Nonintrusive reduced order model for parametric solutions of inertia relief problems
Nonintrusive reduced order model for parametric solutions of inertia relief problems
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DOI：
10.1002/nme.6702
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发表时间：
2020-12
影响因子：
2.9
通讯作者：
Fabiola Cavaliere;S. Zlotnik;R. Sevilla;X. Larráyoz;P. Díez
中科院分区：
文献类型：
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作者：
Fabiola Cavaliere;S. Zlotnik;R. Sevilla;X. Larráyoz;P. Díez
惯性缓解（IR）技术被广泛应用于工业，产生平衡负载，允许分析无约束系统，而无需求助于更昂贵的全动态分析。本工作的主要目标是开发一种计算框架，用于用IR和适当广义分解（PGD）方法求解无约束参数结构问题。首先，在材料参数和几何参数的参数设置中制定了红外方法。然后，使用封装的PGD套件开发了一个降阶模型来解决参数IR问题，从而避免了所谓的维数诅咒。仅通过一次离线计算，提出的PGD - IR方案提供了一个包含预定义参数范围的所有可能解决方案的计算vdemecum。所提出的方法是非侵入式的，因此可以与商业有限元（FE）软件包集成。通过一个三维测试用例和一个更复杂的工业测试用例显示了所开发技术的适用性和潜力。第一个示例用于突出该方案的数值特性，而第二个示例演示了在更复杂的设置中的潜力，并显示了将所建议的框架集成到商业FE包中的可能性。此外，最后一个例子显示了在多目标优化设置中使用广义解的可能性。
The Inertia Relief (IR) technique is widely used by industry and produces equilibrated loads allowing to analyze unconstrained systems without resorting to the more expensive full dynamic analysis. The main goal of this work is to develop a computational framework for the solution of unconstrained parametric structural problems with IR and the Proper Generalized Decomposition (PGD) method. First, the IR method is formulated in a parametric setting for both material and geometric parameters. A reduced order model using the encapsulated PGD suite is then developed to solve the parametric IR problem, circumventing the so‐called curse of dimensionality. With just one offline computation, the proposed PGD‐IR scheme provides a computational vademecum that contains all the possible solutions for a predefined range of the parameters. The proposed approach is nonintrusive and it is therefore possible to be integrated with commercial finite element (FE) packages. The applicability and potential of the developed technique is shown using a three‐dimensional test case and a more complex industrial test case. The first example is used to highlight the numerical properties of the scheme, whereas the second example demonstrates the potential in a more complex setting and it shows the possibility to integrate the proposed framework within a commercial FE package. In addition, the last example shows the possibility to use the generalized solution in a multi‐objective optimization setting.