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Even though the Dissipative Particle Dynamics (DPD) has shown its worth in a variety of research areas, it has been rarely used for polymer dynamics, particularly in dilute and semi-dilute conditions and under imposed flow fields. For such applications, the most popular technique has been Brownian dynamics (BD), even though the formulation of the same may be complicated for flow in complex geometries, which is straightforward for DPD. This is partly due to the flexibility of BD simulations to mimic any dynamic regime for polymer solutions by independently tuning hydrodynamic interactions (HI) and excluded volume (EV). In this study, we reveal that DPD also offers a similar flexibility and the regimes with respect to dominant EV and HI can be selected as conveniently as BD. This flexibility is achieved by tuning the repulsive interaction parameter of polymer beads and the spring length (which determines the chain resolution). Our results show that the former sets the chain size (and thus, EV) while the latter can be used to set the HI, nearly independently of each other. Thus, any rheological regime of certain level of EV and HI can be attained by appropriately tuning only these two parameters, providing a flexibility of similar levels as BD simulations. We further indicate the suitability of DPD by comparing rheological predictions with equivalent models in BD. For this, we imposed startup uniaxial extensional flows and steady shear flows on the system. Our results indicate the consistency of DPD with BD simulations, which is known to agree well with experiments.

Brownian dynamics simulations of fairly long, highly detailed polymer chains, at the resolution of a single Kuhn step, remains computationally prohibitive even on the modern processors. This is especially true when the beads on the chain experience hydrodynamic interactions (HI), which requires the usage of methods like Cholesky decomposition of large matrices at every timestep. In this study, we perform Petascale BD simulations, with HI, of fairly long and highly resolved polymer chains on modern GPUs. Our results clearly highlight the inadequacies of the typical models that use beads connected by springs. In this manuscript, firstly, we present the details of a highly scalable, parallel hybrid code implemented on a GPU for BD simulations of chains resolved to a single Kuhn step. In this hybrid code using CUDA and MPI, we have incorporated HI using the Cholesky decomposition method. Next, we validate the GPU implementation extensively with theoretical expectations for polymer chains at equilibrium and in flow with results in the absence of HI. Further, our results in flow with HI show significantly different temporal variations of stretch, in both startup extensional and shear flows, relative to the conventional bead-spring models. In all cases investigated, the ensemble averaged chain stretch is much lower than bead-spring predictions. Also, quite remarkably, our GPU implementation shows a scaling of $\sim$$N^{1.2}$ and $\sim$$N^{2.2}$ of the computational times for shorter and longer chains in the most modern available GPU, respectively, which is significantly lower than the theoretically expected $\sim$$N^{3}$. We expect our methods and results to pave the way for further analysis of polymer physics in flow fields, with long and highly detailed chain models., Comment: 14 pages, 15 figures