2020
DOI: 10.1007/s00158-020-02598-0
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Topology optimization of binary structures under design-dependent fluid-structure interaction loads

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Cited by 47 publications
(39 citation statements)
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“…Sci. 2021, 11, x FOR PEER REVIEW 3 of 17 vapuram and Picelli further extended the TOBS method to design topologies by considering fluid-structure interaction loads [29], pressure, and thermal loads simultaneously [30].…”
Section: Basic Configuration Of a Pmpicmentioning
confidence: 99%
See 1 more Smart Citation
“…Sci. 2021, 11, x FOR PEER REVIEW 3 of 17 vapuram and Picelli further extended the TOBS method to design topologies by considering fluid-structure interaction loads [29], pressure, and thermal loads simultaneously [30].…”
Section: Basic Configuration Of a Pmpicmentioning
confidence: 99%
“…The current challenge for structural topology optimization is to develop reliable techniques to account for different physics interactions. In 2020, Sivapuram and Picelli further extended the TOBS method to design topologies by considering fluid-structure interaction loads [29], pressure, and thermal loads simultaneously [30].…”
Section: Introductionmentioning
confidence: 99%
“…For a pure CHT problem, another approach using a residual-based formulation for the PDE solvers and subsequent derivation of the discrete adjoint equations is presented in Makhija and Beran (2019), it also incorporates density-based topology optimization variables into the problem setting, which in the SU2 framework is only currently supported for FSI problems (Gomes and Palacios 2020). Indeed, the differentiation of multiphysics solvers for topology optimization is common (Dunning et al 2015;Lundgaard et al 2018;Picelli et al 2020). However, in such applications solvers tend to be of the monolithic type (for example so that locations of the domain can be either fluid or solid), or the discrete adjoint methodology is developed specifically for the primal methods used.…”
Section: Introductionmentioning
confidence: 99%
“…For clarity, a monolithic FSI or CHT solver as used for topology optimization [e.g., as in Lundgaard et al (2018)] would be classified as one solver and thus occupy one zone. However, when the part of the domain being designed is much smaller than the surrounding fluid volume, it could be reasonable to model the problem as two partially overlapping zones as done in Picelli et al (2020), and then track the interface as it develops. Such strategies are not yet available in the SU2 framework due to its aeronautical background, characterized by high Reynolds number applications for which body-fitted meshes and shape optimization are more common than immersed boundary and topology optimization.…”
Section: Introductionmentioning
confidence: 99%
“…Extensive research has been conducted to establish various topology optimization methods, including the solid isotropic material with penalty (SIMP) method, 8 level set method (LSM), 9 evolutionary structure optimization (ESO) method, 10 moving morphable components (MMC) method, 11 and discrete variable topology optimization methods. [12][13][14][15][16][17] The discrete variable method recently proposed by Liang and Cheng 12,13 utilizes the sequential approximate integer programming (SAIP) and canonical relaxation algorithm to solve optimum design for structural compliance minimization, thermal compliance minimization, multiple displacement constraints, and thousands of local infill constraints. Later, the nonlinear trust-region constraint 18 that stems from nonlinear mathematical programming and improves the algorithmic local convergence is well integrated into the SAIP algorithm.…”
Section: Introductionmentioning
confidence: 99%