A topology optimization method for hyperelastic porous structures subject to large deformation

Huang, Jiaqi; Xu, Shuzhi; Ma, Yongsheng; Liu, Ji‐Kai

doi:10.1007/s10999-021-09576-4

Cited by 15 publications

(7 citation statements)

References 48 publications

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“…Until today, SIMP has maintained its status and continues to develop. Recent advances include SIMP combined with phase-field method [37], multi-material TO [38][39][40][41][42][43][44][45], anisotropic material behaviour [46,47], dynamic performance and fatigue TO [48][49][50][51][52], large elastic deformation TO [53], additive manufacturing (AM) constrained TO [54][55][56][57][58][59], subtractive manufacture (SM) constrained TO [60], and casting constrained TO [61]. Some approaches lead to self-weight TO [62], nonlinear load cases [63,64], stress constrained TO [65], surface corrosion TO [66], buckling considerations [67,68], non-linear heat conduction [69], thermal dissipation [70].…”

Section: Solid Isotropic Materials With Penalty Methodsmentioning

confidence: 99%

On Topology Optimisation Methods and Additive Manufacture for Satellite Structures: A Review

Hurtado-Pérez,

Pablo-Sotelo,

Ramírez-López

et al. 2023

Aerospace

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Launching satellites into the Earth’s orbit is a critical area of research, and very demanding satellite services increase exponentially as modern society takes shape. At the same time, the costs of developing and launching satellite missions with shorter development times increase the requirements of novel approaches in the several engineering areas required to build, test, launch, and operate satellites in the Earth’s orbit, as well as in orbits around other celestial bodies. One area with the potential to save launching costs is that of the structural integrity of satellites, particularly in the launching phase where the largest vibrations due to the rocket motion and subsequent stresses could impact the survival ability of the satellite. To address this problem, two important areas of engineering join together to provide novel, complete, and competitive solutions: topology optimisation methods and additive manufacturing. On one side, topology optimisation methods are mathematical methods that allow iteratively optimising structures (usually by decreasing mass) while improving some structural properties depending on the application (load capacity, for instance), through the maximisation or minimisation of a uni- or multi-objective function and multiple types of algorithms. This area has been widely active in general for the last 30 years and has two main core types of algorithms: continuum methods that modify continuous parameters such as density, and discrete methods that work by adding and deleting material elements in a meshing context. On the other side, additive manufacturing techniques are more recent manufacturing processes aimed at revolutionising manufacturing and supply chains. The main exponents of additive manufacturing are Selective Laser Melting (SLM) (3D printing) as well as Electron Beam Melting (EBM). Recent trends show that topology-optimised structures built with novel materials through additive manufacturing processes may provide cheaper state-of-the-art structures that are fully optimised to better perform in the outer-space environment, particularly as part of the structure subsystem of novel satellite systems. This work aims to present an extended review of the main methods of structural topology optimisation as well as additive manufacture in the aerospace field, with a particular focus on satellite structures, which may set the arena for the development of future satellite structures in the next five to ten years.

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Section: Solid Isotropic Materials With Penalty Methodsmentioning

confidence: 99%

On Topology Optimisation Methods and Additive Manufacture for Satellite Structures: A Review

Hurtado-Pérez,

Pablo-Sotelo,

Ramírez-López

et al. 2023

Aerospace

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“…Unfortunately, the nonlinear setting markedly adds to the complexity of the (inverse) map from property to structure. Extensions of topology optimization to nonlinear properties exist 17,18 but remain challenging due to strong dependence on the initial guess and discretization 19 , lack of physical effects such as contact 20 and degrading solver stability when considering non-trivial mechanisms such as post-buckling 21 . Most importantly, a single optimization study may require hours of runtime, which is a prime reason why recent studies focused on rather simple design spaces and optimization objectives 22,23 .…”

Section: Video Denoising Diffusion Modelmentioning

confidence: 99%

Inverse design of nonlinear mechanical metamaterials via video denoising diffusion models

Bastek,

Kochmann

2023

Nat Mach Intell

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The accelerated inverse design of complex material properties—such as identifying a material with a given stress–strain response over a nonlinear deformation path—holds great potential for addressing challenges from soft robotics to biomedical implants and impact mitigation. Although machine learning models have provided such inverse mappings, they are typically restricted to linear target properties such as stiffness. Here, to tailor the nonlinear response, we show that video diffusion generative models trained on full-field data of periodic stochastic cellular structures can successfully predict and tune their nonlinear deformation and stress response under compression in the large-strain regime, including buckling and contact. Key to success is to break from the common strategy of directly learning a map from property to design and to extend the framework to intrinsically estimate the expected deformation path and the full-field internal stress distribution, which closely agree with finite element simulations. This work thus has the potential to simplify and accelerate the identification of materials with complex target performance.

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“…A multi-objective optimization that increased the heat transfer efficiency and decreased the maximum stress was established. As a robust and capable tool [22], topology optimization can be combined with FSI to improve the design. Combining the level set method (LSM) and the extended finite element method (XFEM), Jenkins and Maute [23] presented an FSI-based topology optimization approach.…”

Section: Fsi-based Design Optimizationmentioning

confidence: 99%

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction

Zou

et al. 2022

Micromachines

Self Cite

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Based on expert system theory and fluid–structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design for additive manufacturing (DfAM). By changing design parameters, a series of flow channel models can be obtained. According to the design characteristics, the system can intelligently allocate suitable computational models to compute the flow field of a specific model. The pressure-based normal stress is abstracted from the results and transmitted to the solid region by the fluid–structure (FS) interface to analyze the strength of the structure. The design space is obtained by investigating the simulation results with the metamodeling method, which is further applied for pursuing design objectives under constraints. Finally, the improved design is derived by gradient-based optimization. This system can improve the accuracy of the FSI simulation and the efficiency of the optimization process. The design optimization of a flow channel in a simplified hydraulic manifold is applied as the case study to validate the feasibility of the proposed system.

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A topology optimization method for hyperelastic porous structures subject to large deformation

Cited by 15 publications

References 48 publications

On Topology Optimisation Methods and Additive Manufacture for Satellite Structures: A Review

On Topology Optimisation Methods and Additive Manufacture for Satellite Structures: A Review

Inverse design of nonlinear mechanical metamaterials via video denoising diffusion models

Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction

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