A Structural-Acoustic Vehicle System Model for Noise and Vibration Analysis

2003 ◽  
Author(s):  
Shung H. Sung ◽  
Donald J. Nefske ◽  
Douglas A. Feldmaier ◽  
Spencer J. Doggett
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Exhaust System ◽  
Element Model ◽  
Interior Noise ◽  
System Model ◽  
Structural Vibration ◽  
Vehicle Interior ◽  
Front Suspension ◽  
Vehicle System

A structural-acoustic finite-element model of a sedan-type automotive vehicle is developed and experimentally evaluated for predicting vehicle interior noise and structural vibration. The vehicle system model is developed from finite-element models of the major structural subsystems, which include the trimmed body, front suspension, rear suspension, powertrain and exhaust system. An acoustic finite-element model of the passenger compartment cavity is coupled with the vehicle system model to predict the interior noise response. The predicted interior noise and structural vibration by the vehicle system model are compared with the measured responses for shaker excitation at the axle to 200 Hz. The comparisons demonstrate the accuracy of the structural-acoustic vehicle system model, and they indicate where modeling improvements are required.

Identification of Dynamic Rates of Elastomeric Vibration Isolators in Automotive Vehicles

2004 ◽  
Author(s):  
Donald J. Nefske ◽  
Shung H. (Sue) Sung ◽  
Douglas A. Feldmaier
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Dynamic Stiffness ◽  
Element Model ◽  
Interior Noise ◽  
Rear Suspension ◽  
Model Comparisons ◽  
Vibration Isolators ◽  
Vehicle System ◽  
Stiffness And Damping

Dynamic stiffness and damping rates of elastomeric vibration isolators used in automotive vehicles are identified from static isolator tests and the use of an isolator finite element model. Comparisons are made of the predicted versus measured dynamic stiffness and damping rates from 0 to 300 Hz of a rear suspension isolator to validate the technique. The identified dynamic rates of the elastomeric isolators of a representative vehicle are then input to the vehicle system finite-element model to compare the predicted versus measured vehicle vibration and interior noise response for laboratory shaker excitation.

A Structural-Acoustic Finite Element Method for Predicting Automobile Vehicle Interior Road Noise

2009 ◽  
Author(s):  
Shung H. Sung ◽  
Donald J. Nefske ◽  
Douglas A. Feldmaier
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
Interior Noise ◽  
Passenger Compartment ◽  
Road Noise ◽  
Vehicle Interior ◽  
Contact Points ◽  
Power Spectral ◽  
Structural Acoustic Model

A structural-acoustic finite element model of an automotive vehicle is developed and experimentally evaluated for predicting the structural-borne interior noise in the passenger compartment when the vehicle travels over a randomly rough road at a constant speed. The structural-acoustic model couples a structural finite element model of the vehicle with an acoustic finite element model of the passenger compartment. Measured random road profile data provides the prescribed power spectral density excitation applied at the tire-patch contact points to predict the structural-borne interior road noise. Comparisons of the predicted and measured interior noise for laboratory shaker excitation, tire patch excitation, and vehicle travel over a randomly rough road are used to assess the accuracy of the model.

Statistical Vibration Based Damage Identification Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Monte Carlo ◽  
Finite Element Model ◽  
Element Model ◽  
Point Estimation ◽  
Structural Vibration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) telah digunakan dengan meluas bagi tujuan mengesan kerosakan dalam struktur menggunakan data–data mod dari gegaran. Walau bagaimanapun, ketidakpastian yang wujud dalam model unsur terhingga dan data dari lapangan yang tidak dapat dielakkan boleh menyebabkan kesilapan dalam meramalkan magnitud dan lokasi kerosakan. Dalam kajian ini kaedah statistik digunakan untuk mengambil kira ketidakpastian ini. ANN digunakan untuk meramalkan parameter–parameter kekukuhan dari frekuensi dan mod bentuk bagi sesebuah struktur. Untuk mengambil kira ketidakpastian dalam ramalan, kaedah statistik digunakan di mana kaedah Rossenblueth point estimation diperbandingkan dengan kaedah Monte Carlo diaplikasikan bagi mengambil kira ketidakpastian ini. Keputusan menunjukkan bahawa dengan mengambil kira ketidakpastian dalam membuat ramalan menggunakan ANN, kerosakan boleh diramalkan pada tahap keyakinan yang tinggi. Kata kunci: Artificial neural network; ketidakpastian; kesilapan rawak Artificial Neural Network (ANN) has been widely applied to detect damages in structures based on structural vibration modal parameters. However, uncertainties that inevitably exist in finite element model and measured vibration data might lead to false or unreliable prediction of structural damage. In this study, a statistical approach is proposed to include the effect of uncertainties in the ANN algorithm for damage prediction. ANN is used to predict the stiffness parameters of structures from measured structural vibration frequencies and mode shapes. Uncertainties in the measured data and finite element model of the structure are considered in the prediction. The statistics of the identified parameters are determined using Rossenblueth’s point estimation method and verified by Monte Carlo simulation. The results show that by considering these uncertainties in the ANN model, the damages can be detected with a higher confidence level. Key words: Artificial neural network; uncertainties; random error

Modeling and Control of a Deformable Mirror

2002 ◽  
Vol 124 (2) ◽  
pp. 297-302 ◽  
Author(s):  
S. E. Winters ◽  
J. H. Chung ◽  
S. A. Velinsky
Keyword(s):  
Finite Element ◽  
Control System ◽  
Finite Element Model ◽  
Adaptive Optics ◽  
Element Model ◽  
Surface Shape ◽  
System Model ◽  
Deformable Mirror ◽  
Wavefront Aberration ◽  
Control System Model

A multi-input multi-output surface shape control system model is developed to study wavefront aberration correction. The plant model represents a deformable mirror and utilizes a finite element model that is validated using an actual prototype deformable mirror and an interferometer system. The sensor model is based on a Shack-Hartmann sensor, and the controller model is based on a least squares approach. The control system model is used to compare an available Gaussian model with the validated finite element model. Results clearly show the efficacy of the approach and the superiority of the finite element based method. The control approach is expected to be implemented on the adaptive optics system of the National Ignition Facility.

Validation of an Acoustic Finite Element Model of an Automobile Passenger Compartment

2009 ◽  
Author(s):  
Sangyun Lee ◽  
Kwangseo Park ◽  
Shung H. Sung ◽  
Donald J. Nefske
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Sound Pressure ◽  
Element Model ◽  
Rear Seat ◽  
Pressure Response ◽  
Mode Shapes ◽  
Instrument Panel ◽  
Passenger Compartment ◽  
Vehicle Interior

An acoustic finite-element model of an automobile passenger compartment that represents the more complicated vehicle interior acoustic characteristics is developed and experimentally assessed using loudspeaker excitation. The acoustic finite-element model represents the passenger compartment cavity, trunk compartment cavity, front and rear seats, parcel shelf, door volumes, and IP (Instrument Panel) volume. The model accounts for the coupling between the compartment cavity and trunk cavity through the rear seat and parcel shelf, and the coupling between the compartment cavity and the door and IP panel volumes. Modal analysis tests of a vehicle were conducted using loudspeaker excitation to identify the compartment cavity modes and sound pressure response at a large number of interior locations. Comparisons of the predicted versus measured mode frequencies, mode shapes, and sound pressure response at the occupant ear locations are made to assess the accuracy of the model to 400 Hz.

scholarly journals DEVELOPMENT OF A DESIGN METHOD FOR DETERMINING THE ATTACHMENT OF THE EXHAUST MOUNTING MOUNTS

Akustika ◽  
2019 ◽  
Vol 34 ◽  
pp. 141-147
Author(s):  
Rakhmatjon Rakhmatov ◽  
Vitaliy Krutolapov ◽  
Valeriy Zuzov
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Strain Energy ◽  
Natural Frequencies ◽  
Design Method ◽  
Exhaust System ◽  
Element Model ◽  
Vehicle Body ◽  
Vibration Modes ◽  
The Finite Element Model

The article presents the developed method of determining the attachment points of the mounts of the exhaust system to the vehicle body. The requirements for the construction of a finite element model of the exhaust system are presented, the finite element model of the exhaust system is created, the results of natural frequencies and vibration modes and the strain energy of the structure are shown.

A Coupled Structural-Acoustic Finite Element Model for Vehicle Interior Noise Analysis

1984 ◽  
Vol 106 (2) ◽  
pp. 314-318 ◽  
Author(s):  
S. H. Sung ◽  
D. J. Nefske
Keyword(s):  
Finite Element ◽  
Noise Analysis ◽  
Coupled Model ◽  
Element Model ◽  
Interior Noise ◽  
Vehicle Interior ◽  
Noise Sources ◽  
Acoustic Cavity ◽  
Vehicle Interior Noise ◽  
Vehicle Structure

An analytical method is developed for predicting vehicle interior noise and identifying noise sources. In this method, the finite element models representing the vehicle structure and its enclosed acoustic cavity are coupled mathematically. A modal formulation is employed to solve for the interior acoustic response, and an analysis is developed to identify the structural and acoustic modal participation as well as the boundary panel participation in producing the response. As an example application, a coupled model of an automotive vehicle is presented and experimentally evaluated. The modal and panel participations are identified from the results.

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