Finding the Best Design Within Limited Time: A Comparative Case Study on Methods for Optimization in Structural Acoustics

Ranjbar, Mostafa; Hardtke, H.‐J.; Fritze, Denny; Marburg, Steffen

doi:10.1142/s0218396x10004139

Cited by 22 publications

(8 citation statements)

References 26 publications

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“…These are in the standard variant often selected on the basis of experience and experimentation (since optimal values are problem dependent), which represents a significant deficiency in practice. Certainly, the optimization procedure based on deterministic analysis is also able to reduce the value of acoustic responses of structures [19][20][21][22] significantly.…”

Section: The Implementation Methods Of the Structural-acoustic Optimizmentioning

confidence: 99%

Reliability-Based Optimization of Coupled Structural-Acoustic System with Random Parameters

Wang¹,

Li²,

Ma³

et al. 2016

IJAV

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Structural noise is an important factor that endangers aircraft fatigue life and flight safety. It also has a negative effect on aircraft stealth performance and noise navigability. An optimal design of a structure-acoustic coupled system is an effective way to reduce noise and vibration. Due to the uncertainties that exist in the structural and acoustical parameters, the traditional deterministic optimization method may be unfeasible when the parameters are subject to fluctuations. This means that when the parameters are uncertain, the results obtained from the deterministic optimization method may be beyond their constraints. This paper proposes to apply the stochastic reliability-based optimization method to the design optimization of the coupled structural-acoustic system with random parameters. A comparison between the results of the stochastic reliability-based method, the safety factorbased method, and the deterministic method show that the first two methods can effectively consider the dispersion of the parameters.

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Section: The Implementation Methods Of the Structural-acoustic Optimizmentioning

confidence: 99%

Reliability-Based Optimization of Coupled Structural-Acoustic System with Random Parameters

Wang¹,

Li²,

Ma³

et al. 2016

IJAV

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“…Peak values influence the RMSL much more than the total sound power level. That is the reason that RMSL is adopted to be the objective function in optimization of the sound radiation in this study [42,[47][48].…”

Section: Vibro-acoustic Responsementioning

confidence: 99%

Enhancement of the vibro-acoustic performance of anti-tetra-chiral auxetic sandwich panels using topologically optimized local resonators

et al. 2021

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“…This is also known as the equivalent radiated sound power [20]. So, an equivalent radiated sound power level is considered as the objective function to be minimized.…”

Section: Optimization Proceduresmentioning

confidence: 99%

Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks

Ranjbar¹,

Marburg²

2013

IJMEA

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An artificial neural network (ANN) is adjusted to make analytical approximation of objective function for a specific structural acoustic application. It is used as the replacement of the main real objective function during the optimization process. The goal of optimization is to find the best geometry modification of the considered model which is supposed to produce lower values of the radiated sound power levels. The result of this study shows that the function approximation by neural networks can reduce the duration of optimization procedure. Furthermore, the tuning of ANN internal parameter settings is a real challenge to be considered.

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Finding the Best Design Within Limited Time: A Comparative Case Study on Methods for Optimization in Structural Acoustics

Cited by 22 publications

References 26 publications

Reliability-Based Optimization of Coupled Structural-Acoustic System with Random Parameters

Reliability-Based Optimization of Coupled Structural-Acoustic System with Random Parameters

Enhancement of the vibro-acoustic performance of anti-tetra-chiral auxetic sandwich panels using topologically optimized local resonators

Fast Vibroacoustic Optimization of Mechanical Structures Using Artificial Neural Networks

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