Real-Time GPU-Accelerated Topology Optimization of a Compact Bracket: A Simulation-Only Workflow

Aswin Karkadakattil

doi:10.20535/2521-1943.2026.10.1.351072

Authors

Aswin Karkadakattil Independent Researcher Kasaragod Kerala, India https://orcid.org/0009-0004-6141-0545

DOI:

https://doi.org/10.20535/2521-1943.2026.10.1.351072

Keywords:

GPU-accelerated topology optimization, real-time simulation, digital mechanics, lightweight structural design, interactive design workflows, additive manufacturing, conceptual design exploration, computational mechanics

Abstract

Background: Topology optimization (TO) is widely adopted for lightweight structural design; however, its integration into early-stage engineering workflows is often constrained by computational expense and long solution times associated with conventional CPU-based solvers. The emergence of GPU-accelerated simulation environments offers the possibility of transforming topology optimization into a more interactive and accessible design tool. Objective: This Technical Note evaluates a practical GPU-accelerated workflow for topology optimization and examines its suitability for conceptual lightweight structural design using commercially available software. Methods: A compact triangular bracket was selected as a representative case study and analysed in ANSYS Discovery Live. A static concentrated load of 100 N was applied at one mounting interface, while the remaining interfaces were constrained using cylindrical supports to represent mechanically consistent boundary conditions. No dynamic or transient loading effects were considered. The optimization problem was formulated as compliance minimization subject to a 50 % global volume constraint. Material behaviour of AlSi10Mg was modelled as linear elastic and isotropic to ensure compatibility with the real-time GPU solver. Mesh sensitivity analysis and supplementary simulation-based validation checks were performed to assess structural consistency within a conceptual design framework. Results: The optimized configuration achieved approximately 50 % reduction in material volume while maintaining stresses and deformations within conservative limits under the prescribed static loading condition. Material redistribution followed principal load paths, and mesh refinement studies indicated stable topology convergence. The GPU-based solver enabled continuous visualization of stress evolution and structural response throughout the optimization process. Conclusions: The results demonstrate that GPU-accelerated topology optimization can provide mechanically interpretable and computationally efficient support for early-stage structural exploration. While limited to a simulation-only scope, the proposed workflow illustrates how interactive GPU-based tools can enhance structural insight and accelerate preliminary design decision-making without requiring high-performance computing infrastructure.

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