Forecasting phase-field variable in brittle fracture problems by autoregressive integrated moving average technique
Phase-field modeling is a powerful and versatile computational approach for modeling the evolution of cracks in solids. However, phase-field modeling requires high computational cost for accurately capturing how cracks develop under increasing loads. In brittle fracture mechanics, crack initiation...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2024-12-01
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| Series: | Computer Assisted Methods in Engineering and Science |
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| Online Access: | https://cames.ippt.pan.pl/index.php/cames/article/view/1697 |
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| author | Cuong T. Nguyen Long H. Le Minh N. Dinh Ngoc M. La |
| author_facet | Cuong T. Nguyen Long H. Le Minh N. Dinh Ngoc M. La |
| author_sort | Cuong T. Nguyen |
| collection | DOAJ |
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Phase-field modeling is a powerful and versatile computational approach for modeling the evolution of cracks in solids. However, phase-field modeling requires high computational cost for accurately capturing how cracks develop under increasing loads. In brittle fracture mechanics, crack initiation and propagation can be considered as a time series forecasting problem so they can be studied by observing changes in the phase-field variable, which represents the level of material damage. In this paper, we develop a rather simple approach utilizing the autoregressive integrated moving average (ARIMA) technique to predict variations of the phase-field variable in an isothermal, linear elastic and isotropic phase-field model for brittle materials. Time series data of the phase-field variable is extracted from numerical results using coarse finite-element meshes. Two ARIMA schemes are introduced to exploit the structure of the collected data and provide a prediction for changes in phasefield variable when using a finer mesh. This finer mesh gives a better results in terms of accuracy but requires significantly higher computational cost.
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| format | Article |
| id | doaj-art-32b04b6f72c24d11a5c41494857f959c |
| institution | Matheson Library |
| issn | 2299-3649 2956-5839 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Computer Assisted Methods in Engineering and Science |
| spelling | doaj-art-32b04b6f72c24d11a5c41494857f959c2025-07-09T10:21:01ZengInstitute of Fundamental Technological Research Polish Academy of SciencesComputer Assisted Methods in Engineering and Science2299-36492956-58392024-12-0131410.24423/cames.2024.1697Forecasting phase-field variable in brittle fracture problems by autoregressive integrated moving average techniqueCuong T. Nguyen0Long H. Le1Minh N. Dinh2Ngoc M. La3Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NYAutomotive R&D Center, Bosch Vietnam, Le DuanSchool of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh CitySchool of Science, Engineering and Technology, RMIT University Vietnam, Ho Chi Minh City Phase-field modeling is a powerful and versatile computational approach for modeling the evolution of cracks in solids. However, phase-field modeling requires high computational cost for accurately capturing how cracks develop under increasing loads. In brittle fracture mechanics, crack initiation and propagation can be considered as a time series forecasting problem so they can be studied by observing changes in the phase-field variable, which represents the level of material damage. In this paper, we develop a rather simple approach utilizing the autoregressive integrated moving average (ARIMA) technique to predict variations of the phase-field variable in an isothermal, linear elastic and isotropic phase-field model for brittle materials. Time series data of the phase-field variable is extracted from numerical results using coarse finite-element meshes. Two ARIMA schemes are introduced to exploit the structure of the collected data and provide a prediction for changes in phasefield variable when using a finer mesh. This finer mesh gives a better results in terms of accuracy but requires significantly higher computational cost. https://cames.ippt.pan.pl/index.php/cames/article/view/1697fracture mechanicsbrittle fracturephase-field modelingtime-series forecasting |
| spellingShingle | Cuong T. Nguyen Long H. Le Minh N. Dinh Ngoc M. La Forecasting phase-field variable in brittle fracture problems by autoregressive integrated moving average technique Computer Assisted Methods in Engineering and Science fracture mechanics brittle fracture phase-field modeling time-series forecasting |
| title | Forecasting phase-field variable in brittle fracture problems by autoregressive integrated moving average technique |
| title_full | Forecasting phase-field variable in brittle fracture problems by autoregressive integrated moving average technique |
| title_fullStr | Forecasting phase-field variable in brittle fracture problems by autoregressive integrated moving average technique |
| title_full_unstemmed | Forecasting phase-field variable in brittle fracture problems by autoregressive integrated moving average technique |
| title_short | Forecasting phase-field variable in brittle fracture problems by autoregressive integrated moving average technique |
| title_sort | forecasting phase field variable in brittle fracture problems by autoregressive integrated moving average technique |
| topic | fracture mechanics brittle fracture phase-field modeling time-series forecasting |
| url | https://cames.ippt.pan.pl/index.php/cames/article/view/1697 |
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