LARGE-SCALE LINEAR BUCKLING TOPOLOGY OPTIMIZATION WITH SUCCESSIVE ITERATION OF ANALYSIS AND DESIGN Kang Zhan* and Cao Yu State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology,Dalian University of Technology, China email: zhankang@dlut.edu.cn, China Kewwords: Topology optimization, Large-scale topolog optimization, Linear buckling, Successive Iteration of Analysis and Design, Spurious mode identification ABSTRACT Conventional linear buckling topology optimization methods generally rely on a nested double-loop iteration approach, in which the inner-loop iteration finds the buckling modes, while the outer loop updates the design variables. The computational cost required by the repeated eigenvalue analyses and sensitivity computations during the iterative design process often imposes a significant challenge for large-scale problems. We extend the method of successive iteration of analysis and design (SIAD) to buckling-load factor topology optimization. The method integrates approximate eigenvalue analysis and design variable updating into a single iteration loop, thereby avoiding the need to solve the computationally expensive eigenvalue problem at each design iteration and significantly improving computational efficiency. It can achieve simultaneous convergence of the buckling modes and design variables. We also propose a criterion for identifying spurious buckling modes induced by stress concentration. Numerical examples illustrate the computational efficiency of the proposed SIAD method. The method is shown to be capable of solveing topology optimization problems with over 30 million degrees of freedom on a desktop computer at an affordable computational cost. REFFERENCES [1] Ferrari F., Sigmund O., Towards solving large-scale topology optimization problems with buckling constraints at the cost of linear analyses, Computer Methods in Applied Mechanics and Engineering , 363, 112911, 2020. [2] Cao Y., Kang Z., An efficient linear buckling topology optimization framework based on successive iteration of analysis and design. Computer Methods in Applied Mechanics and Engineering, 451,118672, 2026.