Topology optimization of reactive acoustic mufflers using a bi-directional evolutionary optimization method

Azevedo, Felipe Miranda; Moura, Márcio S.; Vicente, William; Picelli, Renato; Pavanello, Renato

doi:10.1007/s00158-018-2012-5

Cited by 24 publications

(10 citation statements)

References 42 publications

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“…The applications of topology optimization are very broad, i.e., multifunctional materials (Guest and Prévost 2006), aeroelasticity (Townsend et al 2018), biomedical design (Sutradhar et al 2010), heat transfer (Alexandersen et al 2016), and acoustics (Azevedo et al 2018), and others (Bendsøe 1995;Chakraborty et al 2019). Deaton and Grandhi (2014) identified design-dependent physics as Olhoff 2004;Zhang et al 2008;Lee and Martins 2012;Wang et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Topology optimization for design-dependent hydrostatic pressure loading via the level-set method

Picelli

Neofytou

Kim

2019

Struct Multidisc Optim

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A few level-set topology optimization (LSTO) methods have been proposed to address complex fluid-structure interaction. Most of them did not explore benchmark fluid pressure loading problems and some of their solutions are inconsistent with those obtained via density-based and binary topology optimization methods. This paper presents a LSTO strategy for designdependent pressure. It employs a fluid field governed by Laplace's equation to compute hydrostatic fluid pressure fields that are loading linear elastic structures. Compliance minimization of these structures is carried out considering the designdependency of the pressure load with moving boundaries. The Ersatz material approach with fixed grid is applied together with work equivalent load integration. Shape sensitivities are used. Numerical results show smooth convergence and good agreement with the solutions obtained by other topology optimization methods.

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Section: Introductionmentioning

confidence: 99%

Topology optimization for design-dependent hydrostatic pressure loading via the level-set method

Picelli

Neofytou

Kim

2019

Struct Multidisc Optim

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“…This heuristic method produces only discrete topologies, it has been improved over the past years, and it now constitutes a well-established branch of topology optimization methods (Huang and Xie 2007;Xia et al 2018a). It has been extended to a wide range of problems, among them: optimization considering multiple materials (Huang and Xie 2009); problems with displacement constraints (Huang and Xie 2010); problems with topology-dependent fluid pressure loads (Picelli et al 2015;Cunha and Pavanello 2017); multi-objective and multi-scale optimization (Yan et al 2015); problems with multi-scale non-linear structures (Xia and Breitkopf 2017); maximum stress minimization (Xia et al 2018b); frequency responses minimization (Vicente et al 2016); frequency gaps maximization (Lopes et al 2021); design of acoustic mufflers (Azevedo et al 2018); and design of piezoelectric energy harvesters with topology-dependent constraints (de Almeida et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Finite variation sensitivity analysis for discrete topology optimization of continuum structures

Cunha

Almeida

Lopes

et al. 2021

Struct Multidisc Optim

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This paper proposes two novel approaches to perform more suitable sensitivity analyses for discrete topology optimization methods. To properly support them, we introduce a more formal description of the Bi-directional Evolutionary Structural Optimization (BESO) method, in which the sensitivity analysis is based on finite variations of the objective function. The proposed approaches are compared to a naive strategy; to the conventional strategy, referred to as First-Order Continuous Interpolation (FOCI) approach; and to a strategy previously developed by other researchers, referred to as High-Order Continuous Interpolation (HOCI) approach. The novel Woodbury Sensitivity (WS) approach provides exact sensitivity values and is a better alternative to HOCI. Although HOCI and WS approaches may be computationally prohibitive, they provide useful expressions for a better understanding of the problem. The novel Conjugate Gradient Sensitivity (CGS) approach provides sensitivity values with arbitrary precision and is computationally viable for a small number of steps. The CGS approach is a better alternative to FOCI since, for appropriate initial conditions, it is always more accurate than the conventional strategy. The standard compliance minimization problem with volume constraint is considered to illustrate the methodology. Numerical examples are presented together with a broad discussion about BESO-type methods.

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“…This heuristic method produces only discrete topologies, it has been improved over the past years and it now constitutes a well established branch of topology optimization methods [8,9]. It has been extended to a wide range of problems, among them: optimization considering multiple materials [10]; problems with displacement constraints [11]; problems with topology-dependent fluid pressure loads [12,13]; multi-objective and multi-scale optimization [14]; problems with multi-scale non-linear structures [15]; maximum stress minimization [16]; frequency responses minimization [17]; frequency gaps maximization [18]; design of acoustic mufflers [19]; design of piezoelectric energy harvesters with topology-dependent constraints [20].…”

Section: Introductionmentioning

confidence: 99%

Finite Variation Sensitivity Analysis for Discrete Topology Optimization of Continuum Structures

Cunha,

de Almeida,

Lopes

et al. 2021

Preprint

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This paper proposes two novel approaches to perform more suitable sensitivity analyses for discrete topology optimization methods. To properly support them, we introduce a more formal description of the Bi-directional Evolutionary Structural Optimization (BESO) method, in which the sensitivity analysis is based on finite variations of the objective function. The proposed approaches are compared to a naive strategy; to the conventional strategy, referred to as First-Order Continuous Interpolation (FOCI) approach; and to a strategy previously developed by other researchers, referred to as High-Order Continuous Interpolation (HOCI) approach. The novel Woodbury approach provides exact sensitivity values and is a better alternative to HOCI. Although HOCI and Woodbury approaches may be computationally prohibitive, they provide useful expressions for a better understanding of the problem. The novel Conjugate Gradient Method (CGM) approach provides sensitivity values with arbitrary precision and is computationally viable for a small number of steps. The CGM approach is a better alternative to FOCI since, for appropriate initial conditions, it is always more accurate than the conventional strategy. The standard compliance minimization problem with volume constraint is considered to illustrate the methodology. Numerical examples are presented together with a broad discussion about BESO-type methods.

show abstract

Topology optimization of reactive acoustic mufflers using a bi-directional evolutionary optimization method

Cited by 24 publications

References 42 publications

Topology optimization for design-dependent hydrostatic pressure loading via the level-set method

Topology optimization for design-dependent hydrostatic pressure loading via the level-set method

Finite variation sensitivity analysis for discrete topology optimization of continuum structures

Finite Variation Sensitivity Analysis for Discrete Topology Optimization of Continuum Structures

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