Multiscale Biomechanical Modeling of the Brain
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51 559 Ft
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Product details:
- Publisher Elsevier Science
- Date of Publication 2 November 2021
- ISBN 9780128181447
- Binding Paperback
- No. of pages276 pages
- Size 234x190 mm
- Weight 450 g
- Language English
- Illustrations 60 illustrations (30 in full color) 151
Categories
Long description:
Multiscale Biomechanical Modeling of the Brain discusses the constitutive modeling of the brain at various length scales (nanoscale, microscale, mesoscale, macroscale and structural scale). In each scale, the book describes the state-of-the- experimental and computational tools used to quantify critical deformational information at each length scale. Then, at the structural scale, several user-based constitutive material models are presented, along with real-world boundary value problems. Lastly, design and optimization concepts are presented for use in occupant-centric design frameworks. This book is useful for both academia and industry applications that cover basic science aspects or applied research in head and brain protection.
The multiscale approach to this topic is unique, and not found in other books. It includes meticulously selected materials that aim to connect the mechanistic analysis of the brain tissue at size scales ranging from subcellular to organ levels.
Table of Contents:
1. Introduction to multiscale modeling
2. Downscaling (Macroscale to nanoscale) multiscale paradigm -Discuss on things related damage/strength, stiffness, vibrations/resonance -Temperature, strain rate and stress state -Fatigue, creep and overloads
3. DFT; Electronics for organic molecules
4. Nanoscale Atomistics and Molecular Dynamics
5. Microscale Mechano-Physiological Modeling and Coarse-Grain Molecular Dynamics
6. Mesoscale Finite Element Modeling
7. Macroscale Thermodynamic Framework and Modeling
8. Structural Scale - Brain's VUmat file calibration, and validation - Blast Finite Element Simulations (high rate) -Blunt Impact Simulations (intermediate rate) - Car Crash Simulations (intermediate rate)
9. Robust Multi-objective design and optimization
10. Summary and conclusions
