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Multi-Fidelity Surrogate And Reduced-Order Model-Based Microfluidic Concentration Gradient Generator Design, Haizhou Yang
Multi-Fidelity Surrogate And Reduced-Order Model-Based Microfluidic Concentration Gradient Generator Design, Haizhou Yang
Theses and Dissertations
The microfluidic concentration gradient generator (μCGG) is an important device to generate and maintain concentration gradients (CGs) of biomolecules for understanding and controlling biological processes. However, determining the optimal operating parameters of μCGG is still a significant challenge, especially for complex CGs in cascaded networks. To tackle such a challenge, this study presents multi-fidelity surrogate and reduced-order model-based optimization methodologies for accurate and computationally efficient design of μCGGs.
The surrogate-based optimization (SBO) method is first proposed for the design optimization of μCGGs based on an efficient physics-based component model (PBCM). Various combinations of regression and correlation functions in Kriging and …