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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The phase-ordering kinetics of the two-dimensional uniaxial nematic has been studied using a cell dynamic scheme. The system after quench from T=infinity was found to scale dynamically with an asymptotic growth law similar to that of the two-dimensional O(2) model (quenched from above the Kosterlitz-Thouless transition temperature), i.e., L (t) approximately [t/ln (t/ t(0) ) ](1/2) (with nonuniversal time scale t(0) ). We obtained the true asymptotic limit of the growth law by performing our simulation for a sufficiently long time. The presence of topologically stable 1/2 -disclination points is reflected in the observed large-momentum dependence k(-4) of the structure factor. The correlation function was also found to tally with the theoretical prediction of the correlation function for the two-dimensional O(2) system.

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Purpose Housing infrastructure is the basic need for people of a community and due to disaster many houses may severaly damaged. Stakeholders and decision makers should focus on this issue and make the infrastructure more resilient against natural hazards. As dependency plays a very important role in resilience, it is important to study the dependencies and correlations among the housing infrastructure resilience factors. The evaluation of dependencies involve vagueness due to subjective judgement of experts. Design/methodology/approach In this work, the interaction between the housing infrastructure resilience factors are evaluated by using two different approaches such as crisp DEMATEL (Decision-Making and Trial Evaluation Laboratory) and rough DEMATEL (intregated crisp DEMATEL and rough set theory), where rough theory addressed the involvement of vagueness. These two approaches are compared with each other to find the effectiveness of rough DEMATEL over crisp DEMATEL. Findings The important factors of housing infrastructure resilience are identified by using both the approaches against flood hazard. Research limitations/implications The limitation of rough DEMATEL method is that it does not differentiate the type of influence such as positive or negative. Practical implications The outcome of the work will helps the stakeholders and ecission makers to make the infrastructure more resilient. Originality/value This study identify the imporatnat resilience factors of housing infrastructure against flood hazard by using two methodologies.