Data-driven models for crashworthiness optimisation: intrusive and non-intrusive model order reduction techniques

To enable multi-query analyses, such as optimisations of large-scale crashworthiness problems, a numerically efficient model is crucial for the development process. Therefore, data-driven Model Order Reduction (MOR) aims at generating low-fidelity models that approximate the solution while strongly...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Structural and multidisciplinary optimization Ročník 65; číslo 7
Hlavní autori: Czech, Catharina, Lesjak, Mathias, Bach, Christopher, Duddeck, Fabian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022
Springer Nature B.V
Predmet:
Algorithms
Computational Mathematics and Numerical Analysis
Crashworthiness
Decomposition
Engineering
Engineering Design
Impact strength
Machine learning
Methods
Model reduction
Optimization
Partial differential equations
Research Paper
Singular value decomposition
Theoretical and Applied Mechanics
Transient analysis
Intrusive reduced order modelling
Reduced order model
Optimisation
Crashworthiness
Nonlinear model order reduction
Non-intrusive modelling
ISSN:1615-147X, 1615-1488
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:To enable multi-query analyses, such as optimisations of large-scale crashworthiness problems, a numerically efficient model is crucial for the development process. Therefore, data-driven Model Order Reduction (MOR) aims at generating low-fidelity models that approximate the solution while strongly reducing the computational cost. MOR methods for crashworthiness became only available in recent years; a detailed and comparative assessment of their potential is still lacking. Hence, this work evaluates the advantages and drawbacks of intrusive and non-intrusive projection based MOR methods in the framework of non-linear structural transient analysis. Both schemes rely on the collection of full-order training simulations and a subsequent subspace construction via Singular Value Decomposition. The intrusive MOR is based on a Galerkin projection and a consecutive hyper-reduction step. In this work, its inter-and extrapolation abilities are compared to the non-intrusive technique, which combines the subspace approach with machine learning methods. Moreover, an optimisation analysis incorporating the MOR methods is proposed and discussed for a crashworthiness example.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-022-03282-1