Determination of Interior NVH Levels from Tire/Wheel Variations using a Monte Carlo Process

Qatu, Mohamad S.; King, Roger L.; Shubailat, Omar; Wheeler, Rachel

doi:10.4271/2011-01-1580

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…In the former case, for example, the sound is physically emitted from an exhaust pipe and transmitted. In the latter case, acoustic energy generated from a vibration source such as an engine moves through the rigid vehicle body and then flows into the interior as noise and vibration or is generated by road stimuli transmitted through tires and suspension systems [ 12 , 13 ]. As such, noise and vibration form a complex relationship, and nonlinear elements act strongly.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis of Noise, Vibration, and Harshness in a Vehicle Using Driving Data Based on Big Data Analysis Technique

Song

Seong-Eun

Seo

et al. 2022

Sensors

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A new development process for the noise, vibration, and harshness (NVH) of a vehicle is presented using data analysis and machine learning with long-term NVH driving data. The process includes exploratory data analysis (EDA), variable importance analysis, correlation analysis, sensitivity analysis, and development target selection. In this paper, to dramatically reduce the development period and cost related to vehicle NVH, we propose a technique that can accurately identify the precise connectivity and relationship between vehicle systems and NVH factors. This new technique uses whole big data and reflects the nonlinearity of dynamic characteristics, which was not considered in existing methods, and no data are discarded. Through the proposed method, it is possible to quickly find areas that need improvement through correlation analysis and variable importance analysis, understand how much room noise increases when the NVH level of the system changes through sensitivity analysis, and reduce vehicle development time by improving efficiency. The method could be used in the development process and the validation of other deep learning and machine learning models. It could be an essential step in applying artificial intelligence, big data, and data analysis in the vehicle and mobility industry as a future vehicle development process.

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Section: Introductionmentioning

confidence: 99%

Correlation Analysis of Noise, Vibration, and Harshness in a Vehicle Using Driving Data Based on Big Data Analysis Technique

Song

Seong-Eun

Seo

et al. 2022

Sensors

View full text Add to dashboard Cite

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Gaussian Processes for Transfer Path Analysis Applied on Vehicle Body Vibration Problems

Gurbuz

Eser

Schmid

et al. 2022

SAE Int. J. Adv. & Curr. Prac. in Mobility

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<div class="section abstract"><div class="htmlview paragraph">Transfer path analyses of vehicle bodies are widely considered as an important tool in the noise, vibration and harshness design process, as they enable the identification of the dominating transfer paths in vibration problems. It is highly beneficial to model uncertain parameters in early development stages in order to account for possible variations on the final component design. Therefore, parameter studies are conducted in order to account for the sensitivities of the transfer paths with respect to the varying input parameters of the chassis components. To date, these studies are mainly conducted by performing sampling-based finite element simulations. In the scope of a sensitivity analysis or parameter studies, however, a large amount of large-scale finite element simulations is required, which leads to extremely high computational costs and time expenses. This contribution presents a method to drastically reduce the computational burden of typical sampling-based simulations. For this purpose, Gaussian processes are introduced to find a probabilistic function approximation of the transfer paths. Initial results reveal that a wider solution space can be explored by only observing a few transfer path samples. This entails a time-efficient and robust technique, which inherently incorporates the variability of the input parameters. As such, Gaussian processes offer a versatile solution strategy for transfer path analyses, where simulation data as well as experimental measurements can be holistically investigated.</div></div>

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Determination of Interior NVH Levels from Tire/Wheel Variations using a Monte Carlo Process

Cited by 2 publications

References 15 publications

Correlation Analysis of Noise, Vibration, and Harshness in a Vehicle Using Driving Data Based on Big Data Analysis Technique

Correlation Analysis of Noise, Vibration, and Harshness in a Vehicle Using Driving Data Based on Big Data Analysis Technique

Gaussian Processes for Transfer Path Analysis Applied on Vehicle Body Vibration Problems

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