Difference-based Equivalent Static Load Method with adaptive time selection and local stiffness adaption

Triller, J.; Immel, Rainer; Harzheim, Lothar

doi:10.1007/s00158-021-03160-2

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…As outlook we refer to some important methodical improvements for the individual steps. Looking on the first phase, the linearization of nonlinear structural requirements can be improved or automatized by new developments like the DiESL method 48,50,64 , providing a well-defined procedure to linearize crash load cases. The adoption of this methodology would involve several advantages: first, nonlinearities in geometry and material-prominent in crash-would find consideration in the linear auxiliary load cases, resulting in an improved approximation of the actual crash load cases.…”

Section: Discussionmentioning

confidence: 99%

Multidisciplinary optimization of automotive mega-castings merging classical structural optimization with response-surface-based optimization enhanced by machine learning

Triller,

Lopez,

Nossek

et al. 2023

Sci Rep

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Large high pressure die castings (HPDC), recently referred to as mega-castings, can replace plenty of steel metal sheets usually employed for body-in-white (BIW) structures. They can save manufacturing expense and unleash additional lightweight potential thanks to additional design freedom and material properties. The BIW plays a major role in automotive design since it must fulfill numerous structural targets ranging from stiffness for vehicle dynamics, dynamic responses for NVH (noise, vibration, harshness), driving comfort standards and several passive safety requirements. The use of mega-casting structures leads to additional requirements with respect to castability and material quality. Achieving a lightweight design considering requirements related to crash or castability is a challenge on its own, due to the high computational cost of related simulation techniques. Considering multiple requirements simultaneously, therefore often leads to non-weight-optimal structures. To exploit the full lightweight potential, we present a generative multidisciplinary optimization pipeline for the structural design of automotive mega-casting parts in this paper. The approach combines established methods in automotive industry such as topology optimization and response-surface-based (RSM) optimization and enhances the latter by machine learning (ML) based clustering and classification. In a first step topology optimization is employed to derive optimal load-paths for multidisciplinary loading conditions. For this purpose, casting manufacturing constraints as well as more than hundred linearized loads are used to incorporate NVH and passive safety requirements. In a next step the optimal thickness distribution and rib orientation of the structure is achieved using RSM optimization algorithms for the computationally expensive nonlinear crash and casting simulations. Performance indicators are treated by unsupervised learning based on clustering. This enables classification constraints based on simulation field results from hundreds of samples to be included into RSM optimization. It resolves a typical risk of pure scalar, regression-type targets, where supposed optimal results fail when domain experts examine the full field result of the corresponding simulation. It is shown how this approach is superior in achieving a weight-optimal design and turnaround time compared to a design workflow classically used for BIW structures.

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Section: Discussionmentioning

confidence: 99%

Multidisciplinary optimization of automotive mega-castings merging classical structural optimization with response-surface-based optimization enhanced by machine learning

Triller,

Lopez,

Nossek

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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Topology optimization using difference-based equivalent static loads

Triller

Immel

Harzheim

2022

Struct Multidisc Optim

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Topology optimization of crash-related problems usually involves a huge number of design variables as well as nonlinearities in geometry, material, and contact. The Equivalent Static Load (ESL) method provides an approach to solve such problems. This method has recently been extended under the name Difference-based Equivalent Static Load (DiESL) method to employ a set of Finite Element models, each describing the deformed geometry at an individual time step. Only sizing optimization problems were considered so far. In this paper, the DiESL method is extended to topology optimization utilizing a Solid Isotropic Material with Penalization approach (SIMP). The method is tested using an example of a rigid pole colliding with a simple beam, where the intrusion of the pole is minimized. The initial velocity of the pole is varied in order to examine the influence of inertia effects on the optimized structures. It is shown that the results differ significantly depending on the chosen initial velocity and, consequently, that they exhibit inertia effects. This cannot be seen in the results derived by the standard ESL method. Consequently, the results of the DiESL method’s show a considerable improvement compared to those of the standard ESL method.

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A first-order equivalent static loads algorithm for optimization of nonlinear static response

Stolpe

Pollini

2023

Advances in Engineering Software

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Difference-based Equivalent Static Load Method with adaptive time selection and local stiffness adaption

Cited by 6 publications

References 30 publications

Multidisciplinary optimization of automotive mega-castings merging classical structural optimization with response-surface-based optimization enhanced by machine learning

Multidisciplinary optimization of automotive mega-castings merging classical structural optimization with response-surface-based optimization enhanced by machine learning

Topology optimization using difference-based equivalent static loads

A first-order equivalent static loads algorithm for optimization of nonlinear static response

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