arXiv subjects

Dingchuan Xue

Publications and source records attributed to Dingchuan Xue.

4 recordsLinked to original sources

arXiv2021

Stiffness minimisation of graded microstructural configurations using asymptotic analysis and machine learning

The article is aimed to address a mutually boosting use of asymptotic analysis and machine learning, for fast stiffness design of configurations infilled with smoothly-varying graded microstructures. The discussion is conducted in the context of an improved asymptotic-homogenisation topology optimisation (AHTO plus) framework. It is demonstrated that on one hand, machine learning can be employed to represent the key but implicit inter-relationships revealed from asymptotic analysis, and the evaluations of the homogenised quantities, as well as the sensitivities of the design variables, become quite efficient. On the other hand, the use of asymptotic analysis identifies a computational routine for data acquisition, thus the training data here are inexhaustible in theory. Key issues regarding integration of the two methods, such as ensuring the positive definiteness of the homogenised elasticity tensor represented with neural networks, are also discussed. The accuracies and the efficiencies of the present scheme are numerically demonstrated. For two-dimensional optimisation, it takes the present algorithm roughly 300 seconds on a standard desktop computer, and this qualifies the present scheme as one of the most efficient algorithms used for the compliance optimisation of configurations infilled with complex microstructures.

math.NA↗

arXiv2020

Generation of smoothly-varying infill configurations from a continuous menu of cell patterns and the asymptotic analysis of its mechanical behaviour

We here introduce a novel scheme for generating smoothly-varying infill graded microstructural (IGM) configurations from a given menu of generating cells. The scheme was originally proposed for essentially improving the variety of describable configurations in a modified asymptotic homogenisation-based topology optimisation framework [1] for fast IGM design. But the proposed scheme, after modification, also demonstrates its unique values in two aspects of applications. First, it provides a fairly simple way of generating an IGM configuration continuously patching any given cell configurations. Second, it tenders a straightforward mean for decorating microstructures on a given manifold. We will further show that the form of topology description function given here effectively offers a platform for unifying most existing approaches for IGM generation. Fuelled by asymptotic analysis of the mechanical behaviour of the resulting IGM configurations, a topology optimisation scheme for compliance minimisation is introduced. We will finally show that, the use of the present scheme helps reduce the compliance value of an optimised structure by nearly a half, if compared with that from the original framework [1].

cs.CE↗

arXiv2019

On speeding up an asymptotic-analysis-based homogenisation scheme for designing gradient porous structured materials using a zoning strategy

Gradient porous structured materials possess significant potential of being applied in many engineering fields. To accelerate the design process of infill graded microstructures, a novel asymptotic homogenisation topology optimisation method was proposed by Zhu et al.(2019), aiming for 1) significantly enriching the pool of representable graded microstructures; 2) deriving an homogenised formulation for stress analysis in consistency with fine-scale results. But the work is severely confined from being widely applied, mainly due to the following two reasons. Firstly, to circumvent the macroscopically pointwise computation for solving various microscopic cell problems, linearisation had to be adopted for its numerical implementation, and this significantly reduces the design freedom. Secondly, lacking of sensitive analysis, genetic algorithm was chosen for optimisation, inevitably decreasing the computational efficiency. To address these bottleneck challenging issues, a zoning scheme empowered by computational parallelism is introduced, and the sensitivity analysis associated with the new asymptotic framework is conducted. Through comparisons with fine-scale simulation results, the proposed algorithm is shown to be an effective tool for evaluating the mechanical behaviour of graded microstructures. As an optimisation tool, the mapping function takes a concise and explicit form. But its parameterisation still needs further investigation, so as to improve the solution optimality of the present approach, especially in comparison with another recently proposed method by Groen et al (2018). The high computational efficiency shown by the proposed scheme is also demonstrated with optimisation results for three-dimensional graded microstructures, which are not frequently discussed in literature, possibly because of the high computational cost generated.

cs.CE↗

arXiv2017

Explicit three dimensional topology optimization via Moving Morphable Void (MMV) approach

Three dimensional (3D) topology optimization problems always involve huge numbers of Degrees of Freedom (DOFs) in finite element analysis (FEA) and design variables in numerical optimization, respectively. This will inevitably lead to large computational efforts in the solution process. In the present paper, an efficient and explicit topology optimization approach which can reduce not only the number of design variables but also the number of degrees of freedom in FEA is proposed based on the Moving Morphable Voids (MMVs) solution framework. This is achieved by introducing a set of geometry parameters (e.g., control points of B-spline surfaces) to describe the boundary of a structure explicitly and removing the unnecessary DOFs from the FE model at every step of numerical optimization. Numerical examples demonstrate that the proposed approach does can overcome the bottleneck problems associated with a 3D topology optimization problem in a straightforward way and enhance the solution efficiency significantly.

cond-mat.other↗