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Objective and Perceptual Sound Quality Analysis of Internal Combustion Engine and Electric Vehicles
No full textThe sound quality of automotive interiors is one of the critical factors regarding customer satisfaction. As electric vehicles (EVs) rise in popularity rapidly, the known literature on sound qualities of internal combustion engine (ICE) automotive interiors has become less relevant. Because of this, comparing and contrasting EV and ICE vehicle\u27s sound qualities is extremely important to have the proper foundation for studying automotive noise quality in the future. In our study, we aim to benchmark the major differences between an EV and an ICE automobile regarding interior sound quality. This study aims to understand basic sound engineering characteristics and how they differ between the two types of vehicles. We also analyzed the preferences that the public has when it comes to the two types of cars. To get as much data as we could in our time-constrained project, we tested both types of vehicles in two different environments, which were an uncontrolled road (Bluff Street) and a controlled track (the GM Mobility Research Center). We also tested three different positions in the car, including the driver\u27s seat, passenger seat, and rear middle seat position. The interior sound was then recorded using the SQobold sound acquisition device and the Head Acoustics AACHEN Head as the microphone. Three recordings of every type of test were taken in order to confirm consistent and accurate results. We then compared and contrasted the data in ArtemisSuite, a sound analysis software. We determined the major differences between the cars, particularly in loudness and sharpness. The final step was jury testing, in which the subjective samples compare well with our conclusions regarding sound quality metrics
Noninvasive identification of directionally-dependent elastic properties of soft tissues using full-field optical data
No full textThis paper introduces an innovative approach for elastic property characterization of soft tissues, having directional-dependent material behavior, via their vibration response measurement and interpretation. The full-field time-dependent surface displacements as a result of externally excited soft tissues are collected through digital image correlation (DIC). A developed analytical model, capturing the low-amplitude vibration behavior of anisotropic layered human skin with the incorporation of the influence of subcutaneous elasticity and inertia, is employed to accurately predict its resonant frequencies and pertaining displacement field images. An efficient solution approach for the model is implemented into an inverse algorithm to rapidly characterize the anisotropic elastic properties based on importing the vibration characteristics. To show the merit of the approach, a 3-D finite element (FE) simulation model was used to generate full-field data, detected and matched with a set of specific vibration modes via modal assurance criterion (MAC). The validity of the model implemented into the inverse characterization algorithm is demonstrated through a comparison of predicted vibration frequencies and mode-shapes simulated via the 3-D FE model for different cases with anisotropic elastic properties in different layers of the skin. It is shown that modes are influenced differently when anisotropic properties are introduced to the model. Thus, the established inverse characterization algorithm is capable of rapidly predicting the elastic material properties of anisotropic soft sheets with adequate accuracy