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The projected frontal area of a vehicle has a significant impact on aerodynamic drag, and thus is an important parameter, for vehicle development, benchmarking, and modeling. However, determining vehicle frontal area can be tedious, time consuming, expensive, or inaccurate. Existing methods include analysis of engineering drawings, vehicle projections, 3D scanners, planimeter measurements from photographs, and estimations using vehicle dimensions. Currently accepted approximation methods can be somewhat unreliable. This study focuses on introducing a method to find vehicle frontal area using digital images and subtraction functions via MATLABs' Image Processing Toolbox. In addition to an overview of the method, this paper describes several variables that were examined to optimize and improve the process such as camera position, surface glare, and vehicle shadow effects.

Research Octane Number (RON) and Motor Octane Number (MON) have traditionally been used to describe fuel anti-knock quality. The test conditions for MON are harsher than those for RON, causing the RON for a particular fuel to be higher than the MON. Previous researchers have proposed the anti-knock performance of a fuel can be described at other operating conditions using the Octane Index (OI), defined as OI=RON-K (RON-MON), where ‘K’ is a weighing factor between RON and MON, and is a function of engine operating condition. The K-factor indicates that at a particular operating condition, knock tolerance is better described by RON as K approaches a value of 0, and MON as K approaches a value of 1. Previous studies claim that K-factor is dependent only on the engine combustion system and the speed-load point, and that it is independent of fuel chemistry. In most of these studies, K was determined experimentally using linear regression.