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Adaptive Sampling Strategies for Crashworthniess Applications

Lualdi, Pietro and Sturm, Ralf (2023) Adaptive Sampling Strategies for Crashworthniess Applications. ASCS Simpulse Day - AI-assisted Crash Simulation and Optimization, 2023-06-13, Online Konferenz.

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Abstract

In the context of surrogate metamodeling for crashworthiness applications, the implementation of adaptive sampling strategies holds great potential for overcoming the challenge of setting the optimal number of samples a priori. These adaptive strategies offer a significant advantage by avoiding the common pitfalls of underand oversampling, making them attractive for expensive-to-evaluate functions such as those commonly encountered in crashworthiness applications. Despite their potential, most current research in this area relies predominantly on static sampling strategies. Recognizing this gap, our work explores the adaptation of innovative adaptive sampling methods specifically tailored to the needs of crashworthiness applications. In this context, we describe the Multi-Query Cross-Validation Voronoi (MQCVVor) method. This approach extends the traditional CVVor technique by integrating parallel processing, thus improving the efficiency and accuracy of surrogate models, especially for small scale multi-response systems. Our method demonstrates a significant improvement over conventional static Latin Hypercube Design (LHD) in terms of convergence speed and robustness. In addition to these results, we briefly discuss the potential limitations of adaptive sampling strategies and lay the groundwork for future research aimed at refining these techniques for more complex scenarios.

Item URL in elib:https://elib.dlr.de/202106/
Document Type:Conference or Workshop Item (Speech)
Title:Adaptive Sampling Strategies for Crashworthniess Applications
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Lualdi, PietroUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Sturm, RalfUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:13 June 2023
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Crash, Optimization, DOI, Design of experiments
Event Title:ASCS Simpulse Day - AI-assisted Crash Simulation and Optimization
Event Location:Online Konferenz
Event Type:international Conference
Event Date:13 June 2023
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - FFAE - Fahrzeugkonzepte, Fahrzeugstruktur, Antriebsstrang und Energiemanagement
Location: Stuttgart
Institutes and Institutions:Institute of Vehicle Concepts > Vehicle Architectures and Lightweight Design Concepts
Deposited By: Sturm, Ralf
Deposited On:19 Jan 2024 14:06
Last Modified:24 Apr 2024 21:02

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