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Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers

Authors: López RRSánchez LMAlazzam ABurnier JVStiharu INerguizian V


Affiliations

1 Department of Electrical Engineering, École de Technologie Supérieure, 1100 Notre Dame West, Montreal, QC H3C 1K3, Canada.
2 Cancer Research Program, RI-MUHC, McGill University, 1001 Decarie Boulevard, Montreal, QC H4A 3J1, Canada.
3 Department of Engineering, Universidad Autónoma de Querétaro, Cerro de las Campanas, Santiago de Querétaro 76010, Querétaro, Mexico.
4 Department of Mechanical Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates.
5 Department of Mechanical and Industrial Engineering, Concordia University, 1515 Saint Catherine West, Montreal, QC H3G 1M8, Canada.

Description

The shape and dimensions of a micromixer are key elements in the mixing process. Accurately quantifying the mixing efficiency enables the evaluation of the performance of a micromixer and the selection of the most suitable one for specific applications. In this paper, two methods are investigated to evaluate the mixing efficiency: a numerical model and an experimental model with a software image processing technique. Using two methods to calculate the mixing efficiency, in addition to corroborating the results and increasing their reliability, creates various possible approaches that can be selected depending on the circumstances, resources, amount of data to be processed and processing time. Image processing is an easy-to-implement tool, is applicable to different programming languages, is flexible, and provides a quick response that allows the calculation of the mixing efficiency using a process of filtering of images and quantifying the intensity of the color, which is associated with the percentage of mixing. The results showed high similarity between the two methods, with a difference ranging between 0 and 6% in all the evaluated points.


Keywords: image processingmicrofluidicsmicromixersmixing efficiencynumerical model


Links

PubMed: https://pubmed.ncbi.nlm.nih.gov/34577745/

DOI: 10.3390/mi12091102