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The relationship of slip resistance (or coefficient of friction) to safe climbing system maneuvers on high profile vehicles has become an issue because of its possible connection to falls of drivers. To partially address this issue, coefficients of friction were measured for seven of the more popular fabricated metal step materials. Evaluated on these steps were four types of shoe materials (crepe, leather, ribbed-rubber, and oil-resistant-rubber) and three types of contaminant conditions (dry, wet-water, and diesel fuel). The final factor evaluated was the direction of sole force application. Results showed that COF varied primarily as a function of sole material and the presence of contaminants. Unexpectedly, few effects were attributible to the metal step materials. Numerous statistical interactions suggested that adequate levels of COF are more likely to be attained by targeting control on shoe soles and contaminants rather than the choice of a particular step material.

Although biomechanics models can predict the stress on the musculoskeletal system, they cannot predict how the muscle load associated with exertion is perceived. The short-term goal of the present study was to model the perception of effort in lifting and reaching tasks. The long-term goal is to determine the correlation between objective and subjective measures of effort and use this information to predict fatigue or the risk of injury. Lifting and reaching tasks were performed in seated and standing situations. A cylindrical object and a box were moved with one hand and two hands, respectively, from a home location to shelves distributed in the space around the subject. The shoulder and torso effort required to perform these tasks were rated on a ten point visual analog scale.

A four-valve-pentroof, direct-injection, optical engine fueled with n-heptane has been operated at four different steady-state HCCI operating conditions including 10% and 65% residuals, both at low and high swirl conditions. Both, planar toluene LIF and volume chemiluminescence show large scale inhomogeneity in the ensemble averaged images. The interpretation of the toluene-tracer LIF signals (when premixed with the fresh-air charge) as a marker for reaction homogeneity is discussed. A binarization scheme and a statistical analysis of the LIF images were applied to the per-cycle planar-LIF images revealing inhomogeneities both from cycle-to-cycle and within the regions of individual cycles that track with the average heat release rate. Comparison of these two homogeneity metrics between the four operating conditions reveals weak but discernable differences.

This paper presents the characterization of the random noise in driveline output shaft torque measurements that is commonly induced by road disturbances. To investigate the interaction between the shaft torque and road side excitation, torque signals are measured using a magnetoelastic torque sensor, as well as a conventional strain gauge sensor, under various types of road surfaces and conditions such as unevenness. A generalized de-trending method for producing a stationary random signal is first conducted. Statistical methods, in particular the probability density function and transform technique, are utilized to provide an evident signature for identifying the road excitation effect on the vehicle output shaft torque. Analysis results show how the road surface can act as a disturbance input to the vehicle shaft torque.

Current automotive design practices related to driver visibility are based on static laboratory studies of mostly straight ahead viewing that were conducted by Meldrum and others beginning in 1962. These individual studies have never been replicated either in the lab or in actual driving situations to determine the validity of their procedures. After a thorough review of the literature related to driver eye location and a statistical analysis of previous static eye location data, an experimental design is proposed to determine dynamic eye location distribution characteristics. This design will provide information on: (a) the relationship of static anthropometric measurements to dynamic eye location; (b) the difference between dynamic on-the-road eye location versus static in-the-lab eye location distributions: (c) the effect of different types of vehicle seating package parameters on eye location; and, (d) a validation of previous static eye location studies.