Introducing the sequential linear programming level-set method for topology optimization

The authors would like to thank Numerical Analysis Group at the Rutherford Appleton Laboratory for their FORTRAN HSL packages (HSL, a collection of Fortran codes for large-scale scientific computation. See http://www.hsl.rl.ac.uk/). Dr H Alicia Kim acknowledges the support from Engineering and Physical Sciences Research Council, grant number EP/M002322/1Peer reviewedPublisher PD

Stochastic level-set method for shape optimisation

We present a new method for stochastic shape optimisation of engineering structures. The method generalises an existing deterministic scheme, in which the structure is represented and evolved by a level-set method coupled with mathematical programming. The stochastic element of the algorithm is built on the methods of statistical mechanics and is designed so that the system explores a Boltzmann-Gibbs distribution of structures. In non-convex optimisation problems, the deterministic algorithm can get trapped in local optima: the stochastic generalisation enables sampling of multiple local optima, which aids the search for the globally-optimal structure. The method is demonstrated for several simple geometrical problems, and a proof-of-principle calculation is shown for a simple engineering structure.Comment: 17 pages, 10 fig

Multi-objective robust topology optimization with dynamic weighting

A common robust topology optimization is formulated as a weighted sum of expected and variance of the objective functions for the given uncertainties. This has recently been applied to topology optimization with uncertainties in loading, [1]. Figure 1(a) shows the Pareto front of solutions found using uniformly distributed weightings. This front suffers from crowding for weight values 0.625. In the general case, the two goals of multi-objective optimization are; to find the most diverse set of Pareto optimal solutions, and, to discover solutions as close as possible to the true Pareto front. This paper presents schemes to achieve both these goals

Level Set Topology Optimization of Load Carrying Heat Dissipation Devices

In this paper, we introduce a level set method topology optimization method of structures subjected to coupled mechanical and thermal loads. Different examples considering compliance minimization and stress minimization under temperature and volume constraints, and mass minimization under stress and temperature constraints, are presented. The p-norm of the stress field and temperature field is used to approximate the maximum stress and temperature, respectively. The developed method is applied in the design of an L-bracket and a battery package. The results show that designs obtained by ignoring the thermal or structural constraints can result in high values of temperature or stress, respectively

Elastic and Piezoelectric Properties of Boron Nitride Nanotube Composites

This paper is the second part of a two-part series where the first part presents a molecular dynamics model of a single Boron Nitride Nanotube (BNNT) and this paper scales up to multiple BNNTs in a polymer matrix. This paper presents finite element (FE) models to investigate the effective elastic and piezoelectric properties of (BNNT) nanocomposites. The nanocomposites studied in this paper are thin films of polymer matrix with aligned co-planar BNNTs. The FE modelling approach provides a computationally efficient way to gain an understanding of the material properties. We examine several FE models to identify the most suitable models and investigate the effective properties with respect to the BNNT volume fraction and the number of nanotube walls. The FE models are constructed to represent aligned and randomly distributed BNNTs in a matrix of resin using 2D and 3D hollow and 3D filled cylinders. The homogenisation approach is employed to determine the overall elastic and piezoelectric constants for a range of volume fractions. These models are compared with an analytical model based on Mori-Tanaka formulation suitable for finite length cylindrical inclusions. The model applies to primarily single-wall BNNTs but is also extended to multi-wall BNNTs, for which preliminary results will be presented. Results from the Part 1 of this series can help to establish a constitutive relationship for input into the finite element model to enable the modeling of multiple BNNTs in a polymer matrix

Level-Set Topology Optimization with Aeroelastic Constraints

Level-set topology optimization is used to design a wing considering skin buckling under static aeroelastic trim loading, as well as dynamic aeroelastic stability (flutter). The level-set function is defined over the entire 3D volume of a transport aircraft wing box. Therefore, the approach is not limited by any predefined structure and can explore novel configurations. The Sequential Linear Programming (SLP) level-set method is used to solve the constrained optimization problems. The proposed method is demonstrated using three problems with mass, linear buckling and flutter objective and/or constraints. A constraint aggregation method is used to handle multiple buckling constraints in the wing skins. A continuous flutter constraint formulation is used to handle difficulties arising from discontinuities in the design space caused by a switching of the critical flutter mode

A level set topology optimization method for the buckling of shell structures

Shell structures are some of the most widely used in engineering applications. Flat plates, stiffened panels, and wing ribs are each examples of components for which the design features may be dictated by the critical buckling load. Despite this practical significance, there exists only a handful of studies in the literature documenting applications of topology optimization which consider buckling performance. This is due to several issues innate to this domain, including mode switching, spurious behavior in void regions, and the presence of repeated eigenvalues. Herein, we propose a level set method capable of effectively optimizing structures despite these challenges in the context of linear buckling. We demonstrate the usefulness of such in the design of several common shell structures and explore the trade-off between stiffness and buckling load performance

Level set topology optimization for design-dependent pressure loads using the reproducing kernel particle method

This paper presents a level set topology optimization method in combination with the reproducing kernel particle method (RKPM) for the design of structures subjected to design-dependent pressure loads. RKPM allows for arbitrary particle placement in discretization and approximation of unknowns. This attractive property in combination with the implicit boundary representation given by the level set method provides an effective framework to handle the design-dependent loads by moving the particles on the pressure boundary without the need of remeshing or special numerical treatments. Moreover, the reproducing kernel (RK) smooth approximation allows for the Young’s modulus to be interpolated using the RK shape functions. This is another advantage of the proposed method as it leads to a smooth Young’s modulus distribution for smooth boundary sensitivity calculation which yields a better convergence. Numerical results show good agreement with those in the literature