Flows containing long-chain polymers are viscoelastic on macroscopic length scales, but in microscale and nanoscale flows hybrid fluid-particle approaches are more suitable. Such an approach would enable more complex predictive modeling, including polymer-wall interactions like adsorption and elution, and flow-induced damage. Our polymer model is a Kramers freely jointed chain subject to stochastic and drag forces, both conservatively coupled to the fluid. We present new work aimed at demonstrating that the order of accuracy of our algorithm, which combines discrete Navier-Stokes with discrete stochastic particle dynamics, is second order in the time step. This improvement to our previous lower-order algorithm makes our overall method amenable to acceleration and higher resolution by adaptive mesh refinement.
Journal: TechConnect Briefs
Volume: 3, Nanotechnology 2008: Microsystems, Photonics, Sensors, Fluidics, Modeling, and Simulation – Technical Proceedings of the 2008 NSTI Nanotechnology Conference and Trade Show, Volume 3
Published: June 1, 2008
Pages: 425 - 428
Industry sectors: Medical & Biotech | Sensors, MEMS, Electronics
Topics: Micro & Bio Fluidics, Lab-on-Chip