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Register a new userIRA assisted MMC-based topology optimization method
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Added by
Zhenxing Cheng
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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An Iterative Reanalysis Approximation (IRA) is integrated with the Moving Morphable Components (MMCs) based topology optimization (IRA-MMC) in this study. Compared with other classical topology optimization methods, the Finite Element (FE) based solver is replaced with the suggested IRA method. In this way, the expensive computational cost can be significantly saved by several nested iterations. The optimization of linearly elastic planar structures is constructed by the MMC, the specifically geometric parameters of which are taken as design variables to acquire explicitly geometric boundary. In the suggested algorithm, a hybrid optimizer based on the Method of Moving Asymptotes (MMA) approach and the Globally Convergent version of the Method of Moving Asymptotes (GCMMA) is suggested to improve convergence ratio and avoid local optimum. The proposed approach is evaluated by some classical benchmark problems in topology optimization, where the results show significant time saving without compromising accuracy.
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Moving Morphable Component (MMC) based topology optimization approach is an explicit algorithm since the boundary of the entity explicitly described by its functions. Compared with other pixel or node point-based algorithms, it is optimized through the parameter optimization of a Topological Description Function (TDF). However, the optimized results partly depend on the selection of related parameters of Method of Moving Asymptote (MMA), which is the optimizer of MMC based topology optimization. Practically, these parameters are tuned according to the experience and the feasible solution might not be easily obtained, even the solution might be infeasible due to improper parameter setting. In order to address these issues, a Machine Learning (ML) based parameter tuning strategy is proposed in this study. An Extra-Trees (ET) based image classifier is integrated to the optimization framework, and combined with Particle Swarm Optimization (PSO) algorithm to form a closed loop. It makes the optimization process be free from the manual parameter adjustment and the reasonable solution in the design domain is obtained. In this study, two classical cases are presented to demonstrate the efficiency of the proposed approach.
Volumetric spline parameterization and computational efficiency are two main challenges in isogeometric analysis (IGA). To tackle this problem, we propose a framework of computation reuse in IGA on a set of three-dimensional models with similar semantic features. Given a template domain, B-spline based consistent volumetric parameterization is first constructed for a set of models with similar semantic features. An efficient quadrature-free method is investigated in our framework to compute the entries of stiffness matrix by Bezier extraction and polynomial approximation. In our approach, evaluation on the stiffness matrix and imposition of the boundary conditions can be pre-computed and reused during IGA on a set of CAD models. Examples with complex geometry are presented to show the effectiveness of our methods, and efficiency similar to the computation in linear finite element analysis can be achieved for IGA taken on a set of models.
This paper presents an efficient gradient projection-based method for structural topological optimization problems characterized by a nonlinear objective function which is minimized over a feasible region defined by bilateral bounds and a single linear equality constraint. The specialty of the constraints type, as well as heuristic engineering experiences are exploited to improve the scaling scheme, projection, and searching step. In detail, gradient clipping and a modified projection of searching direction under certain condition are utilized to facilitate the efficiency of the proposed method. Besides, an analytical solution is proposed to approximate this projection with negligible computation and memory costs. Furthermore, the calculation of searching steps is largely simplified. Benchmark problems, including the MBB, the force inverter mechanism, and the 3D cantilever beam are used to validate the effectiveness of the method. The proposed method is implemented in MATLAB which is open-sourced for educational usage.
Trimming techniques are efficient ways to generate complex geometries in Computer-Aided Design(CAD). In this paper, an improved isogeometric analysis(IGA) method for trimmed geometries is proposed. We will show that the proposed method reduces the numerical error of physical solution by 50% for simple trimmed geometries, and the condition number of stiffness matrix is also decreased. Furthermore, the number of integration elements and integration points involved in the solving process can be significantly reduced compared to previous approaches, drastically improving the computational efficiency for IGA problems on the trimmed geometry. Several examples are illustrated to show the effectiveness of the proposed approach.
Axion helioscopes like the planned International Axion Observatory (IAXO) search for evidence of axions and axion-like particles (ALPs) from the Sun. A strong magnetic field is used to convert ALPs into photons via the generic ALP-photon coupling. To observe the resulting photons, X-ray detectors with low background and high efficiency are necessary. In addition, good energy resolution and low energy threshold would allow for investigating the ALP properties by studying the X-ray spectrum after its discovery. We propose to use low temperature metallic magnetic calorimeters (MMCs). Here we present the first detector system based on MMCs developed for IAXO and discuss the results of the characterization. The detector consists of a two-dimensional 64-pixel array covering an active area of 16 mm$^2$ with a fill factor of 93 %. We achieve an average energy resolution of 6.1 eV FWHM allowing for energy thresholds below 100 eV. This detector is the first step towards a larger 1 cm$^2$ array matching the IAXO X-ray optics. We determine the background rate for an unshielded detector system in the energy range between 1 keV and 10 keV to be $3.2(1) times 10^{-4}$ keV$^{-1}$ cm$^{-2}$ s$^{-1}$ from events acquired over 30 days. In the future, active and passive shields will significantly reduce the background induced by cosmic muons and natural radioactivity. Our results demonstrate that MMCs are a promising technology for helioscopes to discover and study ALPs.
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