DOE OSTI · 2522862
Adaptive immersed isogeometric level-set topology optimization
Abstract
Here, this paper presents for the first time an adaptive immersed approach for level-set topology optimization using higher-order truncated hierarchical B-spline discretizations for design and state variable fields. Boundaries and interfaces are represented implicitly by the iso-contour of one or multiple level-set functions. An immersed finite element method, the eXtended IsoGeometric Analysis, is used to predict the physical response. The proposed optimization framework affords different adaptively refined higher-order B-spline discretizations for individual design and state variable fields. The increased continuity of higher-order B-spline discretizations together with local refinement enables direct control over the accuracy of the representation of each field while simultaneously reducing computational cost compared to uniformly refined discretizations. A flexible mesh adaptation strategy enables local refinement based on geometric measures or physics-based error indicators. These adaptive discretization and analysis approaches are integrated into gradient-based optimization schemes, evaluating the design sensitivities using the adjoint method. Numerical studies illustrate the features of the proposed framework with static, linear elastic, multi-material, two- and three-dimensional problems. The examples provide insight into the effect of refining the design variable field on the optimization result and the convergence rate of the optimization process. Using coarse higher-order B-spline discretizations for level-set fields promotes the development of smooth designs and suppresses the emergence of small features. Moreover, adaptive mesh refinement for state variable fields results in a reduction of overall computational cost. Higher-order B-spline discretizations are especially interesting when evaluating gradients of state variable fields due to their higher inter-element continuity.
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Schmidt, Mathias R. [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0009000282366390), Noël, Lise [Delft Univ. of Technology (Netherlands)], Wunsch, Nils [Univ. of Colorado, Boulder, CO (United States)], Doble, Keenan [Univ. of Colorado, Boulder, CO (United States)], Evans, John A. [Univ. of Colorado, Boulder, CO (United States)], Maute, Kurt [Univ. of Colorado, Boulder, CO (United States)]. 2024-12-27. Adaptive immersed isogeometric level-set topology optimization. https://doi.org/10.1007/s00158-024-03921-9
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