Project Details
Projekt
Bayesian Uncertainty Quantification for Microfluidics: Assessing and Improving the Reliability of Reduced-Order Models and Sample Detection Schemes
Applicant
Dr.-Ing. Henning Bonart
Subject Area
Fluid Mechanics
Term
from 2021 to 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 459970841
Microfluidics deals with very small fluid volumes and geometries and enables promising applications including lab-on-a-chips for drug discovery and liquid-infused surfaces (LIS) in medicine. Often however, the uncertainty in the measurement data and mathematical models, as well as the drawn conclusions and decisions, are not statistically quantified. In Bayesian uncertainty quantification (UQ), data and models are systematically integrated. It has demonstrated tremendous potential in a broad range including weather and election prediction. Unfortunately, it is still seldom applied in microfluidics.The goal of this project is therefore to show that Bayesian UQ offers a great benefit for research and applications involving microfluidics, and is practically and computationally feasible even for complex physical phenomena. Therefore, two interesting and relevant test cases involving small scale fluid mechanics, one basic research and one application case, are used to apply sophisticated Bayesian methods to noisy and uncertain data from microfluidic experiments. In the first case, a predictive reduced-order model for the dynamics of holes in thin liquid films on vibrating surfaces will be developed and quantitatively assessed using Bayesian model comparison. The resulting model will enhance our understanding of the hole dynamics and help in the design of LIS. In the second case, an automatized detection scheme for samples at low concentration from noisy measurement data in microchannels transported via isotachophoresis will be developed. A sophisticated detector will be integrated with Bayesian statistics to allow for traceable and transparent decisions under uncertainty. Consequently, this then will allow the automatized usage of microfluidic detection schemes, for example in medical diagnostics or high-throughput screening applications. To allow other researchers to rapidly transfer the applied methods of Bayesian UQ to their specific microfluidic problems and applications, tutorial cases as well as proof-of-concepts will be provided. This highly interdisciplinary project combines fluid dynamic experiments, mathematical modeling, and Bayesian statistics to provide novel and reliable answers to microfluidic problems. The results of this project might allow for even more involved applications of Bayesian UQ. For example, one interesting question would be to quantitatively compare, and combine, physic-based models and data-driven or machine learning models for complex microfluidics. Methods from Bayesian UQ, as will be applied in this project, provide a solid foundation for this future research.
DFG Programme
WBP Fellowship
International Connection
USA
