Scripts and data underlying the publication that defines and applies the new Adaptive Screening method, for extreme value prediction of non-linear wave-induced responses

DOI:10.4121/f1348609-c912-4d06-82b8-197c01f3437b.v4
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/f1348609-c912-4d06-82b8-197c01f3437b

Datacite citation style

van Essen, Sanne; Seyffert, Harleigh (2025): Scripts and data underlying the publication that defines and applies the new Adaptive Screening method, for extreme value prediction of non-linear wave-induced responses. Version 4. 4TU.ResearchData. dataset. https://doi.org/10.4121/f1348609-c912-4d06-82b8-197c01f3437b.v4
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This set of scripts and data files can be used to re-generate the Adaptive Screening method and the three applications described in the paper "Designing for dangerous waves – a new ‘Adaptive Screening’ method to predict extreme values of non-linear marine and coastal structure responses to waves".


Predicting extreme values of strongly non-linear hydrodynamic responses (such as wave impact loads) is crucial for ensuring the safety and reliability of marine and coastal structures. However, this task is challenging due to the complexity and rarity of these responses. Existing methods are often limited to weakly non-linear responses or are very computationally expensive. This paper presents a new multi-fidelity method called ‘Adaptive Screening’, designed to efficiently predict extreme values of strongly non-linear wave-induced responses. Predicting these values is a critical element of structural design and reliability analysis. Adaptive Screening combines elements of screening, multi-fidelity Gaussian Process Regression, and adaptive sampling. We validate its effectiveness through three applications: predicting the most probable maxima of second-orderwave crests, vertical bending moments on a ferry, and greenwater impact loads on a containership. Our results demonstrate that Adaptive Screening outperforms conventional brute force methods, achieving comparable accuracy in predicting extreme values while significantly

reducing high-fidelity simulation times (especially for the most non-linear cases). Like many alternative methods, Adaptive Screening relies on a response-dependent low-fidelity indicator variable. We also show that the method performs well with realistic indicators for a range of applications. The test cases indicate that Adaptive Screening is very promising for the strongly non-linear responses it was designed for.

History

  • 2024-02-16 first online
  • 2025-05-14 published, posted

Publisher

4TU.ResearchData

Format

Zipped Python code and CSV data files.

Derived from

Organizations

TU Delft, Faculty of Mechanical Engineering, Department of Marine and Transport Technology, Ship Hydromechanics
Maritime Research Institute Netherlands (MARIN), Ships Department

DATA

Files (1)