A Comparative Study of a Hybrid Experimental–Statistical Energy Analysis Model with Advanced Transfer Path Analysis for Analyzing Interior Noise of a Tiltrotor Aircraft

Sohrabi, Shahin; Segura Torres, Amadeu; Cierco Molins, Ester; Perazzolo, Alessandro; Bizzarro, Giuseppe; Rodríguez Sorribes, Pere Vicenç

doi:10.3390/app132212128

Cited by 1 publication

(1 citation statement)

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“…This type of model also provides research methods for the design and development of vibration, noise, power, power, and handling stability of transportation and vehicles. Shahin et al [8] used an experimental-SEA (Statistical Energy Analysis) hybrid model to calculate and analyze the excessive structural noise generated during aircraft flight. Zhang et al [9] summarized an interior noise acoustic design process based on railway research and 'Fuxing' R & D experience, and using experiments, they proved that the process achieved the design goal of a noise reduction of 3 dB.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Sun,

Dai,

et al. 2024

Electronics

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The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.

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Section: Introduction 1backgroundmentioning

confidence: 99%