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首页> 外文会议>Proceedings of the Fifteenth international conference on civil, structural and environmental engineering computing >Critical Examination of Volume-Constrained Topology Optimization for Uncertain Load Magnitude and Direction
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Critical Examination of Volume-Constrained Topology Optimization for Uncertain Load Magnitude and Direction

机译：不确定体积大小和方向的体积受限拓扑优化的关键检查

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Uncertainty is an important consideration in topology optimization to produce robust and reliable solutions. In a recent paper, Dunning et al. introduced a new probabilistic approach for robust topology optimization to minimize the volume-constrained expected compliance with uncertainty in the loading magnitude and applied direction, where uncertainties are assumed normally distributed and statistically independent. The model presented was formulated as a statistical model which after some manipulation was replaced by an equivalent multiple load problem in the function of the number of perturbed loads. Our opinion is that the presented parametric statistical approach is far from engineering practice, because in the most applications the desired results are ones that achieve minimal maximal compliance which is a rigorous nonparametric measure of robustness. To demonstrate the differences between the minimal maximal and the conventional expected compliance models two examples with load perturbations are presented. We show that the parametric expected compliance as the preferred measure of robustness is unable to characterize the compliance variability so it has to be replaced with the easy-to-understand maximal (minimal) compliance and the range which are generally applicable nonparametric measures of robustness. In comparison, in the applied nonparametric approach developed by Csebfalvi the load perturbations are handled as "uncertain-but-bounded" parameters. The result is a robust volume-constrained compliance-minimal design which is invariant to the feasible load perturbations. The proposed robust optimization algorithm is a worst compliance searching model, which can be formulated as a small quadratic programming problem with linearized constraints and box constraints. In the model, the robustness criterion was borrowed from Kocvara which defines the optimal robust solution as the minimum of the maximal compliance on the set of load perturbations.
机译：不确定性是拓扑优化以产生可靠可靠解决方案的重要考虑因素。在最近的一篇论文中，Dunning等人。引入了一种用于稳健拓扑优化的新概率方法，以最小化受负载大小和应用方向的不确定性影响的受体积约束的预期合规性，其中不确定性被假定为正态分布且统计独立。提出的模型被公式化为统计模型，在经过一定的处理后，该模型被等效的多重载荷问题所取代，该问题是受扰动载荷数量的函数。我们的意见是，提出的参数统计方法远非工程实践，因为在大多数应用中，所需结果是达到最小最大顺应性的结果，这是鲁棒性的严格非参数度量。为了证明最小最大和常规预期服从模型之间的差异，给出了两个带有负载扰动的示例。我们表明，作为期望的鲁棒性度量的参数期望遵从性无法表征遵从性可变性，因此必须用易于理解的最大（最小）遵从性和范围（通常适用于鲁棒性的非参数度量）代替它。相比之下，在Csebfalvi开发的应用非参数方法中，负载扰动被视为“不确定但有界”的参数。结果是鲁棒的体积受限的顺应性最小值设计，其对于可行的载荷扰动是不变的。所提出的鲁棒优化算法是最坏的顺应性搜索模型，可以将其表达为具有线性约束和盒约束的小型二次规划问题。在该模型中，鲁棒性标准是从Kocvara借来的，该准则将最佳鲁棒性解决方案定义为载荷扰动集上最大顺应性的最小值。

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