Soft Materials | Seminar
Prof. Safa Jamali
The MaP Doctoral School | Soft Materials track cordially invites you to two seminars by Prof. Safa Jamali of Northeastern University.
Prof. Safa Jamali, external page Northeastern University
Expertise: colloidal suspensions, computational fluid dynamics, hemorheology and hemodynamics, mesoscale computational science, microstructure-macroscopic properties relationship in complex fluids, physics of living systems, rheology and physics of complex and structured fluids
Soft Materials | Seminars
- 22.11.2023: Micro- and Meso-Mechanics of Dense Suspensions Under Flow
- 29.11.2023: Physics-Discovery through Machine Learning for Complex Fluids and Soft Matter
MaP Doctoral School will offer coffee/tea and sweet snacks after the seminars.
Prof. Safa Jamali is hosted by Prof. Lucio Isa and Prof. Jan Vermant for the whole month of November. If you would like to meet Prof. Safa Jamali, please get in touch with them directly.
(Scroll down for abstracts)
Micro- and Meso-Mechanics of Dense Suspensions Under Flow
Dense suspensions commonly exhibit exotic rheological behavior such as shear thickening in response to large applied deformations. This is exemplified in fun videos of people running on a pool of cornstarch and water and sinking in while standing still. Naturally, the field of dense suspension mechanics attracts fluid mechanicians and granular physicists together, and thus has been a center of debate and attention for decades. Nonetheless, a consensus is beginning to emerge over the past few years on how a simple mixture of particles and fluid can shear thicken. I will introduce some historical background into the problem, and the physical consequences of two important theories: frictional contacts, and hydrodynamics. I will then use a series of computational and network science tools to gain insight into this complex physical problem. At the particle-level, micromechanics of the shear thickening will be discussed with respect to governing forces in different scenarios. At the cluster level, mesomechanics are discussed with regards to formation and evolution of force and contact clusters and networks.
Physics-Discovery through Machine Learning for Complex Fluids and Soft Matter
The ability to concisely describe the dynamical behavior of soft materials through closed form constitutive relations holds the key to accelerated and informed design of materials and processes. The conventional approach is to construct constitutive relations through simplifying assumptions and approximating the time- and rate-dependent stress response of a complex fluid to an imposed deformation. While traditional frameworks have been foundational to our current understanding of soft materials, they often face a two-fold existential limitation: (i) constructed on ideal and generalized assumptions, precise recovery of material-specific details is usually serendipitous, if possible, and (ii) inherent biases that are involved by making those assumptions commonly come at the cost of new physical insight. This work introduces a novel approach by leveraging recent advances in scientific machine learning methodologies to discover the governing constitutive equation from experimental data for complex fluids. Our Rheology-informed Neural Network (RhiNN) framework is found capable of learning the hidden rheology of a complex fluid through a limited number of experiments. This is followed by construction of an unbiased material-specific constitutive relation that accurately describes a wide range of bulk dynamical behavior of the material. While extremely efficient in closed-form model discovery for a real-world complex system, the model also provides new insight into the underpinning physics of the material.