Single Chain Slip-Spring Model for Fast Rheology Simulations of Entangled Polymers on GPU

Uneyama, Takashi

doi:10.1678/rheology.39.135

Cited by 27 publications

(26 citation statements)

References 44 publications

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“…In this paper, we present a detailed explanation of a multiscale method that bridges the hydrodynamic motions of fluids using computational fluid dynamics (CFD) and the microscopic (or mesoscopic) dynamics of polymer configurations using molecular dynamics (MD) [or coarse-grained (CG) [2][3][4][5][6][7][8][9][10] ] simulations. The concept of bridging microscale and macroscale dynamics is also important for other flow problems of softmatters with complex internal degrees of freedom (e.g., colloidal dispersion, liquid crystal, and glass).…”

Section: Introductionmentioning

confidence: 99%

Multiscale Modeling for Polymeric Flow: Particle-Fluid Bridging Scale Methods

et al. 2013

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Multiscale simulation methods have been developed based on the local stress sampling strategy and applied to three flow problems with different difficulty levels: (a) general flow problems of simple fluids, (b) parallel (one-dimensional) flow problems of polymeric liquids, and (c) general (two-or three-dimensional) flow problems of polymeric liquids. In our multiscale methods, the local stress of each fluid element is calculated directly by performing microscopic or mesoscopic simulations according to the local flow quantities instead of using any constitutive relations. For simple fluids (a), such as the Lenard-Jones liquid, a multiscale method combining molecular dynamics (MD) and computational fluid dynamics (CFD) simulations is developed rather straightforwardly based on the local stationarity assumption without memories of the flow history. For polymeric liquids in parallel flows (b), the multiscale method is extended to take into account the memory effects that arise in hydrodynamic stress due to the slow relaxation of polymer-chain conformations. The memory of polymer dynamics on each fluid element is thus resolved by performing MD simulations in which cells are fixed at the mesh nodes of the CFD simulations. The complicated viscoelastic flow behaviors of a polymeric liquid confined between oscillating plates are simulated using the multiscale method. For general (two-or three-dimensional) flow problems of polymeric liquids (c), it is necessary to trace the history of microscopic information such as polymer-chain conformation, which carries the memories of past flow history, along the streamline of each fluid element. A Lagrangian-based CFD is thus implemented to correctly advect the polymerchain conformation consistently with the flow. On each fluid element, coarse-grained polymer simulations are carried out to consider the dynamics of entangled polymer chains that show extremely slow relaxation compared to microscopic time scales. This method is successfully applied to simulate a flow around a cylindrical obstacle.

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Section: Introductionmentioning

confidence: 99%

Multiscale Modeling for Polymeric Flow: Particle-Fluid Bridging Scale Methods

et al. 2013

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“…Moreover, the large number of degrees of freedom in entangled polymer melts makes it difficult for us to develop a constitutive equation that describes the relation between the stress tensor and the strain (or strain-rate) tensor in an entangled polymer melt. To overcome the difficulty in the flow prediction of entangled polymer melts, we have developed a new multiscale simulation method [1] that entails macroscopic fluid particle simulation [2,3] and microscopic entangled polymer dynamics simulation [4][5][6][7][8]. Using fluid particle simulation where each fluid particle has a polymer simulator that describes the polymer states in the fluid particles themselves, we can accurately consider the flow history of the polymers in fluid particles.…”

Section: Introductionmentioning

confidence: 99%

“…Using fluid particle simulation where each fluid particle has a polymer simulator that describes the polymer states in the fluid particles themselves, we can accurately consider the flow history of the polymers in fluid particles. Entangled polymer dynamics simulation [4][5][6][7][8] is based on the reptation theory [9][10][11], where the dynamics of a polymer chain is constrained in a tube created by the surrounding polymers because of the excluded volume effect and the entanglements between polymers. Because each polymer chain in entangled polymer dynamics simulation is described by the number of hypothetical entanglement points, Z, and the tube segments r s j ( j = 1, · · · , Z) connecting the entanglement points on the chain, we can considerably decrease the large number of degrees of freedom into a manageable number.…”

Section: Introductionmentioning

confidence: 99%

Flow-History-Dependent Behavior of Entangled Polymer Melt Flow Analyzed by Multiscale Simulation

Murashima

Taniguchi

2012

J. Phys. Soc. Jpn.

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Polymer melts exhibit flow-history-dependent behavior. To clearly show this behavior, we have investigated the flow behavior of an entangled polymer melt around two cylinders placed in tandem along the flow direction in a two-dimensional periodic system. In this system, polymer states around a cylinder on the downstream side differ from those around a cylinder on the upstream side because the former have a memory of the strain they experienced when passing around the cylinder on the upstream side but the latter have no such memory. Therefore, the shear stress distributions around two cylinders are found to differ. Moreover, we have found that the mean flow velocity decreases accordingly with increasing distance between the two cylinders, whereas the applied external force is constant. Although this behavior is consistent with that of the Newtonian fluid, the flow-historydependent behavior is found to enhance the reduction in the flow resistance. We have also discussed the microscopic states during the flow, observing the mean length of polymer chains, the mean number of entanglements, and the degree of orientation of polymer chains, which represented the flowhistory-dependent behavior that reflects each relaxation process.

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“…Sampling the model: generate polymer conformation by Gaussian distribution, sample Z from Poisson distribution, generate Z slip-springs by uniform distribution and each anchoring point from the Gaussian distribution. The concrete form of the distributions above refer to [16].…”

Section: Fluctuations In the Number Of Entanglementsmentioning

confidence: 99%

A Multichain Slip-Spring Model with Fluctuating Number of Entanglements Based On Single-Chain Slip-Spring Model

Ma¹,

Tan²,

Yu³

2017

Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016)

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Abstract. We present a multichain slip-spring model with fluctuating number of entanglements for dynamics of dense polymer melts, which is based on the single chain slip-spring model by Likhtman. By introducing the exclude volume interactions, a multichain version of slip-spring model can be presented with little modification. With this extension, inhomogeneous polymer system can be described properly. The renewal rules for the slip-spring satisfy the detailed balance condition and the number of slip-springs is no longer constant but controlled through a chemical potential. This completion can predict the Possion distribution of Z under equilibrium states and the decrease in the number of entanglements when applied a steady shear flow. The results agree with theoretical predictions and experimental phenomenon. The model bridges the single chain slipspring model and multichain slip-spring model.

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Single Chain Slip-Spring Model for Fast Rheology Simulations of Entangled Polymers on GPU

Cited by 27 publications

References 44 publications

Multiscale Modeling for Polymeric Flow: Particle-Fluid Bridging Scale Methods

Multiscale Modeling for Polymeric Flow: Particle-Fluid Bridging Scale Methods

Flow-History-Dependent Behavior of Entangled Polymer Melt Flow Analyzed by Multiscale Simulation

A Multichain Slip-Spring Model with Fluctuating Number of Entanglements Based On Single-Chain Slip-Spring Model

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