Premium
Nonintrusive reduced order model for parametric solutions of inertia relief problems
Author(s) -
Cavaliere Fabiola,
Zlotnik Sergio,
Sevilla Ruben,
Larráyoz Xabier,
Díez Pedro
Publication year - 2021
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.6702
Subject(s) - curse of dimensionality , parametric statistics , computation , mathematical optimization , inertia , finite element method , computer science , range (aeronautics) , scheme (mathematics) , representation (politics) , mathematics , algorithm , engineering , structural engineering , mathematical analysis , statistics , physics , classical mechanics , politics , law , political science , aerospace engineering , machine learning
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.