Publication

Optimization of electroacoustic absorbers by means of designed experiments

Hervé Lissek, Romain Boulandet
2010
Journal Articles

Abstract

In multivariate systems, when it comes to identifying actual operating conditions ranges, or optimal settings, the use of constrained optimization is often required. Among the different tools for the engineer to perform such optimization, designed experiments offer accurate performances. In this paper, the optimization process of “electroacoustic absorbers” is investigated by means of response surface methodology. A multivariate linear model is established by a series of designed experiments in order to analyze the modification of electroacoustic absorber performances due to the variation of several constitutive parameters (such as the moving mass of the loudspeaker, the enclosure volume, the filling density of mineral fiber within the enclosure, and the electrical load value to which the loudspeaker is connected), that influence their whole absorbing mechanisms. A simple case study is then provided to illustrate the capabilities of the developed optimization procedure, from which general conclusions on such design methodology, as well as on electroacoustic absorbers sensitivity, are drawn.

Official source

https://infoscience.epfl.ch/entities/publication/91873c3e-59bb-4054-bbc9-c1b5ecc31ad0

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Hervé Lissek, Romain Boulandet
2010
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/91873c3e-59bb-4054-bbc9-c1b5ecc31ad0

About this result

Related concepts (32)

Optimal design

In the design of experiments, optimal designs (or optimum designs) are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design.

Response surface methodology

In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The method was introduced by George E. P. Box and K. B. Wilson in 1951. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. Box and Wilson suggest using a second-degree polynomial model to do this. They acknowledge that this model is only an approximation, but they use it because such a model is easy to estimate and apply, even when little is known about the process.

Multi-objective optimization

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

Related publications (31)

Mesh-based topology, shape and sizing optimization of ribbed plates

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

Springer2024

Scalable constrained optimization

Maria-Luiza Vladarean

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024