Computationally efficient multiscale modeling for probabilistic analysis of CFRP composites with micro-scale spatial randomness

2021 ◽  
pp. 114884
Author(s):  
Rudraprasad Bhattacharyya ◽  
Sankaran Mahadevan ◽  
Prodyot K. Basu
Keyword(s):  
Multiscale Modeling ◽  
Probabilistic Analysis ◽  
Computationally Efficient ◽  
Micro Scale ◽  
Cfrp Composites ◽  
Spatial Randomness

scholarly journals A 3D trabecular bone homogenization technique

2020 ◽  
Vol 22 (3) ◽  
Author(s):  
Marco C. Marques ◽  
Jorge Belinha ◽  
António F. Oliveira ◽  
Maria Cristinha M. Cespedes ◽  
Renato M. Natal Jorge
Keyword(s):  
Mechanical Properties ◽  
Mechanical Behavior ◽  
Trabecular Bone ◽  
Multiple Scales ◽  
Bone Adaptation ◽  
Computationally Efficient ◽  
Micro Scale ◽  
Special Cases ◽  
Homogeneous Models ◽  
Time Required

Purpose: Bone is a hierarchical material that can be characterized from the microscale to macroscale. Multiscale models make it possible to study bone remodeling, inducing bone adaptation by using information of bone multiple scales. This work proposes a computationally efficient homogenization methodology useful for multiscale analysis. This technique is capable to define the homogenized microscale mechanical properties of the trabecular bone highly heterogeneous medium. Methods: In this work, a morphology - based fabric tensor and a set of anisotropic phenomenological laws for bone tissue was used, in order to define the bone micro-scale mechanical properties. To validate the developed methodology, several examples were performed in order to analyze its numerical behavior. Thus, trabecular bone and fabricated benchmarks patches (representing special cases of trabecular bone morphologies) were analyzed under compression. Results: The results show that the developed technique is robust and capable to provide a consistent material homogenization, indicating that the homogeneous models were capable to accurately reproduce the micro-scale patch mechanical behavior. Conclusions: The developed method has shown to be robust, computationally less demanding and enabling the authors to obtain close results when comparing the heterogeneous models with equivalent homogenized models. Therefore, it is capable to accurately predict the micro-scale patch mechanical behavior in a fraction of the time required by classic homogenization techniques.

Multiscale Modeling and Analysis of DC/DC Converter Based on Macro and Micro-Scale Description

2020 ◽  
Vol 35 (1) ◽  
pp. 356-365
Author(s):  
Yunhua Liu ◽  
Bo Zhang ◽  
Fan Xie ◽  
Dongyuan Qiu ◽  
Yanfeng Chen
Keyword(s):  
Multiscale Modeling ◽  
Modeling And Analysis ◽  
Micro Scale

scholarly journals A micro-scale cutting model for UD CFRP composites with thermo-mechanical coupling

2017 ◽  
Vol 153 ◽  
pp. 18-31 ◽  
Author(s):  
Hui Cheng ◽  
Jiaying Gao ◽  
Orion Landauer Kafka ◽  
Kaifu Zhang ◽  
Bin Luo ◽  
...  
Keyword(s):  
Mechanical Coupling ◽  
Micro Scale ◽  
Cfrp Composites ◽  
Cutting Model

Computationally efficient model of the implanted knee for time-sensitive applications

2020 ◽  
Author(s):  
E Bori ◽  
A Navacchia ◽  
L Wang ◽  
L Duxbury ◽  
S McGuan ◽  
...  
Keyword(s):  
Computationally Efficient

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

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