Planar Slip Condition For Mesh Morphing Using Radial Basis Functions

Aubert, S.; Mastrippolito, Franck; Rendu, Quentin; Buisson, Martin; Ducros, F.

doi:10.1016/j.proeng.2017.09.819

Cited by 3 publications

(9 citation statements)

References 16 publications

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“…The parametric model technique has been applied to rapidly modify parameterized finite element models in optimization fields, 34–37 according to mesh morphing technology. A parametric model is generally obtained by transforming a node location and without removing relational elements, while the model property and boundary conditions remain stable.…”

Section: Methodsmentioning

confidence: 99%

Multi-objective lightweight design of the container S-beam based on MNSGA-II with grey relational analysis

Wang

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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A novel bottom corrugated cross-beam (S-beam) structure improved the dynamic and static performance of a container based on the combination of a modified non-dominated sorting genetic algorithm (MNSGA-II) and grey relational analysis. First, a parametric model was established and used to verify the structure’s validity through static physical testing. Eight design variables for the S-beam container structure were also defined according to the parametric model technology. Second, MNSGA-II was used for the multi-objective lightweight optimization design of an S-beam container to obtain the optimal combination of design parameters that are considerably affected by weight reduction under peak bending stress and peak loading deflection as well as first-order natural frequency variations within the allowable range. A set of non-dominated solutions was used to obtain a multi-objective optimization design. Finally, grey relational analysis and grey entropy theory are applied to rank all solutions and determine the best compromise solution. The comparison of the technique for the order of preference by similarity to ideal solution method with grey relational analysis demonstrates the extraordinary reliability and superiority of the latter. In addition, the combined method can achieve a weight reduction of up to 23.54%, which can enhance the utilization of materials and demonstrates the superiority of the combined method relative to the initial model.

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Section: Methodsmentioning

confidence: 99%

Multi-objective lightweight design of the container S-beam based on MNSGA-II with grey relational analysis

Wang

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Usually, control points are located on mesh boundaries. The interpolated function is the displacement strue→ of a node, defined as the weighted sum of RBFs (Aubert et al , 2017) by: where truex→ is the initial (i.e. before deformation) node position, N c is the number of control points, ϕ is the chosen RBF, d is the chosen function measuring distance between two positions, truex→cj and trueγ→j are respectively the initial position and weight of the j- th control point.…”

Section: Mesh Morphing Formulationmentioning

confidence: 99%

“…As proposed by Aubert et al (2017), the set of control points is split into two ordered subsets, composed of moving control points (from 1 to N m ) followed by sliding control points (from N m +1 to N m + N s = N c ) Different conditions are associated with each subset (Aubert et al , 2017): the displacement strue→ must satisfy the interpolation condition for each moving control point: …”

Section: Mesh Morphing Formulationmentioning

confidence: 99%

“…This free mesh connectivity method is flexible enough to deal with structured or unstructured grids, meshing fluid or solid domains. The method produces robust transformations (Gillebaart et al , 2016) and high quality meshes (Aubert et al , 2017; Gillebaart et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Improvements have been proposed to speed up the propagation step through a memory formulation and parallelization (Gillebaart et al , 2016). The weight calculation speed-up has been achieved through the reduction of the number of control points with a greedy algorithm (Aubert et al , 2017; Gillebaart et al , 2016; Jakobsson and Amoignon, 2007; Rendall and Allen, 2009). This algorithm builds a quasi-optimal subset of control points used to morph the mesh, reducing considerably the propagation step.…”

Section: Introductionmentioning

confidence: 99%

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RBF-based mesh morphing improvement using Schur complement applied to rib shape optimization

Mastrippolito

Aubert

Ducros

et al. 2019

HFF

Self Cite

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Purpose This paper aims to improve the radial basis fuction mesh morphing method. During a shape optimization based on computational fluid dynamic (CFD) solvers, the mesh has to be changed. Two possible strategies are re-meshing or morphing. The morphing one is advantageous because it preserves the mesh connectivity, but it must be constrained. Design/methodology/approach RBF mesh deformation is one of the most robust and accurate morphing method. Using a greedy algorithm, the computational cost of the method is reduced. To evaluate the morphing performances, a rib shape optimization is performed using the NSGA-II algorithm coupled to kriging metamodels based on CFD. The morphing method is then compared to a re-meshing strategy. Findings The authors propose a method, based on Schur complement, to speed-up the greedy process. By using the information of the previous iteration, smaller linear systems are solved and time is saved. The optimization results highlight the interest of using a morphing-based metamodel regarding the resolution time and the accuracy of the interpolated solutions. Originality/value A new method based on Schur complement is addressed to speed-up the greedy algorithm and successfully applied to a shape optimization.

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Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger

Mastrippolito¹,

Aubert²,

Ducros³

2021

Computers & Fluids

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Planar Slip Condition For Mesh Morphing Using Radial Basis Functions

Cited by 3 publications

References 16 publications

Multi-objective lightweight design of the container S-beam based on MNSGA-II with grey relational analysis

Multi-objective lightweight design of the container S-beam based on MNSGA-II with grey relational analysis

RBF-based mesh morphing improvement using Schur complement applied to rib shape optimization

Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger

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