Performance Evaluation and Effectiveness of the Reverberation Room

Abd-Elbasseer, Mohamed; Mohamed, Hatem Kh

doi:10.32604/sv.2021.09417

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The optimized parameter results are shown in Table 3, and the multi-objective prediction results and measured results obtained according to the optimized parameters are shown in Table 4. According to Table 4, the sound pressure level [39], cost, and weight of the vehicle interior noise in the original state at 1250 Hz are 59.6 dB, 502 yuan, and 251 kg, respectively. The sound pressure level of the vehicle interior noise optimized based on the multi-level architecture model is reduced by 2.4 dB.…”

Section: Optimization and Verification Of Acoustic Package Solutionsmentioning

confidence: 99%

Multi-Objective Prediction and Optimization of Vehicle Acoustic Package Based on ResNet Neural Network

Wu,

Liu,

Huang

et al. 2023

Sound&Vibration

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Vehicle interior noise has emerged as a crucial assessment criterion for automotive NVH (Noise, Vibration, and Harshness). When analyzing the NVH performance of the vehicle body, the traditional SEA (Statistical Energy Analysis) simulation technology is usually limited by the accuracy of the material parameters obtained during the acoustic package modeling and the limitations of the application conditions. In order to effectively solve these shortcomings, based on the analysis of the vehicle noise transmission path, a multi-level objective decomposition architecture of the interior noise at the driver's right ear is established. Combined with the data-driven method, the ResNet neural network model is introduced. The stacked residual blocks avoid the problem of gradient disappearance caused by the increasing network level of the traditional CNN network, thus establishing a higherprecision prediction model. This method alleviates the inherent limitations of traditional SEA simulation design, and enhances the prediction performance of the ResNet model by dynamically adjusting the learning rate. Finally, the proposed method is applied to a specific vehicle model and verified. The results show that the proposed method has significant advantages in prediction accuracy and robustness.

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Section: Optimization and Verification Of Acoustic Package Solutionsmentioning

confidence: 99%

Multi-Objective Prediction and Optimization of Vehicle Acoustic Package Based on ResNet Neural Network

Wu,

Liu,

Huang

et al. 2023

Sound&Vibration

View full text Add to dashboard Cite

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Tuning of Estimated Sound Absorption Coefficient of Materials of Reverberation Room Method

Tang

Chuang

2022

Shock and Vibration

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Acoustical materials are usually used for architectural design, ambient noise, and traffic noise to absorb sound. The sound absorption coefficient (SAC) is often used as the performance index of acoustical materials. It is measured using the reverberation room method (RRM) and the impedance tube method (ITM). The sound source of the RRM is a random incidence in various directions, whereas that of the ITM is a normal incident. The SAC measured by the RRM is more extensive, practical, and costly than ITM. Usually, the SAC measured by RRM is used as the commercially available acoustical materials index. However, RRM requires large-area test samples for measurement and a special sound field of reverberation room for test experiments. Therefore, this study aims to use the ITM to measure SAC. The measured SAC, frequency, thickness, and material density were employed as independent variables and substituted in the obtained multiple regression model to predict SAC obtained by RRM. In addition, the measurement characteristics of impedance tubes provide varisized calibers for different frequency ranges to measure a relatively accurate SAC. Based on the abovementioned characteristics, this study proposed a dual-model for estimating the SAC of the reverberation room method to achieve the best-estimated result. After experimental validation, the dual-model estimated result was compared with SAC from the reverberation room method. It was observed that the maximum absolute value of all errors did not exceed 0.16. Most of the absolute errors were below 0.1, proving the accuracy of this method. Therefore, the method proposed in this study can shorten the acoustical material development schedule for manufacturers and save the cost of the development process.

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Renovation of the reverberation room at Ocean University of China based on ODEON simulation

Shi,

Lin

2024

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The reverberation room is a crucial laboratory for determining the sound absorption properties of materials. The reverberation room at the Laoshan Campus of Ocean University of China lacks complete acoustic design. Using computer-aided simulations, defects in the sound field can be clearly revealed and the design proposals’ effectiveness can be pre-validated. This study focuses on renovating the reverberation room by conducting acoustic performance tests and constructing a three-dimensional model. Leveraging the advantages of ODEON for simulating sound fields, it analyzes the uniformity of sound field diffusion, considering not only reverberation time distribution but also linear decay curves and deviations in sound pressure level distribution. Subsequently, the room undergoes simulated renovation in accordance with national standards of China and international standards, and diffusion body design schemes, including semi-cylindrical and semi-conical hard wall diffusers and suspended plexiglass diffusers, are compared through simulations to arrive at the final optimized renovation plan. The simulations show that the deviation in sound field uniformity can be reduced to less than 1.5 dB after renovation, and the reverberation time is also significantly extended. These findings can inform the actual renovation of the Laoshan Campus reverberation room and serve as a reference for other reverberation room renovations and indoor acoustic simulations.

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Performance Evaluation and Effectiveness of the Reverberation Room

Cited by 3 publications

References 5 publications

Multi-Objective Prediction and Optimization of Vehicle Acoustic Package Based on ResNet Neural Network

Multi-Objective Prediction and Optimization of Vehicle Acoustic Package Based on ResNet Neural Network

Tuning of Estimated Sound Absorption Coefficient of Materials of Reverberation Room Method

Renovation of the reverberation room at Ocean University of China based on ODEON simulation

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