Monte Carlo simulation has been performed in the planar P 2 and P 4 models to investigate the effects of the suppression of topological defects on the phase transition exhibited by these models. Suppression of the 1/2-defects on the square plaquettes in the P 2 model leads to complete elimination of the phase transition observed in this model. However in the P 4 model, on suppressing the single 1/2-defects on square plaquettes, the otherwise first order phase transition changes to a second order one which occurs at a higher temperature and this is due to presence of large number of 1/2-pair defects which are left within the square plaquettes. When we suppressed these charges too, complete elimination of phase transition was observed.

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Despite a solid theoretical foundation and straightforward application to structural design problems, 3D topology optimization still suffers from a prohibitively high computational effort that hinders its widespread use in industrial design. One major contributor to this problem is the cost of solving the finite element equations during each iteration of the optimization loop. To alleviate this cost in large-scale topology optimization, the authors propose a projection-based reducedorder modeling approach using proper orthogonal decomposition for the construction of a reduced basis for the FE solution during the optimization, using a small number of previously obtained and stored solutions. This basis is then adaptively enriched and updated on-the-fly according to an error residual, until convergence of the main optimization loop. The method of moving asymptotes is used for the optimization. The techniques are validated using established 3D benchmark problems. The numerical results demonstrate the advantages and the improved performance of our proposed approach.

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