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Combining Finite Element with Data Analytical Approaches for Structure-Property Modeling in Polymer Nanocomposites
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Combining Finite Element with Data Analytical Approaches for Structure-Property Modeling in Polymer Nanocomposites. |
| 개인저자 | Wang, Yixing. |
| 단체저자명 | Northwestern University. Mechanical Engineering. |
| 발행사항 | [S.l.]: Northwestern University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 135 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
| ISBN | 9781687926074 |
| 학위논문주기 | Thesis (Ph.D.)--Northwestern University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Brinson, Cate. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Polymer nanocomposites have attracted great interest in recent years because of their potential as tailored materials with enhanced properties. Recent experiments have shown that polymer nanocomposites are able to achieve significant improvement in dielectrical, thermal, mechanical and other physical properties compared with their parent polymer systems. More importantly, these outstanding properties can be achieved at low filler loadings such that the polymer system does not sacrifice the advantages of easy processability. Despite the excellent performance of advanced materials including polymer nanocomposites, the time frame of development to application of those materials in industry is long, typically over 10 years. Therefore, a top priority of researchers and engineers is to reduce the time required to bring advanced materials from laborites to the market. The Material Genome Initiative (MGI), proposed by the White House, is one of the major efforts aimed at addressing this challenge. In order to better understand the behavior of these materials and further design materials with targeted properties, researchers have relied on the materials science paradigm of processing-structure-property (PSP) linkage. Data-driven approaches founded on PSP linkage have much attention recently and have become a hot topic in many areas of materials research. Data-driven approaches combine materials science, computer science and statistics with the goal of expediating material discovery by utilizing past research data to draw new fundamental understanding. To facilitate the development of data archiving, sharing and development of data-driven approaches, efforts have been made to create online databases for fast queries and reference. A data resource, NanoMine, has been developed to accelerate material design for polymer nanocomposites. NanoMine allows fast data queries, visualization, and sharing, as well as a number of tools for analysis including microstructure descriptor identification and reconstruction tools. There are three critical requirements needed to expand the functionality of NanoMine for usage by the broader nanocomoposite community. First, more analysis and module tools that better model the behavior of the material must be built. Second, case studies highlighting the capabilities of NanoMine must be developed using curated data to quantify PSP relationships and elucidate material mechanisms and physics. Finally, it is essential to put continuous efforts toward robust data curation, which expands the size of database and enables development systematic studies.In relation to the three needs above, this dissertation first presents a combined finite element analysis (FEA) and optimization approach to accelerate the identification of interphase properties given experimental data of bulk nanocomposite properties. This approach is tested on both simulations of dielectric and viscoelastic properties in nanocomposites. Our work provides insight into identifying interphase properties for polymer nanocomposites using adaptive optimization and demonstrates the potential of data-driven approaches for achieving a deeper understanding of interphase properties that have proven difficult to directly characterize experimentally. Secondly, we present a novel deep learning approach that probes the structure-property relationships in polymer nanocomposites. Analysis of archived experimental data motivates the development of a computational model that allows for demonstration of this approach and gives flexibility to sufficiently explore a wide range of structures. Lastly, to facilitate the data curation process from literature sources, by applying recent machine learning and natural language processing methods, an end-to-end framework to extract material processing and synthesis information from full-length journal articles is developed. The proposed models and methods are shown herein to be a powerful tool to supplement labor-intensive manual curation and improve the efficiency of data input for the database. |
| 일반주제명 | Mechanical engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
