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Abstract Autonomous motion and motility are hallmarks of active matter. Active agents, such as biological cells and synthetic colloidal particles, consume internal energy or extract energy from the environment to generate self-propulsion and locomotion. These systems are persistently out of equilibrium due to continuous energy consumption. It is known that pressure is not always a state function for generic active matter. Torque interaction between active constituents and confinement renders the pressure of the system a boundary-dependent property. The mechanical pressure of anisotropic active particles depends on their microscopic interactions with a solid wall. Using self-propelled dumbbells confined by solid walls as a model system, we perform numerical simulations to explore how variations in the wall stiffness influence the mechanical pressure of dry active matter. In contrast to previous findings, we find that mechanical pressure can be independent of the interaction of anisotropic active particles with walls, even in the presence of intrinsic torque interaction. Particularly, the dependency of pressure on the wall stiffness vanishes when the stiffness is above a critical level. In such a limit, the dynamics of dumbbells near the walls are randomized due to the large torque experienced by the dumbbells, leading to the recovery of pressure as a state variable of density.
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Supervised learning for accurate mesoscale simulations of suspension flow in wall-bounded geometries
Herein, we have employed a supervised learning approach combined with Core-Modified Dissipative Particle Dynamics Simulations (CM-DPD) in order to develop and design a reliable physics-based computational model that will be used in studying confined flow of suspensions. CM-DPD was recently developed and has shown promising performance in capturing rheological behavior of colloidal suspensions; however, the model becomes problematic when the flow of the material is confined between two walls. Wall-penetration by the particles is an unphysical phenomenon that occurs in coarse-grained simulations such as Dissipative Particle Dynamics (DPD) that mostly rely on soft inter-particle interactions. Different solutions to this problem have been proposed in the literature; however, no reports have been given on how to deal with walls using CM-DPD. Due to complexity of interactions and system parameters, designing a realistic simulation model is not a trivial task. Therefore, in this work we have trained a Random Forest (RF) for predicting wall penetration as we vary input parameters such as interaction potentials, flow rate, volume fraction of colloidal particles, and confinement ratio. The RF predictions were compared against simulation tests, and a sufficiently high accuracy and low errors were obtained. This study shows the viability and potentiality of ML combined with DPD to perform parametric studies in complex fluids.
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- Award ID(s):
- 1703919
- PAR ID:
- 10339782
- Date Published:
- Journal Name:
- Physics of Fluids
- Volume:
- 34
- Issue:
- 5
- ISSN:
- 1070-6631
- Page Range / eLocation ID:
- 053110
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0086759
