Interior noise analysis of a construction equipment cabin based on airborne and structure-borne noise predictions

Kim, Sunghee; Hong, Suk‐Yoon; Song, Jee-Hun; Joo, Won-Ho

doi:10.1007/s12206-012-0231-z

Cited by 15 publications

(12 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The agreement of their numerical and experimental results validated their assumption. Kim et al [2012] and demonstrated similarly on construction vehicles and bus respectively. proposed the use of structural intensity to visualize vibrational power flow in the encompassing panels of a scaled automobile in the form of streamlines; analogous to that used in fluid dynamics for fluid flow studies.…”

Section: Finite Element Methods and Boundary Element Methodsmentioning

confidence: 77%

Acoustic Metamaterials: A Potential for Cabin Noise Control in Automobiles and Armored Vehicles

Ang

Koh²,

Lee

2016

Int. J. Appl. Mechanics

View full text Add to dashboard Cite

The aim of this paper is to provide an overview of the existing industrial practices used for cabin noise control in various industries such as automotive, marine, aerospace, and defense. However, emphasis is placed on automobiles and armored vehicles. Generally, automobile cabins usually constitute of thin structural panels, where the fundamental frequency typically falls below 200[Formula: see text]Hz. If a specific structural mode couples with a specific acoustic mode of the cabin, booming noise occurs. As such, discomfort may be felt by the occupants. Fundamentally, vibroacoustics problems may be minimized if the acoustic modes and the structural modes are decoupled, which is achieved usually by structural modifications or acoustical treatments. However, if excessively performed, the weight limitation of an automobile design will be exceeded; not to mention the adverse effect of increased weight on several factors such as fuel efficiency, mileage life of tires and acceleration of the vehicle. Moreover, current solutions have several drawbacks in low frequency noise control. In light of this, it is of great interest to explore the feasibility of acoustic metamaterials as an alternative with hope to improve cabin noise.

show abstract

Section: Finite Element Methods and Boundary Element Methodsmentioning

confidence: 77%

Acoustic Metamaterials: A Potential for Cabin Noise Control in Automobiles and Armored Vehicles

Ang

Koh²,

Lee

2016

Int. J. Appl. Mechanics

View full text Add to dashboard Cite

show abstract

“…where y is the acoustic annoyance; x j (j = 1,2,...,7) stands for the psycho-acoustic objective parameters; the meanings of w 1 ij , v 1 i and other parameters are the same as that of Equation (5).…”

Section: Mathematical Modeling and Predictionmentioning

confidence: 99%

Nonlinear modeling and prediction of forklift acoustic annoyance based on the improved neural networks

et al. 2021

View full text Add to dashboard Cite

Aiming at the characteristics of high decibels and multiple samples for forklift noise, a subjective evaluation method of rank score comparison (RSC) based on annoyance is presented. After pre-evaluation, comprehensive evaluation and data tests on collected 50 noise samples, the annoyance grades of all noise samples were obtained, and seven psycho-acoustic parameters including linear sound pressure level (LSPL), A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, impulsiveness and articulation index (AI) were determined by correlation calculation. Considering the nonlinear characteristics of human ear subjective perception, objective parameters, and annoyance were used as input and output variables correspondingly and then three nonlinear mathematical models of forklift acoustic annoyance were established using traditional artificial neural network (ANN), genetic-algorithm neural network (GANN), and particle-swarm-optimization neural network (PSONN). Moreover, the prediction accuracy of the three models was tested and compared by sample data. The results indicate that the average relative error (ARE) between the experimental and predicted values of acoustic annoyance based on PSONN model is 3.893%, which provides an effective technical support for further optimization and subjective evaluation.

show abstract

“…However, the concerned frequency is from 20 to 200 Hz given that this frequency range covers the typical running frequency and is the main frequency band of cab structure-borne noise. 18,19 When sampling vibration acceleration and sound pressure, the recording time is set at 30 s and the test is repeated thrice. The measurements of the vibration accelerations and sound pressure are illustrated in Figure 4.…”

Section: Cab Interior Noise Prediction and Analysismentioning

confidence: 99%

Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

Zhi-yong

Zhang

Huang

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

Structure design for reducing noise is essential and widely studied because cab interior noise seriously affects the ride comfort of the driver and passengers, especially for heavy commercial vehicles. This paper proposes an enhancement investigation on the low-noise structure performance of a cab in a heavy commercial vehicle based on an approximation model. A series of vibroacoustic tests with varying running speeds are implemented first to acquire the vibration signals at four cab suspensions and the sound pressure signal at the right ear of the driver. The finite element model and structure-acoustic coupled model of the cab of a heavy commercial vehicle are established sequentially. When the vibration accelerations measured in the tests are converted into excitation signals using the cab structure-acoustic coupled model, the sound pressure of the right ear of the driver is predicted, and the accuracy of the cab structureacoustic coupled model is then verified. After panel acoustic contribution analysis, an approximation model of critical panel thickness and the peak noise near the right ear of the driver is established in accordance with the radial basis function. Genetic algorithm is utilized to solve an optimization model to obtain optimal panel thicknesses. By comparing the right ear sound pressure before and after optimization, the results confirm that optimized panel thicknesses can reduce sound pressure level of the critical frequency.

show abstract

Interior noise analysis of a construction equipment cabin based on airborne and structure-borne noise predictions

Cited by 15 publications

References 4 publications

Acoustic Metamaterials: A Potential for Cabin Noise Control in Automobiles and Armored Vehicles

Acoustic Metamaterials: A Potential for Cabin Noise Control in Automobiles and Armored Vehicles

Nonlinear modeling and prediction of forklift acoustic annoyance based on the improved neural networks

Low-noise structure optimization of a heavy commercial vehicle cab based on approximation model

Contact Info

Product

Resources

About