Reduced order modeling based shape optimization of surface acoustic wave driven microfluidic biochips

Biochips are physically and/or electronically controllable miniaturized labs. They are used for combinatorial chemical and biological analysis in environmental and medical studies. The precise positioning of the samples on the surface of the chip in picoliter to nanoliter volumes can be done either by means of external forces (active devices) or by specific geometric patterns (passive devices). The active devices which will be considered here are microfluidic biochips where the core of the technology are nanopumps featuring surface acoustic waves generated by electric pulses of high frequency. These waves propagate like a miniaturized earthquake, enter the fluid filled channels on top of the chip and cause an acoustic streaming in the fluid which provides the transport of the samples. The mathematical model represents a multiphysics problem consisting of the piezoelectric equations coupled with multiscale compressible Navier-Stokes equations that have to be treated by an appropriate homogenization. We discuss the modeling approach, present algorithmic tools for the numerical simulation and address optimal design issues. In particular, the optimal design of specific parts of the biochips leads to large-scale optimization problems. In order to reduce the computational complexity, we present a combination of domain decomposition and balanced The work of the authors has been supported in part by NSF grants DMS-0511624, DMS-0707602, DMS-0810176, DMS-0811153, DMS-0914788, by AFOSR grant FA955009-1-0225, and by the German National Science Foundation (DFG) within the Priority Program SPP 1253 Preprint submitted to Mathematics and Computers in Simulation October 1, 2009 truncation model reduction which allows explicit error bounds for the error between the reduced order and the fine-scale optimization problem. It is shown that this approach gives rise to a significant reduction of the problem size while maintaining the accuracy of the approximation.