A Novel Topological Optimization Method for Multi-Substance-Sensor Microfluidics Devices

Intellectual biochemical detection is an essential part of a smart society, which is important for future personal health management. The judgment process of health level should be carried out by comprehensive consideration of multiple indicators. To achieve that, multi-substance sensors are necessary. Due to the high-throughput and rapid detection characteristics, the multi-material testing microfluidic devices are promising bio-monitoring devices. To ensure the detection accuracy of the multi-substance-detection microfluidic devices, the topological structure of the channels must be designed carefully. Not only the flow rate but also the substance residue is decided by the topological structure of the channels. Thus, in this paper, we report a genetic algorithm-based topological optimization method for microfluidic devices. The material residue is modeled as the energy dissipation caused by friction. The topology and the position of the inlet and outlets are jointly optimized to minimize the energy dissipation with different desired flow rates at the outlets. The proposed genetic algorithm-based two-tier method enhanced global search ability. The simulation and experiment results show that our algorithm can decrease the energy dissipation on the premise of ensuring the outlet flow rate with comparable computational complexity.