원문정보
보안공학연구지원센터(IJUNESST)
International Journal of u- and e- Service, Science and Technology
Vol.8 No.8
2015.08
pp.215-224
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In order to reduce the data storage and improve data compression ratio of the stiffness matrix of 3D finite element, after analyzed the relationship between nonzero submatrix and generalized adjacent nodes of the stiffness matrix, this paper proposes an improved stiffness matrix compression algorithm, which combined negative sign compressed sparse line and a rider to store binary classification method. Then the improved algorithm is applied to the storage of the stiffness matrix of 3D-FEM. Through experimental simulation, the results show that this method saves a lot of storage space to ensure the validity of data for finite element analysis.
목차
Abstract
1. Introduction
2. Storage Method of the Stiffness Matrix based on CSR
2.1 Traditional CSR Storage Method of the Stiffness Matrix
2.2 CSR storage Method based on the Distribution Law of Nonzero Submatrix
2.3 Negative Sign CSR Storage Method
2.4 Generation of Stiffness Matrix
3. Storage Method based on Rider Binary Classification
3.1 Stiffness Matrix Compression and Storage Method based on Rider Binary Classification
3.2 The Stiffness Matrix Storage and Reading Algorithm based on Rider Binary Classification and Negative Sign CSR
4. Algorithm Verification
4.1 A Stiffness Matrix
4.2 Beam Model
5. Conclusions
ACKNOWLEDGEMENTS
References
1. Introduction
2. Storage Method of the Stiffness Matrix based on CSR
2.1 Traditional CSR Storage Method of the Stiffness Matrix
2.2 CSR storage Method based on the Distribution Law of Nonzero Submatrix
2.3 Negative Sign CSR Storage Method
2.4 Generation of Stiffness Matrix
3. Storage Method based on Rider Binary Classification
3.1 Stiffness Matrix Compression and Storage Method based on Rider Binary Classification
3.2 The Stiffness Matrix Storage and Reading Algorithm based on Rider Binary Classification and Negative Sign CSR
4. Algorithm Verification
4.1 A Stiffness Matrix
4.2 Beam Model
5. Conclusions
ACKNOWLEDGEMENTS
References
저자정보
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자료제공 : 네이버학술정보