Topology optimization of thermo‐elastic structures with temperature‐dependent material properties under large temperature gradient

Tang, Lei; Gao, Tong; Zhang, Weitao; Zeng, Jun; Zhang, Weihong

doi:10.1002/nme.7304

Cited by 4 publications

(1 citation statement)

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“…TO methods optimize the distribution of available material within a specified domain by finding layouts that minimize or maximize a suitable objective function, such as minimizing compliance or maximizing thermal conductivity. [1][2][3] The resulting topology provides an efficient use of material within the design domain, yielding cost savings and lightweight structures-all valuable features for engineers.…”

Section: Introductionmentioning

confidence: 99%

Real‐time topology optimization via learnable mappings

Garayalde,

Torzoni,

Bruggi

et al. 2024

Numerical Meth Engineering

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In traditional topology optimization, the computing time required to iteratively update the material distribution within a design domain strongly depends on the complexity or size of the problem, limiting its application in real engineering contexts. This work proposes a multi‐stage machine learning strategy that aims to predict an optimal topology and the related stress fields of interest, either in 2D or 3D, without resorting to any iterative analysis and design process. The overall topology optimization is treated as regression task in a low‐dimensional latent space, that encodes the variability of the target designs. First, a fully‐connected model is employed to surrogate the functional link between the parametric input space characterizing the design problem and the latent space representation of the corresponding optimal topology. The decoder branch of an autoencoder is then exploited to reconstruct the desired optimal topology from its latent representation. The deep learning models are trained on a dataset generated through a standard method of topology optimization implementing the solid isotropic material with penalization, for varying boundary and loading conditions. The underlying hypothesis behind the proposed strategy is that optimal topologies share enough common patterns to be compressed into small latent space representations without significant information loss. Results relevant to a 2D Messerschmitt‐Bölkow‐Blohm beam and a 3D bridge case demonstrate the capabilities of the proposed framework to provide accurate optimal topology predictions in a fraction of a second.

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Section: Introductionmentioning

confidence: 99%

Real‐time topology optimization via learnable mappings

Garayalde,

Torzoni,

Bruggi

et al. 2024

Numerical Meth Engineering

View full text Add to dashboard Cite

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Dynamic topology optimization considering the influence of the non‐uniform temperature field

Zhang,

Li,

Zhao

et al. 2024

Numerical Meth Engineering

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With the wide application of thermoelastic structures in industries such as the aerospace field, the problem of topology optimization of thermoelastic structures has become a very common and important research topic. It is well known that the thermal environment has a non‐negligible influence on the dynamic performance of structures. However, few people consider the influence of the thermal environment on structural stiffness in thermoelastic dynamic topology optimization. In practical engineering applications, the influence of the environment on the structure performance should be considered to obtain the optimal structure. In this paper, we focus on the problem of dynamic topology optimization considering the effect of non‐uniform temperature fields on structural stiffness. The influence of non‐uniform temperature fields adds on structural stiffness to the topology optimization of thermoelastic dynamic for the first time, thereby comprehensively addressing its effects on structural stiffness in the context of dynamic topology optimization under harmonic vibration and transient load. The proposed method begins by computing the distribution of the non‐uniform temperature field within the structure. Subsequently, thermal stresses in the structure are determined through the application of thermoelastic theory. The geometric stiffness matrix of the structure is then calculated using finite element theory. The dynamic topology optimization model, employing a variable density approach, is established in conjunction with the dynamic compliance design objective. Sensitivity analysis is conducted through the adjoint method, and the design variables are updated utilizing the method of moving asymptotes. Numerical examples are presented to validate the efficacy of the proposed method and obtain the influence of different factors on the optimization results. The results show that the dynamic compliance of the optimized structure increases with increasing heat flux. For the optimization under harmonic vibration, the optimization results obtained by different external excitation frequencies are significantly different. For transient optimization, the study discovers that the optimization present transient effect.

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