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Browsing by Author "Boggs, Christopher Matthew"

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    Field Study to Evaluate Driver Fatigue Performance in Air-Inflated Truck Seats
    Boggs, Christopher Matthew (Virginia Tech, 2004-07-21)
    This study conducted a series of road tests in the regular fleet operations of a revenue service to better understand the relationship between vehicle seat design and driver fatigue, improve two newly proposed objective methods for evaluating driver fatigue, and provide design guidelines for evaluating and improving vehicle seat characteristics in terms of driver fatigue. Each driver completed a test session on two seat cushions - one a polyurethane foam cushion and one an air-inflated cushion. Objective measurements of pressure distribution were taken throughout each test session, while subjective measurements were collected using surveys taken at one-hour intervals. Based on these results, we find that the air-inflated seat cushion has advantages in terms of subjective measures of comfort, support, and fatigue. We show that the objective measure aPcrms highlights characteristic differences between seat cushions, as the air-inflated seat cushion provides less area in high pressure regions, thus occluding less blood flow to tissue in the seated area. While we were unable to effectively assess the validity of the proposed measures or improve them further, the characteristic difference between seat cushions is not highlighted by using previously existing objective measures. This implies that aPcrms is a more useful measure and should be considered when evaluating the subjective quality of seat cushion designs under dynamic conditions, such as those existing in commercial truck driving.
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    The Use of Simulation to Expedite Experimental Investigations of the Effect of High-Performance Shock Absorbers
    Boggs, Christopher Matthew (Virginia Tech, 2009-01-19)
    Successful race teams rely heavily on track testing to search for the ideal suspension setup. As more restrictions are placed on the amount of on-track testing by major racing sanctioning bodies, such as NASCAR, teams have increased their attention to alternate testing methods to augment their track data and better understand the dynamics of their racecars. One popular alternate to track testing is 8-post dynamic shaker rig testing. Eight-post rig testing gives the team a better understanding of the vehicle's dynamics before they arrive at the race track, allowing them to use their limited track testing time more efficiently. While 8-post rig testing certainly is an attractive option, an extensive test matrix is often required to find the best suspension setups. To take full advantage of 8-post rig tests, more efficient experimental methods are needed. Since investigating shock absorber selection is often the most time-consuming task, this study focuses on developing more efficient methods to select the best shock absorber setups. This study develops a novel method that applies dynamic substructuring and system identification to generate a mathematical model that predicts the results of future tests as both command inputs and components are changed. This method is used to predict the results of 8-post rig tests as actuator commands and shock absorber forces are varied. The resulting model can then be coupled with shock absorber models to simulate how the vehicle response changes with shock absorber selection. This model can then be applied to experimental design. First, a physically-motivated nonlinear dynamic shock absorber model is developed, suitable for quickly fitting experimental data and implementing in simulation studies. Next, a system identification method to identify a vehicle model using experimental data is developed. The vehicle model is then used to predict response trends as shock absorber selection is varied. Comparison of simulation and experimental results show that this model can be used to predict the response levels for 8-post rig tests and aid in streamlining 8-post rig testing experimental designs.