multi-scale analysis

The geometrical parameter bounds are selected to facilitate access during the optimization procedure to significant variations in both Poisson’s ratio and axial stiffness. Future studies should seek to experimentally validate the multiscale geometries presented within this paper. In addition, it would be interesting to explore higher-order homogenization schemes for the multiscale structural analysis, with the aim to exploit instabilities such as buckling at both scales to realize larger deformations. Multiscale topology optimization frameworks permit the microscale description of a structure to be spatially optimized to fulfill functional objectives at the macroscale. This permits hierarchical structures with superior mechanical (Imediegwu et al. 2019; Murphy et al. 2021b), thermal (Imediegwu et al. 2021), and dynamic (Nightingale et al. 2021) performance to be generated relative to conventional topology optimization-based approaches.

1 Microscale model

multi-scale analysis

In a two-level setup, at any macro time step ormacro iteration step, the procedure is as follows. The idea is to decompose the wholecomputational domain into several overlapping or non-overlappingsubdomains and to obtain the numerical solution over the whole domainby iterating over the solutions on these subdomains. The domaindecomposition method is not limited to multiscale problems, but it canbe used for multiscale problems. This is a way of summing up longrange interaction potentials for a large set of particles. Thecontribution to the interaction potential is decomposed intocomponents with different scales and these different contributions areevaluated at different levels in a hierarchy of grids. These methods are certainly more accurate than their single-scale, isotropic predecessors, but fall short when trying to analyze novel parts/materials for which there is no historical correlations or empirical guide-posts.

1 Strain space exploration

Importantly, a full-factorial DOE is selected for this application, as the uniform distribution of simulation nodes affords a number of advantages. The primary benefit of this uniformity is the existence of rotational and reflectional symmetries, which enables up to eight unique-second Piola–Kirchhoff stress tensors to be derived from multi-scale analysis a single parent simulation. Symmetries are identified within the DOE by applying combinations of two-dimensional transformation matrices to a series of points representing all four structural members and all four strain direction vectors. The transformed set of points is then inspected to determine how each parameter is permuted during the transformation procedure.

Macro-micro formulations for polymer fluids

multi-scale analysis

However, in the general case, the generalized Langevinequation can be quite complicated and one needs to resort toadditional approximations in order to make it tractable. While heterogeneity offers huge advantages in performance (making airplanes, space shuttles and lightweight cars possible), it also introduces difficulties in the engineering design. Presently, there is not enough computational power to include all the important details within a single Finite Element (FE) model, as is customary in industry. This is because that would require a high-resolution model too complex to be feasibly solved.

multi-scale analysis

An ANN-assisted efficient enriched finite element method via the selective enrichment of moment fitting

Multiscale ideas have also been used extensively in contexts where nomulti-physics models are involved. An example of such problems involve the Navier–Stokes equations for incompressible fluid flow. E, « Stochastic models of polymeric fluids at small Deborah number, » submitted to J. This is further evidenced by the straightforward minimization of the error functional depicted in Fig. Check if you have access through your login credentials or your institution to get full access on this article. A classical example in which matched asymptotics has been used isPrandtl’s boundary layer theory in fluid mechanics.

These slowly varying quantities aretypically the Goldstone modes of the system. For example, the densities ofconserved quantities such as mass, momentum and energy densities areGoldstone modes. The equilibrium states of macroscopicallyhomogeneous systems are parametrized by the values of thesequantities.

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