Model reduction techniques for the stochastic simulations of dilute polymer solutions
Master of Science
This study develops reduced order models for the stochastic simulations of dilute polymeric solutions in shear and extensional flows. Reduced order models are obtained by modal analysis and balanced truncation with Brownian dynamics simulations. The reduced models offer reduction in the computational cost of the simulations of polymeric systems while predicting configurational properties accurately. Various model reduction techniques are compared to obtain the best candidate for carrying out the model reduction. We found that larger value of the timesteps can be used for simulations of the reduced models. However, the generation of Brownian force is the limiting step in the simulations of the reduced models. The simulation time of the reduced models will be significantly reduced if a ready made expression of the Brownian force can be derived. The reduced models may be used in micro-macro simulations to obtain constitutive relationships for a generic class of polymeric fluids to model complex flows of industrial interest.
Polymers; Chemical engineering; Applied sciences; Pure sciences