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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳俊杉(Chuin-Shan Chen) | |
| dc.contributor.author | Tung-Huan Su | en |
| dc.contributor.author | 蘇東垣 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:06:07Z | - |
| dc.date.available | 2022-01-17 | |
| dc.date.available | 2022-11-24T03:06:07Z | - |
| dc.date.copyright | 2022-01-17 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-01-05 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80412 | - |
| dc.description.abstract | "數據驅動計算力學(data-driven computational mechanics, DDCM)顛覆傳統的計算力學架構，賦予計算力學在材料大數據時代的重大轉變。透過直接使用應力應變數據資料暨材料數據庫於計算力學架構中，材料組成律的建模需求得以緩解。承襲無材料模型的精神，數據驅動鑑定(data-driven identification, DDI)法為數據驅動計算架構提供了大量的材料數據。DDCM和DDI在許多應用中都展現出相當看好的前景，例如材料數據獲取法、使用全域應力應變測量法於材料特性分析以及以數據驅動的多尺度模擬計算方法。在本論文中，我們致力於以數據驅動無組成律模型的方法來擴展上述應用之範疇。 關於材料數據獲取法方面，我們將流形學習技術中的局部凸空間重建法融入到DDI方法中，建立了局部凸形數據驅動鑑定(Local-convexity data-driven identification, LCDDI)法。並透過線彈性、複合彈性材料與彈塑性材料的三個數值案例來驗證LCDDI方法的有效性。與DDI方法相比，LCDDI可以減少一個數量級的機械應力誤差以及減少60%以上的材料數據誤差。 在材料特性分析方面，我們首次將DDI方法應用於銅基形狀記憶合金以研究其超彈性行為。在第一個應用DDI於銅鋁錳單晶形狀記憶合金的案例，我們結合DDI方法與數位影像相關法(digital image correlation, DIC)來鑑定銅鋁錳單金形狀記憶合金的局部能量耗散特性；關於第二個應用DDI於銅鋁錳雙晶形狀記憶合金的案例，我們首次使用DIC+DDI方法探討兩個不同晶粒方向的雙晶，在循環負載次數增加下，非均勻的轉變應力場與應變場的變化；以及造成轉變應力下降即功能性疲勞的可能原因。 在多尺度模擬方面，我們呈現局部凸形數據驅動（LCDD）多尺度模擬法在準確性和效率方面的潛力。與其他的數據驅動多尺度有限元素法相比，結果顯示，LCDD多尺度法可以大幅降低準備離線數據庫的計算成本，這是因為該方法只需要相對少的材料數據點的數據庫即可達到相同的精度。 本研究展現了無組成律的計算方法在許多方面的潛力，例如高品質材料數據獲取法、全域應力應變測量法(DIC+DDI)於超彈性材料的特性分析以及高效率數據驅動多尺度模擬法。最後，在這項研究工作，我們討論並給出關於上述方面的結果總結以及未來展望" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:06:07Z (GMT). No. of bitstreams: 1 U0001-0501202214385700.pdf: 9375270 bytes, checksum: 6964364a8a21b01296f0d8ab916e0798 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員審定書 # 誌謝 i 中文摘要 iii ABSTRACT v CONTENTS vii LIST OF FIGURES xi LIST OF TABLES xxi Chapter 1 Introduction 1 1.1 Backgrounds 1 1.2 Objectives 4 1.3 Organization of the dissertation 4 Chapter 2 Literature Reviews 7 2.1 Data-driven computational mechanics 7 2.1.1 Elementary example: bar and spring 7 2.1.2 Noisy materials data sets 11 2.1.3 State of the art in DDCM 14 2.1.4 Applications of DDCM to multiscale simulation 15 2.2 Data-based derivation of material response 17 2.2.1 Data-driven identification (DDI) method 18 2.2.2 State of the art in DDI 20 2.2.3 Application of DDI to real experimental data 21 2.3 Potential applications 22 2.3.1 Material characterization of Cu-based shape memory alloys 22 2.3.2 Data-driven multiscale method 25 2.3.3 Quantitative quasi-static ultrasound elastography 27 2.4 Chapter summary 28 Chapter 3 Data-Driven Model-Free Methods in Computational Mechanics and Material Data Identification 31 3.1 Distance-minimizing data-driven (DMDD) computing 31 3.1.1 Distance-minimizing data-driven problem 31 3.1.2 Data-driven solver 33 3.1.3 Solution procedure 36 3.2 Local-convexity data-driven (LCDD) computing 37 3.2.1 Locally convex construction 37 3.2.2 Solving non-negative least squares 39 3.2.3 Solution procedure 41 3.3 Distance-minimizing data-driven identification (DMDDI) 41 3.3.1 Data-driven identification method 41 3.3.2 Distance-minimizing iterative material stress solver 46 3.4 Local-convexity data-driven identification (LCDDI) 48 3.4.1 Locally convex construction 49 3.4.2 Local convexity-preserving iterative material stress solver 51 3.4.3 Solution procedure for homogeneous and heterogenous elastic materials 53 Chapter 4 Numerical Verification of Data-Driven Identification Approaches 55 4.1 Methods 55 4.2 Example 1: elastic materials 56 4.2.1 Problem setup 56 4.2.2 Results 57 4.3 Example 2: composite materials 65 4.3.1 Problem setup 65 4.3.2 Results 66 4.4 Example 3: elastoplastic materials 72 4.4.1 Problem setup 73 4.4.2 Results 74 4.5 Discussion 80 4.5.1 Treatment of oversampling 80 4.5.2 Importance sampling 81 4.5.3 Full-domain analysis 82 4.6 Summary and concluding remarks 83 Chapter 5 Full-Field Stress and Strain Measurements Revealing Energy Dissipation Characteristics in Martensitic Band of CuAlMn Shape Memory Alloy 85 5.1 Materials and methods 86 5.1.1 Experimental method 86 5.1.2 Full-field stress and strain measurements (DIC+DDI) 87 5.2 Results 88 5.2.1 Material properties 88 5.2.2 Mechanical responses 90 5.2.3 Martensitic phase transformation 94 5.2.4 Dissipation energy 97 5.3 Discussion 101 5.4 Conclusions 105 Chapter 6 On the Decrease in Transformation Stress in a Bicrystal Cu-Al-Mn Shape-Memory Alloy during Cyclic Compressive Deformation 107 6.1 Materials and methods 107 6.1.1 Experimental method 107 6.1.2 Full-field stress and strain measurements (DIC+DDI) 109 6.2 Results 111 6.2.1 Cyclic superelastic behaviors of Cu-Al-Mn bicrystal sample 111 6.2.2 Correlations between residual strain and transformation stress 113 6.3 Discussion 118 6.4 Conclusions 125 Chapter 7 Data-driven Multiscale Finite Element Computation 127 7.1 Methods 127 7.1.1 Classical multiscale finite element method 127 7.1.2 Data-driven multiscale finite element method 129 7.1.3 Local-convexity data-driven computing for anisotropic elastic solids 132 7.2 Example 1: Isotropic composite cantilever beam 133 7.2.1 Problem setup 133 7.2.2 Results 136 7.3 Example 2: Multi-layer anisotropic composite cantilever beam 140 7.3.1 Problem setup 140 7.3.2 Results 143 7.4 Concluding remarks 144 Chapter 8 Conclusion and Perspectives 147 8.1 Contributions 147 8.2 Results summary 148 8.3 Future perspectives 151 Bibliography 153 | |
| dc.language.iso | en | |
| dc.subject | 數據驅動多尺度有限元素法 | zh_TW |
| dc.subject | 數據驅動計算力學 | zh_TW |
| dc.subject | 數據驅動鑑定法 | zh_TW |
| dc.subject | 材料數據獲取 | zh_TW |
| dc.subject | 材料數據庫品質 | zh_TW |
| dc.subject | 流形學習法 | zh_TW |
| dc.subject | 局部凸空間數據驅動鑑定法 | zh_TW |
| dc.subject | 全域非接觸式應力應變測量法 | zh_TW |
| dc.subject | 數位影像相關法 | zh_TW |
| dc.subject | 銅鋁錳形狀記憶合金 | zh_TW |
| dc.subject | 超彈性力學行為 | zh_TW |
| dc.subject | 能量消散 | zh_TW |
| dc.subject | 轉變應力 | zh_TW |
| dc.subject | 功能性疲勞 | zh_TW |
| dc.subject | Transformation stress | en |
| dc.subject | Data-driven multiscale finite element method (Data-driven FE2) | en |
| dc.subject | Functional fatigue | en |
| dc.subject | Data-driven computational mechanics (DDCM) | en |
| dc.subject | Data-driven identification (DDI) | en |
| dc.subject | Material data acquisition | en |
| dc.subject | Material database quality | en |
| dc.subject | Manifold learning | en |
| dc.subject | Local convexity data-driven identification (LCDDI) | en |
| dc.subject | Full-field non-contact stress and strain measurements | en |
| dc.subject | Digital image correlation (DIC) | en |
| dc.subject | Cu-Al-Mn shape memory alloys (SMA) | en |
| dc.subject | Superelasticity | en |
| dc.subject | Energy dissipation | en |
| dc.title | 以數據驅動的計算彈性固體力學及其應用 | zh_TW |
| dc.title | Data-Driven Computational Elastic Solid Mechanics and its Applications | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳志軒(Hsin-Tsai Liu),洪宏基(Chih-Yang Tseng),陳正宗,陳東陽 | |
| dc.subject.keyword | 數據驅動計算力學,數據驅動鑑定法,材料數據獲取,材料數據庫品質,流形學習法,局部凸空間數據驅動鑑定法,全域非接觸式應力應變測量法,數位影像相關法,銅鋁錳形狀記憶合金,超彈性力學行為,,能量消散,轉變應力,功能性疲勞,數據驅動多尺度有限元素法, | zh_TW |
| dc.subject.keyword | Data-driven computational mechanics (DDCM),Data-driven identification (DDI),Material data acquisition,Material database quality,Manifold learning,Local convexity data-driven identification (LCDDI),Full-field non-contact stress and strain measurements,Digital image correlation (DIC),Cu-Al-Mn shape memory alloys (SMA),Superelasticity,Energy dissipation,Transformation stress,Functional fatigue,Data-driven multiscale finite element method (Data-driven FE2), | en |
| dc.relation.page | 170 | |
| dc.identifier.doi | 10.6342/NTU202200014 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-01-07 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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