Details
| Original language | English |
|---|---|
| Title of host publication | Virtual Design and Validation |
| Place of Publication | Cham |
| Publisher | Springer Nature |
| Pages | 147-166 |
| Number of pages | 20 |
| ISBN (electronic) | 9783030381561 |
| ISBN (print) | 9783030381554 |
| Publication status | Published - 4 Mar 2020 |
Publication series
| Name | Lecture Notes in Applied and Computational Mechanics |
|---|---|
| Volume | 93 |
| ISSN (Print) | 1613-7736 |
| ISSN (electronic) | 1860-0816 |
Abstract
This chapter introduces a methodology to implement systematically spatially varying fiber misalignment distribution characterized experimentally into numerical modeling for failure surface analyses under in-plane loading conditions in compressive domain. If stochastically characterized spectral density of fiber misalignment by performing averaging over measured data as an ensemble is available, the approach allows designers to enhance the efficient usage of Fiber Reinforced Polymers (FRPs) by utilizing maximum capacity of the material with calculable reliability. In the present work, Fourier transform algorithms generally used in signal processing theory, are employed to generate representative distributions of fiber misalignments. The generated distributions are then mapped onto a numerical model as fluctuations of the material orientations. Through Monte Carlo analyses, probability distribution of peak stresses are subsequently calculated. This information is then used to define a probabilistic failure surface.
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computational Theory and Mathematics
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Virtual Design and Validation . Cham: Springer Nature, 2020. p. 147-166 (Lecture Notes in Applied and Computational Mechanics; Vol. 93).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - The representation of fiber misalignment distributions in numerical modeling of compressive failure of fiber reinforced polymers
AU - Safdar, N.
AU - Daum, B.
AU - Rolfes, R.
AU - Allix, O.
PY - 2020/3/4
Y1 - 2020/3/4
N2 - This chapter introduces a methodology to implement systematically spatially varying fiber misalignment distribution characterized experimentally into numerical modeling for failure surface analyses under in-plane loading conditions in compressive domain. If stochastically characterized spectral density of fiber misalignment by performing averaging over measured data as an ensemble is available, the approach allows designers to enhance the efficient usage of Fiber Reinforced Polymers (FRPs) by utilizing maximum capacity of the material with calculable reliability. In the present work, Fourier transform algorithms generally used in signal processing theory, are employed to generate representative distributions of fiber misalignments. The generated distributions are then mapped onto a numerical model as fluctuations of the material orientations. Through Monte Carlo analyses, probability distribution of peak stresses are subsequently calculated. This information is then used to define a probabilistic failure surface.
AB - This chapter introduces a methodology to implement systematically spatially varying fiber misalignment distribution characterized experimentally into numerical modeling for failure surface analyses under in-plane loading conditions in compressive domain. If stochastically characterized spectral density of fiber misalignment by performing averaging over measured data as an ensemble is available, the approach allows designers to enhance the efficient usage of Fiber Reinforced Polymers (FRPs) by utilizing maximum capacity of the material with calculable reliability. In the present work, Fourier transform algorithms generally used in signal processing theory, are employed to generate representative distributions of fiber misalignments. The generated distributions are then mapped onto a numerical model as fluctuations of the material orientations. Through Monte Carlo analyses, probability distribution of peak stresses are subsequently calculated. This information is then used to define a probabilistic failure surface.
UR - http://www.scopus.com/inward/record.url?scp=85081541034&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-38156-1_8
DO - 10.1007/978-3-030-38156-1_8
M3 - Contribution to book/anthology
AN - SCOPUS:85081541034
SN - 9783030381554
T3 - Lecture Notes in Applied and Computational Mechanics
SP - 147
EP - 166
BT - Virtual Design and Validation
PB - Springer Nature
CY - Cham
ER -