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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳俊杉 | zh_TW |
| dc.contributor.advisor | Chuin-Shan Chen | en |
| dc.contributor.author | 萬文甯 | zh_TW |
| dc.contributor.author | Wen-Ning Wan | en |
| dc.date.accessioned | 2025-08-01T16:10:05Z | - |
| dc.date.available | 2025-08-02 | - |
| dc.date.copyright | 2025-08-01 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-30 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98310 | - |
| dc.description.abstract | 交互式材料網路（IMN）引入了代表性體積元素（RVE）內部的應力平衡方向交互機制，使其能有效地模擬複合材料的內部微觀結構力學行為。在本研究中，我們建立了一個雙尺度模擬框架，將 IMN 與商用有限元素分析（FEM）軟體整合，以促進彈塑性複合材料的多尺度模擬，在巨觀尺度下，我們使用 LS-DYNA 執行分析，並以 IMN 作為微觀材料的替代模型，透過立方體的單軸載重測試與孔洞版的顯式模擬，驗證了本框架的預測能力，基準測試進一步突顯出 LS-DYNA 與 IMN 結合後，在計算效率與穩定性上的優越表現。此框架提供了一種具擴展性且高效的方式，推動多尺度模擬技術的發展。 | zh_TW |
| dc.description.abstract | The Interaction-based Material Network (IMN) incorporates an interaction mechanism for stress-equilibrium directions within representative volume elements (RVEs). This approach enables the effective modeling of internal microstructural mechanics in elastoplastic composite materials. In this study, we develop a two-scale framework by integrating IMN with commercial finite element method (FEM) software, facilitating multiscale simulations of composite materials. Macroscale analyses are conducted in LS-DYNA, which utilizes IMN as a microscopic material surrogate model. This framework's predictive capabilities are shown by uniaxial loading tests on a one-element mesh and hole-plate problems on an explicit solver. Benchmarking further underscores the LS-DYNA-IMN superior computational efficiency and robustness. This framework establishes a scalable and efficient approach for advancing multiscale simulation. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-01T16:10:05Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-01T16:10:05Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements iii 摘要 v Abstract vii Table of Contents ix List of Illustrations xiii List of Tables xv Chapter 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Structure of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Chapter 2 Literature Review 5 2.1 Multiscale Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 F E2 Modeling Framework . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Homogenization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.2.1 Averaging Theorem: Strain . . . . . . . . . . . . . . . 10 2.1.2.2 Averaging Theorem: Stress . . . . . . . . . . . . . . . 12 2.1.2.3 Hill-Mandel Lemma . . . . . . . . . . . . . . . . . . . 13 2.1.3 Composite RVE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2 Machine Learning Based Material Model . . . . . . . . . . . . . . . 15 2.2.1 Feed-forward Neural Network . . . . . . . . . . . . . . . . . . . . 15 2.2.2 Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . 16 2.2.3 Graph Neural Network . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.4 Deep Material Network . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.4.1 Rotation-Free DMN . . . . . . . . . . . . . . . . . . . 20 2.2.4.2 Thermomechanical Deep Material Network . . . . . . 21 2.2.4.3 Interaction-based Material Network . . . . . . . . . . . 22 2.2.4.4 Orientation-aware Deep Material Network . . . . . . . 23 2.2.5 Finite Element Analysis Tool . . . . . . . . . . . . . . . . . . . . . 24 2.2.5.1 ANSYS LS-DYNA® . . . . . . . . . . . . . . . . . . 25 2.2.5.2 Abaqus® . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2.5.3 FEniCS . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.5.4 MFEM . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 3 Methodology 29 3.1 Offline Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2 Online Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.1 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.2 Offline Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.3.3 Online Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.3.4 Conclusion on Comparison . . . . . . . . . . . . . . . . . . . . . . 39 3.4 LS-DYNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Chapter 4 Result and Discussion 43 4.1 Experimental Environment . . . . . . . . . . . . . . . . . . . . . . . 43 4.2 Example 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3 Example 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.4.1 Timestep Control in Explicit Solver . . . . . . . . . . . . . . . . . 51 Chapter 5 Conclusion and Future Work 53 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 References 55 Appendix A — Data of Comparison on IMN and DMN 61 A.1 Online Prediction Time and Total Iterations . . . . . . . . . . . . . . 61 A.2 Online Prediction Accuracy (MSE) . . . . . . . . . . . . . . . . . . 61 Appendix B — LS-DNYA input card of Examples 65 B.1 Example 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 B.1.1 Tensile Loading in X Direction . . . . . . . . . . . . . . . . . . . . 65 B.1.2 Loading-unloading in X Direction . . . . . . . . . . . . . . . . . . 67 B.2 Example 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 | - |
| dc.language.iso | en | - |
| dc.subject | 深度材料網路 | zh_TW |
| dc.subject | 多尺度模擬 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 彈塑性材料 | zh_TW |
| dc.subject | LS-DYNA | zh_TW |
| dc.subject | LS-DYNA | en |
| dc.subject | elastoplasticity | en |
| dc.subject | machine learning | en |
| dc.subject | deep material network | en |
| dc.subject | multiscale simulation | en |
| dc.title | 深度材料網路於雙尺度彈塑性問題 | zh_TW |
| dc.title | Deep Material Networks for Two-Scale Modeling of Elastoplastic Problems | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃琮暉;劉立偉 | zh_TW |
| dc.contributor.oralexamcommittee | Tsung-Hui Huang;Li-Wei Liu | en |
| dc.subject.keyword | 多尺度模擬,深度材料網路,LS-DYNA,彈塑性材料,機器學習, | zh_TW |
| dc.subject.keyword | multiscale simulation,deep material network,LS-DYNA,elastoplasticity,machine learning, | en |
| dc.relation.page | 72 | - |
| dc.identifier.doi | 10.6342/NTU202502115 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-07-31 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 土木工程學系 | |
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