Acceleration of shape optimization analysis using model order reduction by Karhunen-Loève expansion

We present an inertial projected gradient method for solving large-scale topology optimization problems. We consider the compliance minimization problem, the heat conduction problem and the compliant mechanism problem of continua. We use the projected gradient method to efficiently treat the linear constraints of these problems. Also, inertial techniques are used to accelerate the convergence of the method. We consider an adaptive step size policy to further reduce the computational cost. The proposed method has a global convergence property. By numerical experiments, we show that the proposed method converges fast to a point satisfying the first-order optimality condition with high accuracy compared with the existing methods. The proposed method has a low computational cost per iteration, and is thus effective in a large-scale problem.

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Model order reduction using Karhunen-Loève expansion in shape optimization analysis of hyperelastic body

Tango

Shimomoto

Azegami

2022

Mechanical Engineering Journal

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Acceleration of shape optimization analysis using model order reduction by Karhunen-Loève expansion

Cited by 3 publications

References 30 publications

Efficient acoustic topology optimization with the Multifrequency Quasi-Static Ritz vector (MQSRV) method

Efficient acoustic topology optimization with the Multifrequency Quasi-Static Ritz vector (MQSRV) method

Inertial projected gradient method for large-scale topology optimization

Model order reduction using Karhunen-Loève expansion in shape optimization analysis of hyperelastic body

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