Multiresolution Topology Optimization Research Articles

Multiresolution topology optimization using isogeometric analysis

SummaryThe paper introduces a novel multiresolution scheme to topology optimization in the framework of the isogeometric analysis. A new variable parameter space is added to implement multiresolution topology optimization based on the Solid Isotropic Material with Penalization approach. Design density variables defined in the variable space are used to approximate the element analysis density by the bivariate B‐spline basis functions, which are easily obtained using k‐refinement strategy in the isogeometric analysis. While the nonuniform rational B‐spline basis functions are used to exactly describe geometric domains and approximate unknown solutions in finite element analysis. By applying a refined sensitivity filter, optimized designs include highly discrete solutions in terms of solid and void materials without using any black and white projection filters. The Method of Moving Asymptotes is used to solve the optimization problem. Various benchmark test problems including plane stress, compliant mechanism inverter, and 2‐dimensional heat conduction are examined to demonstrate the effectiveness and robustness of the present method.

Topology optimization using the p-version of the finite element method

A multiresolution topology optimization approach is proposed using the p-version finite element method (p-version FEM). Traditional topology optimization, where a density design variable is assigned to each element, is suitable for low-order h-version FEM. However, it cannot take advantage of the higher accuracy of higher-order p-version FEM analysis for generating results with higher resolution. In contrast, the proposed method separates density variables and finite elements so that the resolution of the density field, which defines the structure, can be higher than that of the finite element mesh. Thus, the method can take full advantage of the higher accuracy of p-version FEM.

Designing patient-specific 3D printed craniofacial implants using a novel topology optimization method

Large craniofacial defects require efficient bone replacements which should not only provide good aesthetics but also possess stable structural function. The proposed work uses a novel multiresolution topology optimization method to achieve the task. Using a compliance minimization objective, patient-specific bone replacement shapes can be designed for different clinical cases that ensure revival of efficient load transfer mechanisms in the mid-face. In this work, four clinical cases are introduced and their respective patient-specific designs are obtained using the proposed method. The optimized designs are then virtually inserted into the defect to visually inspect the viability of the design . Further, once the design is verified by the reconstructive surgeon, prototypes are fabricated using a 3D printer for validation. The robustness of the designs are mechanically tested by subjecting them to a physiological loading condition which mimics the masticatory activity. The full-field strain result through 3D image correlation and the finite element analysis implies that the solution can survive the maximum mastication of 120 lb. Also, the designs have the potential to restore the buttress system and provide the structural integrity. Using the topology optimization framework in designing the bone replacement shapes would deliver surgeons new alternatives for rather complicated mid-face reconstruction.

Polygonal multiresolution topology optimization (PolyMTOP) for structural dynamics

We use versatile polygonal elements along with a multiresolution scheme for topology optimization to achieve computationally efficient and high resolution designs for structural dynamics problems. ...

Improving multiresolution topology optimization via multiple discretizations

SUMMARYUnlike the traditional topology optimization approach that uses the same discretization for finite element analysis and design optimization, this paper proposes a framework for improving multiresolution topology optimization (iMTOP) via multiple distinct discretizations for: (1) finite elements; (2) design variables; and (3) density. This approach leads to high fidelity resolution with a relatively low computational cost. In addition, an adaptive multiresolution topology optimization (AMTOP) procedure is introduced, which consists of selective adjustment and refinement of design variable and density fields. Various two‐dimensional and three‐dimensional numerical examples demonstrate that the proposed schemes can significantly reduce computational cost in comparison to the existing element‐based approach. Copyright © 2012 John Wiley & Sons, Ltd.

Single-loop system reliability-based topology optimization considering statistical dependence between limit-states

This paper presents a single-loop algorithm for system reliability-based topology optimization (SRBTO) that can account for statistical dependence between multiple limit-states, and its applications to computationally demanding topology optimization (TO) problems. A single-loop reliability-based design optimization (RBDO) algorithm replaces the inner-loop iterations to evaluate probabilistic constraints by a non-iterative approximation. The proposed single-loop SRBTO algorithm accounts for the statistical dependence between the limit-states by using the matrix-based system reliability (MSR) method to compute the system failure probability and its parameter sensitivities. The SRBTO/MSR approach is applicable to general system events including series, parallel, cut-set and link-set systems and provides the gradients of the system failure probability to facilitate gradient-based optimization. In most RBTO applications, probabilistic constraints are evaluated by use of the first-order reliability method for efficiency. In order to improve the accuracy of the reliability calculations for RBDO or RBTO problems with high nonlinearity, we introduce a new single-loop RBDO scheme utilizing the second-order reliability method and implement it to the proposed SRBTO algorithm. Moreover, in order to overcome challenges in applying the proposed algorithm to computationally demanding topology optimization problems, we utilize the multiresolution topology optimization (MTOP) method, which achieves computational efficiency in topology optimization by assigning different levels of resolutions to three meshes representing finite element analysis, design variables and material density distribution respectively. The paper provides numerical examples of two- and three-dimensional topology optimization problems to demonstrate the proposed SRBTO algorithm and its applications. The optimal topologies from deterministic, component and system RBTOs are compared with one another to investigate the impact of optimization schemes on final topologies. Monte Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach.

A computational paradigm for multiresolution topology optimization (MTOP)

This paper presents a multiresolution topology optimization (MTOP) scheme to obtain high resolution designs with relatively low computational cost. We employ three distinct discretization levels for the topology optimization procedure: the displacement mesh (or finite element mesh) to perform the analysis, the design variable mesh to perform the optimization, and the density mesh (or density element mesh) to represent material distribution and compute the stiffness matrices. We employ a coarser discretization for finite elements and finer discretization for both density elements and design variables. A projection scheme is employed to compute the element densities from design variables and control the length scale of the material density. We demonstrate via various two- and three-dimensional numerical examples that the resolution of the design can be significantly improved without refining the finite element mesh.

Multi-resolution multi-scale topology optimization — a new paradigm

The purpose of this work is to present a new-concept multi-resolution multi-scale topology optimization. The key idea of the present strategy is that design optimization should be performed progressively from low to high resolution, not at a single resolution level. To achieve the multi-resolution strategy, design optimization is formulated in a wavelet-based variable space, not in a direct density variable space. The major advantages of the multi-resolution design optimization include: (1) topologically simple and close-to-the-global-optimum structures may be obtained without any explicit constraint, and (2) the convergence is not sensitive to mathematical programming methods. For the efficient numerical implementation of the multi-resolution approach, the side constraints imposed on the direct density variables are removed by mapping the density variables into intermediate variables. These intermediate variables are then wavelet-transformed to new design variables. It is addressed that the present multi-resolution topology optimization can resolve major numerical instability problems such as mesh-dependencies and local minima. The usefulness of the multi-scale nature of the wavelets in the present multi-resolution multi-scale optimization formulation is also discussed.