Utilizing spectral analysis to quantify resolution of low frequency behavior in testing commercial vehicles

Despite the recent broadening of acceptable test methods for certifying aerodynamic performance, there has been little attention on how to determine the time averaging window used for providing mean forces. This is of particular relevance to the assessment of commercial vehicles as they are significantly affected by low-frequency patterns that are hard to predict and vary with different geometry configurations. Published guidelines in the industry suggest that good engineering judgement be used and a qualitative assessment of force histories is adequate. These suggested methods leave the accuracy of the time averaging to the experience and judgement of the user and is highly dependent on the specific characteristics of the benchmark case. Furthermore these methods are not able to quantify the error present due to motions slower than length of the sampled data. In order to robustly determine appropriate averaging window a new method is proposed which utilizes spectral analysis of the drag. By investigating changes in the power spectral density for different averaging windows it is possible to determine adequate averaging time as well as to assess the error introduced by selecting a particular averaging window. The method detailed presently is applied to both wind tunnel measurements and computation fluid dynamics simulations.