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Adjoint optimization of pressurized membrane structures using automatic differentiation tools

机译:使用自动分化工具伴随加压膜结构的优化

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This paper presents an adjoint-based method for solving optimization problems involving pressurized membrane structures subject to external pressure loads. Shape optimization of pressurized membranes is complicated by the fact that, lacking bending stiffness, their three-dimensional shape must be sustained by the internal pressure of the inflation medium. The proposed method treats the membrane structure as an immersed manifold and employs a total Lagrangian kinematic description with an analytical pressure-volume relationship for the inflating medium. To demonstrate the proposed method, this paper considers hydrostatically loaded inflatable barriers and develops an application-specific shape parametrization based on the analytical inhomogeneous solution for the inflated shape of cylindrical membranes. Coupling this shape parametrization approach with the adjoint method for computing the gradients of functionals enables a computationally efficient optimization of pressurized membrane structures. Numerical examples include minimization and minimax problems with inequality and state constraints, which are solved considering both plane strain and general plane stress conditions. The numerical implementation leverages the high-level mathematical syntax and automatic differentiation features of the finite-element library FEniCS and related library dolfin-adjoint. The overall techniques generalize to a broad range of structural optimization problems involving pressurized membrane and thin shell structures. (C) 2020 Elsevier B.V. All rights reserved.
机译:本文介绍了一种基于伴随的方法,用于求解涉及受加压膜结构的优化问题,该膜结构受到外部压力负荷。由于缺乏弯曲刚度,它们的三维形状必须通过膨胀介质的内部压力来维持加压膜的形状优化复杂。所提出的方法将膜结构作为浸入式歧管处理,并采用总拉格朗日运动学描述,其具有用于膨胀介质的分析压力量关系。为了证明所提出的方法,本文认为液压加载的可充气屏障,并基于用于圆柱形膜的膨胀形状的分析不均匀溶液,产生特定于涂布的形状参数化。将该形状参数化方法与用于计算功能梯度的伴随方法耦合,使得能够有效地优化加压膜结构。数值示例包括最小化和最小化与不等式和状态约束的问题,其考虑到平面应变和一般平面应力条件来解决。数值实现利用有限元库毡的高级数学语法和自动差异化特征及相关库Dolfin伴随。整体技术推广到涉及加压膜和薄壳结构的广泛结构优化问题。 (c)2020 Elsevier B.v.保留所有权利。

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