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In this work, an approach is proposed that can perform a nondestructive evaluation of wind turbine structures using a non-contact, three-dimensional full-field optical digital image correlation (DIC) technique. This approach can quantify the level of strain and loading conditions that rotating structures such as wind turbines experience during operation. The optical technique does not interfere with the structural functionality of the wind turbine. Moreover, the use of Unmanned Aerial Vehicles (UAVs) for remote inspection enables robust measurements for periodic inspection. The strain obtained using the proposed approach is validated using strain gauges mounted on the blades. A control algorithm is designed for the UAV to stabilized and obtain the desired field of interest and working distance based on the turbine size. Blending the benefits of the remote accessibility of UAVs and full-field dynamic evaluation of structures using strain data obtained with DIC is a novel method of monitoring wind turbines.
In this work, an approach is proposed that can perform a nondestructive evaluation of wind turbine structures using a non-contact, three-dimensional full-field optical digital image correlation (DIC) technique. This approach can quantify the level of strain and loading conditions that rotating structures such as wind turbines experience during operation. The optical technique does not interfere with the structural functionality of the wind turbine. Moreover, the use of Unmanned Aerial Vehicles (UAVs) for remote inspection enables robust measurements for periodic inspection. The strain obtained using the proposed approach is validated using strain gauges mounted on the blades. A control algorithm is designed for the UAV to stabilized and obtain the desired field of interest and working distance based on the turbine size. Blending the benefits of the remote accessibility of UAVs and full-field dynamic evaluation of structures using strain data obtained with DIC is a novel method of monitoring wind turbines.
<div class="section abstract"><div class="htmlview paragraph">Data-driven modeling can help improve understanding of the governing equations for systems that are challenging to model. In the current work, the Sparse Identification of Nonlinear Dynamical systems (SINDy) is used to predict the dynamic behavior of dynamic problems for NVH applications. To show the merit of the approach, the paper demonstrates how the equations of motions for linear and nonlinear multi-degree of freedom systems can be obtained. First, the SINDy method is utilized to capture the dynamic behavior of linear systems. Second, the accuracy of the SINDy algorithm is investigated with nonlinear dynamic systems. SINDy can output differential equations that correspond to the data. This method can be used to find equations for dynamical systems that have not yet been discovered or to study current systems to compare with our current understanding of the dynamical system. With this amount of flexibility, SINDy can be used for NVH applications to help analyze vibration-related datasets as the study shows that SINDy results are consistent with ODE solutions. This study demonstrates how SINDy can accurately replicate mature known dynamical system models to highlight its potential to extract equations for more complex systems whose dynamic equations are challenging or impossible to obtain.</div></div>
<div class="section abstract"><div class="htmlview paragraph">The sound quality of automotive interiors is one of the critical factors regarding customer satisfaction. As electric vehicles (EVs) rapidly rise in popularity, the known literature on sound qualities of internal combustion engine (ICE) automotive interiors has become less relevant. Because of this, comparing and contrasting 'the sound qualities of EV and ICE vehicles is essential to have the proper foundation for studying automotive noise quality in the future. In this paper, we aim to benchmark the major differences between an EV and an ICE automobile regarding interior sound quality. This study seeks to understand basic sound engineering characteristics and how they differ between the two types of vehicles. We also analyzed the public's preferences when it comes to the two types of cars. To get as much data as possible in our time-constrained project, we tested both types of vehicles in two different environments: an uncontrolled road (Bluff Street in Flint, MI) and a controlled track (the GM Mobility Research Center - MRC - at Kettering University). We also tested three different positions in the car, including the driver's 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 <b>Artemis</b> SUITE<sup>TM</sup>, 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.</div></div>
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