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It is well known that the ride quality of trucks is much harsher than that of automobiles. Additionally, truck drivers typically drive trucks for much longer duration than automobile drivers. These two factors contribute to the fatigue that a truck driver typically experiences during long haul deliveries. Fatigue reduces driver alertness and increases reaction times, increasing the possibility of an accident. One may conclude that better ride quality contributes to safer operation. The secondary suspensions of a tractor have been an area of particular interest because of the considerable ride comfort improvements they provide. A gap exists in the current engineering domain of an easily configurable high fidelity low computational cost simulation tool to analyze the ride of a tractor semi-trailer. For a preliminary design study, a 15 d.o.f. model of the tractor semi-trailer was developed to simulate in the Matlab/Simulink environment.

Track testing of race cars is expensive and racing series typically limit the amount of testing that can be done on circuit tracks. Because of this, we saw the need to develop a computer model that could simulate a car on a track with any specified surface roughness and with aerodynamic loading acting on the vehicle. This model allows an analysis of the effect of aerodynamic loading on the vertical dynamic response of the vehicle. Vehicle parameters specific to an IMSA GTP car including aerodynamic data from wind tunnel testing and nonlinear shock characteristics were used in this study. Simulations were run for various speeds and ride height configurations and it was found that very small changes in the static settings of the front and rear ride heights can lead to large differences in the resulting ride heights at speed. This can be attributed to the variations in the nonlinear aerodynamic loading as the ride height and speed of the vehicle change.

A computational methodology has been developed for loss prediction in intake regions of internal combustion engines. The methodology consists of a hierarchy of four major tasks: (1) proper computational modeling of flow physics; (2) exact geometry and high quality and generation; (3) discretization schemes for low numerical viscosity; and (4) higher order turbulence modeling. Only when these four tasks are dealt with properly will a computational simulation yield consistently accurate results. This methodology, which is has been successfully tested and validated against benchmark quality data for a wide variety of complex 2-D and 3-D laminar and turbulent flow situations, is applied here to a loss prediction problem from industry. Total pressure losses in the intake region (inlet duct, manifold, plenum, ports, valves, and cylinder) of a Caterpillar diesel engine are predicted computationally and compared to experimental data.