1. Multiscale modeling of materials or the importance of multidisciplinary dialogue Rémi Dingreville NYU-Poly Research Showcase Collaborative Opportunities in Science and Engineering NYU Langone Medical Center March 22, 2010 @: rdingre@poly.edurdingre@poly.edu

2. ➡ Development of self-consistent, validated and predictive sensitivity analysis computational tools to simulate mechanical response at the “mesoscale”. ➡ In the long run: provide guidelines on how to optimize the microstructure and materials’ length scales to develop and characterize a new class of functional materials. How lower length scales affect response? H2H2 H2H2 H2H2 ps μsμs s cm μmμm nm 20 nm Pd nanowire

3. Frustrating, challenging, intriguing... T, ∇ T σ ij, ∇ σ ij Burn-up Fuel microstructure Fuel properties Anisotropy Grain size Grain morphology Grain boundaries Dislocations Vacancies Bubbles, FP Porosity Defects Environment ➡ Fuel pins are complex coupled systems which become more complex with burn-up. ➡ Modeling investigation involves different length and time scales.

4. Fuel pin modeling strategy: MPALE: Multiple length and time scales Atomistic Physics Mesoscale PhysicsContinuum Physics Mechanical response Burn-up Thermal conductivity (Lagrangian MPM) Microstructure effects Texture evolution Grain coarsening Bubble transport (Calibrated MC) FP dynamics Diffusion theory (DFT) MPALE (Material Point Method)

5. ➡ Material point = representative volume/mass/energy. ➡ Pseudo-FEM strategy with Lagrangian grid. ➡ Solution and internal state variables on the material points [not the grid...]. ➡ Traditional thermo-mechanical constitutive models. Material Point Method Computational framework Mechanical response Crystal Plasticity Grain restructuring Calibrated Monte Carlo

6. Mechanically-informed grain restructuring Texture evolution by elastic loading “Softer” materials (w.r.t loading axis) survive Texture evolution by plastic loading Materials with smaller Schmid factor survive

7. 100nm90nm80nm70nm60nm50nm40nm30nm20nm10nm1nm Strand of DNA (2nm wide) Au-FePt nanocomposites Nanobelt (80nm) Niobium particles (5 nm) Smaller is different. But why? ➡ Lack of understanding of macroscopic behavior dues to surface effects, difference in length scales. ➡ Approach combining continuum mechanics and atomistic description. Differences between “conventional” and nano-materials Grain Core Grain Boundary Characteristic length (grain or particle size) of the microstructure Amount of grain boundaries (or particle/matrix interfaces in case of composites) per unit volume

8. Size dependency in materials properties Dividing surface concept E (MPa) x 0 8 (σ ij -σ 0 )dx = Σ αβ + σ t i H iαβ ?

9. Multiscale modeling in biological science Cellular-based multiscale modeling of the mechano-fluidic behavior of the aortic heart valve (with B. Griffith) Structure hierarchy in biological systems...Have some fresh lobster.

10. Multiscale modeling and fresh lobster... H2H2 H2H2 H2H2 ps μsμs s cm μmμm nm 20 nm Pd nanowire ➡ Multiscale modeling: dialogue at many time and length scales More physics at lower length scales More mathematics at the continuum scale. ➡ Hierarchical biological systems: many challenges to theory and experiments. Message of the day

11. Thank you for your attention Multiscale modeling and fresh lobster...