Freeze-casting is a popular method to produce biomaterial scaffolds with highly porous structures. Three approaches are developed to predict the scaffold pore structure as function of experimental conditions including mold geometry, material and thermal boundary conditions. First, a mathematical model integrating Computational Fluid Dynamics (CFD) with Population Balance Model is developed to predict average pore size (APS) of 3D porous chitosan alginate scaffolds and to assess the influence of the geometrical parameters of mold on scaffold pore structure. The model predicted the crystallization pattern and APS for scaffolds cast in different diameter molds and filled to different heights. The predicted APS compared favorably with APS measurements from a corresponding experimental dataset, validating the model. Sensitivity analysis is performed to assess the response of the APS to the three geometrical parameters of the mold. The pore size is found to be most sensitive to the spacing between the wells and least sensitive to solution height. An image processing code is developed as second investigated approach using python and ImageJ open source software to analyze the microstructure of the scaffolds including pore size distribution, average pore size and average pore elongation relative to mold geometry. The image processing data are used to correlate the scaffold pore structure with the experimental conditions under which the scaffolds are produced. In the third approach, a deep learning neural network coupled with a support vector machine classifier is used to predict the scaffold pore structure based on CFD results obtained from the first approach. The validated models demonstrate the capability of the methods developed in this study for prediction and optimization of the APS of freeze-cast biomaterial scaffolds that could be applied to other compositions or applications.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Mechanical and Aerospace Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Rouhollahi, Amir, "Integration Of Computational Fluid Dynamics And Machine Learning For Modeling Scaffold Pore Structure For Tissue Engineering" (2019). Electronic Theses and Dissertations. 6880.