Maximisation of the Crushing Performance of a Tubular Device by Shape Optimisation

2009 ◽  
Author(s):  
G. Chiandussi ◽  
M. Avalle ◽  
M.P. Cavatorta
Keyword(s):  
Shape Optimisation

TURBOCHARGER TURBINE DIFFUSER WALL SHAPE OPTIMISATION

2019 ◽  
Author(s):  
José Arthur Gonçalves da Silva Teixeira ◽  
Bryan Castro Caetano ◽  
Matheus Ungaretti Borges ◽  
Jose Baeta ◽  
Ricardo Poley Martins Ferreira
Keyword(s):  
Shape Optimisation

scholarly journals A novel p-harmonic descent approach applied to fluid dynamic shape optimization

2021 ◽  
Author(s):  
Peter Marvin Müller ◽  
Niklas Kühl ◽  
Martin Siebenborn ◽  
Klaus Deckelnick ◽  
Michael Hinze ◽  
...  
Keyword(s):  
Shape Optimization ◽  
Large Deformations ◽  
Fluid Dynamic ◽  
Shape Optimisation ◽  
Shape Features ◽  
Floating Bodies ◽  
Novel Method ◽  
Sharp Corners ◽  
Dynamic Shape

AbstractWe introduce a novel method for the implementation of shape optimization for non-parameterized shapes in fluid dynamics applications, where we propose to use the shape derivative to determine deformation fields with the help of the $$p-$$ p - Laplacian for $$p > 2$$ p > 2 . This approach is closely related to the computation of steepest descent directions of the shape functional in the $$W^{1,\infty }-$$ W 1 , ∞ - topology and refers to the recent publication Deckelnick et al. (A novel $$W^{1,\infty}$$ W 1 , ∞ approach to shape optimisation with Lipschitz domains, 2021), where this idea is proposed. Our approach is demonstrated for shape optimization related to drag-minimal free floating bodies. The method is validated against existing approaches with respect to convergence of the optimization algorithm, the obtained shape, and regarding the quality of the computational grid after large deformations. Our numerical results strongly indicate that shape optimization related to the $$W^{1,\infty }$$ W 1 , ∞ -topology—though numerically more demanding—seems to be superior over the classical approaches invoking Hilbert space methods, concerning the convergence, the obtained shapes and the mesh quality after large deformations, in particular when the optimal shape features sharp corners.

Shape optimisation of singly-symmetric cold-formed steel purlins

2021 ◽  
Vol 161 ◽  
pp. 107402
Author(s):  
V.M. Guimarães ◽  
B.P. Gilbert ◽  
N. Talebian ◽  
B. Wang
Keyword(s):  
Shape Optimisation ◽  
Cold Formed Steel

scholarly journals Enhancing CAD-based shape optimisation by automatically updating the CAD model’s parameterisation

2018 ◽  
Vol 59 (5) ◽  
pp. 1639-1654 ◽  
Author(s):  
Dheeraj Agarwal ◽  
Trevor T. Robinson ◽  
Cecil G. Armstrong ◽  
Christos Kapellos
Keyword(s):  
Shape Optimisation

Shape optimisation for crashworthiness followed by a robustness analysis with respect to shape variables

2013 ◽  
Vol 48 (2) ◽  
pp. 367-378 ◽  
Author(s):  
Stephan Hunkeler ◽  
Fabian Duddeck ◽  
Milan Rayamajhi ◽  
Hans Zimmer
Keyword(s):  
Robustness Analysis ◽  
Shape Optimisation

scholarly journals 3D shape optimisation of a low-pressure turbine stage

2018 ◽  
Vol 3 (1) ◽  
pp. 10-21
Author(s):  
L. Witanowski ◽  
P. Klonowicz ◽  
P. Lampart
Keyword(s):  
Low Pressure ◽  
Shape Optimisation ◽  
Turbine Stage ◽  
3D Shape ◽  
Low Pressure Turbine

scholarly journals Shape optimisation with the level set method for contact problems in linearised elasticity

2017 ◽  
Vol 3 ◽  
pp. 249-292 ◽  
Author(s):  
Aymeric Maury ◽  
Grégoire Allaire ◽  
François Jouve
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Contact Problems ◽  
Shape Optimisation

scholarly journals Implicit constraint handling for shape optimisation with POD-morphing

2012 ◽  
Author(s):  
Balaji Raghavan ◽  
Manyu Xiao ◽  
Piotr Breitkopf ◽  
Pierre Villon
Keyword(s):  
Structural Optimization ◽  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Shape Optimisation ◽  
Design Domain ◽  
Cad Models ◽  
Typical Shape ◽  
Global Understanding ◽  
Proper Orthogonal ◽  
Diffuse Approximation

In the former paper, we have introduced an original morphing approach based on Proper Orthogonal Decomposition (POD) of shapes, designed to replace parametrized CAD models in structural optimization. Here, we expand the method to interpolate exclusively between admissible instances of structural shapes, thus permitting a global understanding of the design domain and also reducing the size of the optimisation problem. The result is a bilevel reparametrization approach for structural geometries based on Diffuse Approximation in a properly chosen locally linearized space, and the geometric parameters are replaced with the smallest set of variables needed to represent a manifold of admissible shapes for a chosen precision. We demonstrate the approach in a typical shape optimisation problem.

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