The term photonic crystals appears because of the analogy between electron waves in crystals and the light waves in artificial periodic dielectric structures. During the recent years the investigation of one-, two-and three-dimensional periodic structures has attracted a widespread attention of the world optics community because of great potentiality of such structures in advanced applied optical fields. The interest in periodic structures has been stimulated by the fast development of semiconductor technology that now allows the fabrication of artificial structures, whose period is comparable with the wavelength of light in the visible and infrared ranges.

http://ieeexplore.ieee.org/iel5/6354/16969/00781867.pdf

Photonic crystals

Topology optimization has undergone a tremendous development since its introduction in the seminal paper by Bendsoe and Kikuchi in 1988. By now, the concept is developing in many different directions, including “density”, “level set”, “topological derivative”, “phase field”, “evolutionary” and several others. The paper gives an overview, comparison and critical review of the different approaches, their strengths, weaknesses, similarities and dissimilarities and suggests guidelines for future research.

Topology optimization approaches: A comparative review

Topology optimization is the process of determining the optimal layout of material and connectivity inside a design domain. This paper surveys topology optimization of continuum structures from the year 2000 to 2012. It focuses on new developments, improvements, and applications of finite element-based topology optimization, which include a maturation of classical methods, a broadening in the scope of the field, and the introduction of new methods for multiphysics problems. Four different types of topology optimization are reviewed: (1) density-based methods, which include the popular Solid Isotropic Material with Penalization (SIMP) technique, (2) hard-kill methods, including Evolutionary Structural Optimization (ESO), (3) boundary variation methods (level set and phase field), and (4) a new biologically inspired method based on cellular division rules. We hope that this survey will provide an update of the recent advances and novel applications of popular methods, provide exposure to lesser known, yet promising, techniques, and serve as a resource for those new to the field. The presentation of each method's focuses on new developments and novel applications.

/pdf/a-survey-of-structural-and-multidisciplinary-continuum-3lbeegkisv.pdf

A survey of structural and multidisciplinary continuum topology optimization: post 2000

Manufacturing-oriented topology optimization has been extensively studied the past two decades, in particular for the conventional manufacturing methods, for example, machining and injection molding or casting. Both design and manufacturing engineers have benefited from these efforts because of the close-to-optimal and friendly-to-manufacture design solutions. Recently, additive manufacturing (AM) has received significant attention from both academia and industry. AM is characterized by producing geometrically complex components layer-by-layer, and greatly reduces the geometric complexity restrictions imposed on topology optimization by conventional manufacturing. In other words, AM can make near-full use of the freeform structural evolution of topology optimization. Even so, new rules and restrictions emerge due to the diverse and intricate AM processes, which should be carefully addressed when developing the AM-specific topology optimization algorithms. Therefore, the motivation of this perspective paper is to summarize the state-of-art topology optimization methods for a variety of AM topics. At the same time, this paper also expresses the authors' perspectives on the challenges and opportunities in these topics. The hope is to inspire both researchers and engineers to meet these challenges with innovative solutions.

/pdf/current-and-future-trends-in-topology-optimization-for-7k1902v47j.pdf

Current and future trends in topology optimization for additive manufacturing

We present a simple and robust Matlab code for polygonal mesh generation that relies on an implicit description of the domain geometry. The mesh generator can provide, among other things, the input needed for finite element and optimization codes that use linear convex polygons. In topology optimization, polygonal discretizations have been shown not to be susceptible to numerical instabilities such as checkerboard patterns in contrast to lower order triangular and quadrilaterial meshes. Also, the use of polygonal elements makes possible meshing of complicated geometries with a self-contained Matlab code. The main ingredients of the present mesh generator are the implicit description of the domain and the centroidal Voronoi diagrams used for its discretization. The signed distance function provides all the essential information about the domain geometry and offers great flexibility to construct a large class of domains via algebraic expressions. Examples are provided to illustrate the capabilities of the code, which is compact and has fewer than 135 lines.

https://paulino.ce.gatech.edu/conferences/presentations/11periera_polymesher.pdf

PolyMesher: a general-purpose mesh generator for polygonal elements written in Matlab

The paper presents an efficient 88 line MATLAB code for topology optimization. It has been developed using the 99 line code presented by Sigmund (Struct Multidisc Optim 21(2):120---127, 2001) as a starting point. The original code has been extended by a density filter, and a considerable improvement in efficiency has been achieved, mainly by preallocating arrays and vectorizing loops. A speed improvement with a factor of 100 is obtained for a benchmark example with 7,500 elements. Moreover, the length of the code has been reduced to a mere 88 lines. These improvements have been accomplished without sacrificing the readability of the code. The 88 line code can therefore be considered as a valuable successor to the 99 line code, providing a practical instrument that may help to ease the learning curve for those entering the field of topology optimization. The paper also discusses simple extensions of the basic code to include recent PDE-based and black-and-white projection filtering methods. The complete 88 line code is included as an appendix and can be downloaded from the web site www.topopt.dtu.dk .

Efficient topology optimization in MATLAB using 88 lines of code

Giga-voxel-resolution computational morphogenesis is used to optimize the internal structure of a full-scale aeroplane wing, yielding light-weight designs with more similarities to animal bone structures than to current aeroplane wing designs. Computational morphogenesis is used to design the best possible shapes and material distributions for the desired structural properties, such as high strength at minimal weight. In plants and animals, morphogenesis occurs naturally through slow genetic evolution. In engineering, a much faster iterative approach for optimum material distribution has been adopted, called topology optimization. So far, it has been used to calculate only small or simple structures owing to limited resolution. Niels Aage et al. have developed a morphogenesis tool that can be run on a supercomputer and can calculate two orders of magnitude more voxels (the three-dimensional equivalents of pixels) than was previously attainable. This makes it possible to design structures with unprecedented detail, yielding new insights into optimal material distribution. The authors calculate an optimized full aircraft wing structure with remarkable structural detail at several length scales, which displays similarities to naturally occurring bone structures such as those seen in bird beaks. The new tool could inspire surprising design approaches for a range of structures, including wind turbine blades, tower masts and bridges. In the design of industrial products ranging from hearing aids to automobiles and aeroplanes, material is distributed so as to maximize the performance and minimize the cost. Historically, human intuition and insight have driven the evolution of mechanical design, recently assisted by computer-aided design approaches. The computer-aided approach known as topology optimization enables unrestricted design freedom and shows great promise with regard to weight savings, but its applicability has so far been limited to the design of single components or simple structures, owing to the resolution limits of current optimization methods1,2. Here we report a computational morphogenesis tool, implemented on a supercomputer, that produces designs with giga-voxel resolution—more than two orders of magnitude higher than previously reported. Such resolution provides insights into the optimal distribution of material within a structure that were hitherto unachievable owing to the challenges of scaling up existing modelling and optimization frameworks. As an example, we apply the tool to the design of the internal structure of a full-scale aeroplane wing. The optimized full-wing design has unprecedented structural detail at length scales ranging from tens of metres to millimetres and, intriguingly, shows remarkable similarity to naturally occurring bone structures in, for example, bird beaks. We estimate that our optimized design corresponds to a reduction in mass of 2–5 per cent compared to currently used aeroplane wing designs, which translates into a reduction in fuel consumption of about 40–200 tonnes per year per aeroplane. Our morphogenesis process is generally applicable, not only to mechanical design, but also to flow systems3, antennas4, nano-optics5 and micro-systems6,7.

/pdf/giga-voxel-computational-morphogenesis-for-structural-design-4fs8r63d2g.pdf

Giga-voxel computational morphogenesis for structural design

https://backend.orbit.dtu.dk/ws/files/83189900/How_to_determine_composite_material_properties_originalVersion.pdf

How to determine composite material properties using numerical homogenization

Design of manufacturable 3D extremal elastic microstructure

This paper presents a flexible framework for parallel and easy-to-implement topology optimization using the Portable and Extendable Toolkit for Scientific Computing (PETSc). The presented framework is based on a standardized, and freely available library and in the published form it solves the minimum compliance problem on structured grids, using standard FEM and filtering techniques. For completeness a parallel implementation of the Method of Moving Asymptotes is included as well. The capabilities are exemplified by minimum compliance and homogenization problems. In both cases the unprecedented fine discretization reveals new design features, providing novel insight. The code can be downloaded from www.topopt.dtu.dk/PETSc .

Erik Andreassen

Papers

Efficient topology optimization in MATLAB using 88 lines of code

Giga-voxel computational morphogenesis for structural design

How to determine composite material properties using numerical homogenization

Design of manufacturable 3D extremal elastic microstructure

Topology optimization using PETSc: An easy-to-use, fully parallel, open source topology optimization framework