Publication Date



Design of experiments has become an increasingly popular tool used by racing teams in professional motorsports. A race car has literally hundreds of adjustable parameters associated with it. Specialized racing engineers are in a constant search to find the optimal combination of settings for these parameters in order to optimize a given cars speed and performance potential for a given driver and race track. Many teams employ a wide variety of computer-based simulations and actual development testing to help optimize aspects of the car's performance, which in most cases is ultimately the minimum lap time for a given race track. Most team development however, requires a large amount of money and time. The purpose of this study was to develop predictive models and optimization methods for the mean pitch response of a race car, based on a virtual 7-post rig simulation software called Rigsim. In addition, chosen factors were varied in order to determine the effects and interactions of different factor configurations on mean pitch response. The primary factors of interest were the front and rear, low and high speed bump and rebound damper settings, as well as the front and rear tire stiffness. An initial fractional factorial DOE was generated to study the mean pitch response as a result of selected damper and tire stiffness settings. The results of the DOE were then used to create a model in order to help predict the mean pitch response for a given combination of damper and tire stiffness settings. The initial experiment was then augmented by a D-optimal response surface design in order to further explore the design space and predictive capabilities of the model. The DOE portion of the study utilized software named Design Expert for the experimental design, data analysis, optimization, and model creation. Optimization routines were employed to optimize the mean pitch response of the virtual Rigsim software, given a range of damper and tire stiffness settings. Optimal solutions were then compared to Rigsim simulations to gauge accuracy and determine the validity of the model. Design of experiments was shown to help effectively compile a significant amount of information with a relatively small subset of experiments. The model proved to be a fairly reasonable predictor of the simulation's mean pitch response within limits. Statistical analysis of the data helped determine significant effects and interactions involving mean pitch response, thus providing suggestions in order to focus on factors likely to improve mean pitch response. It appears to be most useful to study trends and comparisons between different damper and tire configurations. Ultimately, the approach to information gathering and modeling used in this study has potential to be highly useful in many aspects of race car engineering.

Degree Name


Level of Degree


Department Name

Mathematics & Statistics

First Committee Member (Chair)

Edward John Bedrick

Second Committee Member

Pedro Embid

Third Committee Member

Gabriel Huerta




Shock absorbers--Dynamics--Computer simulation, Automobiles, Racing--Chassis--Computer simulation, Experimental design.

Document Type