A numerical model for translational and rotational momentum transfer of soft deformable micro particles in dilute two-phase flows

• Applicant: Professor Dr.-Ing. Paul Steinmann
• Subject Area: Mechanics ; Applied Mechanics, Statics and Dynamics ; Mechanical Process Engineering ; Fluid Mechanics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 510050592

In project phase I, we established a strong foundation for modeling and simulating soft micro-particles suspended in viscous flow using our novel, efficient and utmost versatile pseudo-rigid body approach in combination with Lagrangian point-particle tracking. Building upon these advancements, we are able to target six objectives for Phase II. As a first step, we will extend our modeling framework to encompass a broader range of constitutive models beyond the Neo-Hookean elastic framework, allowing for a more accurate representation of the mechanical behavior of various soft particles, including those exhibiting hyperelastic and viscoelastic properties. Furthermore, we acknowledge the importance of considering surface tension effects for studying liquid particles. Thus, we aim to extend our model to include this phenomenon, which will enhance the fidelity of our simulations and consequently will enhance our understanding of shape dynamics and barycentric motion in soft particle systems. Furthermore, we aim to investigate the dynamics of soft particles with inhomogeneous mass distributions, such as radially symmetric density distributions and off-center mass distributions. This objective aims to elucidate how such inhomogeneities affect particle trajectories and shape dynamics. As a next step, we aim to explore the heat transfer characteristics of soft particles in fluid flows, particularly focusing on thermo-sensitive materials that exhibit significant changes in rheological properties with temperature variations. This will involve developing a computational model that links viscoelastic behavior with heat transfer dynamics. In addition, we aim to expand our neural network approach to account for affinely mapped superellipsoids, which arise when superellipsoids undergo affine deformation. This will ensure accurate modeling of the shape dynamics of soft particles that do not retain their initial shape-class during deformation. Lastly, we aim to address the under-researched area of soft particles in turbulent environments. To date, the state-of-the art methods for soft particles from the literature involve surface discretization and are thus not feasible to study statistically relevant numbers of soft particles. However, using our novel and efficient pseudo-rigid body approach, we are able to conduct a thorough investigation of soft particles in turbulent flows, leveraging our novel modeling approach to gain insights into their dynamics and interactions in complex flow environments. In this context, we couple our pseudo-rigid body approach with existing turbulence databases, which enables the study of trajectories, deformation, and potential clustering behavior of soft particles under turbulent conditions. Taken together, phase II aims to advance our understanding of soft micro-particles in various fluid dynamics contexts, with significant implications for practical applications across multiple fields.

• DFG Programme: Research Grants