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Analyzing the complexity of ice with explainable machine learning for the development of an ice material model
Citation Link: https://doi.org/10.15480/882.4076
Publikationstyp
Doctoral Thesis
Date Issued
2022-01
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2021-12-16
TORE-DOI
Citation
Technische Universität Hamburg (2022)
Seasonally or partially ice-covered sea areas are attractive to many stakeholders. Ships and structures operating in these areas must be designed to withstand ice-induced loads. This is done with empirical methods and model-scale ice tests, but these have various drawbacks. Numerical simulations are a desirable remedy, but their accuracy is limited because current ice material models only partially capture ice behavior. Improving ice material models is difficult due to gaps in ice mechanics theory and full-scale measurements. Small-scale experimental data is analyzed with explainable machine learning for an improved understanding of ice. An elastic-brittle material model is developed and applied in several studies.
DDC Class
620: Ingenieurwissenschaften
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Leon_Kellner_Analyzing_complexity_ice_with_explainable_machine_learning.pdf
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30.27 MB
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