자료유형  

 학위논문

Control Number  

0017161896

International Standard Book Number  

9798384453864

Dewey Decimal Classification Number  

620

Main Entry-Personal Name  

Tamur, Caglar.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Berkeley., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

107 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Li, Shaofan.

Dissertation Note  

Thesis (Ph.D.)--University of California, Berkeley, 2024.

Summary, Etc.  

요약The dissertation investigates the complex behavior of polymers through computational mechanics, employing numerical techniques that include peridynamics (PD), finite element methods (FEM), molecular dynamics (MD), and deep learning. Our aim is to develop a deeper understanding of the relationship between the polymer microstructure and the resultant mechanical properties, in the context of fracture mechanics, large deformation problems, and additive manufacturing.In the second chapter, we introduce a numerical framework to simulate the fracture response of elastomeric materials. The approach utilizes the bond-based peridynamics formulation, which is a nonlocal and meshfree alternative to the continuum-based methods. We introduced a novel bond potential into the framework, based on the non-Gaussian chain statistics theory, which treats the material as a complex network of randomly jointed chains with extensible links. The resultant formulation is capable of capturing the fracture and large deformation process of elastomeric materials, as demonstrated in several numerical examples and comparisons with the experimental data.The remaining chapters focus on the semicrystalline polymers that are used in additive manufacturing. In the third chapter, we have developed a constitutive law for crystalline polymers, based on molecular dynamics and machine learning. We have collected data from the MD simulations of Polyamide12 (PA12), which is used to train deep neural networks to capture the mechanical response of the material. We demonstrated that this novel approach can accurately provide a three-dimensional molecular-level anisotropic constitutive relation that can be used in macroscale mechanics methods such as the FEM. The final chapter consists of an analysis of the crystallization of polymers during the additive manufacturing process. By incorporating crystallization and melting models for PA12, we performed a heat transfer analysis using FEM to predict changes in crystallinity during the manufacturing process, which can help achieve the desired mechanical properties in the final product.

Subject Added Entry-Topical Term  

Engineering.

Subject Added Entry-Topical Term  

Mechanics.

Subject Added Entry-Topical Term  

Environmental engineering.

Index Term-Uncontrolled  

Computational mechanics

Index Term-Uncontrolled  

Deep learning

Index Term-Uncontrolled  

Molecular dynamics

Index Term-Uncontrolled  

Multiscale modeling

Index Term-Uncontrolled  

Peridynamics

Index Term-Uncontrolled  

Polymers

Added Entry-Corporate Name  

University of California, Berkeley Civil and Environmental Engineering

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:654564