven different maneuvers: straight into tracking a half circle (radius = 6 meters and 10 meters), tracking a straight line, straight to a 2 meter lane change, slalom with 3 meter spacing, steady circle tracking (radius = 5 and 10 meters).</p> <p>Details of the experiments and example results can be found in:</p> <p>Moore, J. K., "Human Control of a Bicycle", 2012</p> <p>http://moorepants.github.io</p> <p>All of the video data is hosted on the Internet Archive:</p> <p>https://archive.org/details/BicycleSteerTorqueExperiment01</p> <p></p

An Optimal Handling Bicycle

We present a method to find an optimal handling bicycle using purely analytical and numerical means. Given a linear parametrized bicycle vehicle model, a simple manual controller, and a model-based metric that correlates with subjective handling measures, we formulate the search for optimal geometric parameters that give the best handling bicycle. Optimal bicycle designs for a number of design speeds are discovered including bicycles with unintuitive geometry, such as large negative trail. The resulting maximum handling quality metrics for the optimal bicycle designs follow a logarithmic relationship with respect to design speed. These optimal handling bicycles can be ridden at speeds other than the design speed and, in general, handling difficulty decreases with increasing speed. Finally, we show that there is little evidence of correlation between bicycle open-loop stability and optimal handling

Optimal bicycle design to maximize handling and safety

This article is part of the Proceedings of the 6th Annual International Cycling Safety Conference held in Davis, California, USA on September 20th through 23rd in the year 2017.<br><br>Paper ID: 10

Experimental Validation of Bicycle Handling Prediction

This article is part of the Proceedings of the 6th Annual International Cycling Safety Conference held in Davis, California, USA on September 20th through 23rd in the year 2017.<br><br>Paper ID: 10