Multi-Material and Multi-Objective Topology Optimization Considering Crashworthiness Queen's University

Format:

Book

Conference/Event

Author/Creator:

Huang, Yuhao, author.

Contributor:

Kim, Il Yong

Morris, Zane

Shi, Yifan

Tameer, Daniel

Teoli, Mira

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Recently, topology optimization (TO) has seen increased usage in the automotive industry as a numerical tool, greatly enhancing the accessibility and production-readiness of optimal, lightweight solutions. By natural extension of classic single material TO (SMTO), a wealth of research has been completed in multi-material TO (MMTO), enabling simultaneous determination of material selection and existence. MMTO is effective for linear static analyses, making use of structural responses that are continuously differentiable, giving itself to efficient gradient-based optimization engines. A structural response that is inherently nonlinear and transient, thus providing difficulty to the mainstay MMTO process, is that of crashworthiness. This paper presents a multi-objective MMTO framework considering crashworthiness using the equivalent static load (ESL) method. The ESL method uses a series of linear static sub-models to approximate the transient crashworthiness model. Then, the sub-models can be optimized sequentially, using a conventional MMTO program. The limitations of the existing framework are: (1) its sole focus on intrusion minimization using displacement constraints, (2) some results have checkerboard patterns despite the use of a filter. In this paper, an improved framework aims to support the weighted sum of multiple objectives such as compliance and aggregated stress, which would affect multiple performance metrics, such as intrusion, deceleration, and energy absorption. Also, the ESL load generation method is updated to reduce checkerboarding. Firstly, the MMTO theory and ESL method are introduced. Second, the operational flow of the framework is discussed. Finally, the multi-objective solutions of an example academic model are compared using Pareto Frontiers. The optimized results show 6.4% reduction of maximum intrusion and 1.5% reduction of maximum deceleration

Notes:

Vendor supplied data

Publisher Number:

2024-01-2262

Access Restriction:

Restricted for use by site license