Purpose: This paper aims to propose a gradient-based shape optimization framework in which traditional time-consuming conversions between computer-aided design and computer-aided engineering and the mesh update procedure are avoided/eliminated. The scheme is general so that it can be used in all cases as a black box, no matter what the objective and/or design variables are, whilst the efficiency and accuracy are guaranteed. Design/methodology/approach: The authors integrated CAD and CAE by using isogeometric analysis (IGA), enabling the present methodology to be robust and accurate. To overcome the difficulty in evaluating the sensitivities of objective and/or constraint functions by analytic method in some cases, the authors adopt the finite difference method to calculate these sensitivities, thereby providing a universal approach. Moreover, to further eliminate the inefficiency caused by the finite difference method, the authors advance the exact reanalysis method, the indirect factorization updating (IFU), to exactly and efficiently calculate functions and their sensitivities, which guarantees its generality and efficiency at the same time. Findings: The proposed isogeometric gradient-based shape optimization using our IFU approach is reliable and accurate, as well as general and efficient. Originality/value: The authors proposed a gradient-based shape optimization framework in which they first integrate IGA and the proposed exact reanalysis method for applicability to structural response and sensitivity analysis.
|Original language||English (US)|
|Number of pages||26|
|Journal||Engineering Computations (Swansea, Wales)|
|State||Published - Nov 27 2018|
Bibliographical noteFunding Information:
This work was supported by the State Key Program of National Natural Science of China (61232014), National Science Foundation of China (11472101), Hunan Provincial Innovation Foundation for Postgraduate (CX2016B079) and China Scholarship Council (201606130079).
© 2018, Emerald Publishing Limited.
- Finite difference method
- Gradient-based shape optimization
- Indirect factorization updating (IFU)
- Isogeometric exact reanalysis