Electronic Thesis and Dissertation Repository


Master of Science


Medical Biophysics


Dr. Daniel Goldman

2nd Supervisor

Dr. Dwayne Jackson

Joint Supervisor


The objective of this work was to develop a computational model that could accurately predict blood flow in skeletal muscle arteriolar trees in the absence of complete boundary data. We used arteriolar trees in the rat gluteus maximus muscle (GM) that were reconstructed from montages obtained via intravital videomicroscopy, and incorporated a recently published method for approximating unknown boundary conditions into our existing steady-state model of two-phase blood flow. For varying numbers of unknown boundary conditions, we used the new flow model and GM arteriolar geometry to approximately match red blood cell (RBC) flows corresponding to experimental measurements. We showed that this method gives errors that decrease as the number of known boundary conditions increases. We also showed that specifying total blood flow into the arteriolar tree decreases the mean RBC flow error and its variance. By varying the target values of pressure and wall shear stress required by the model, we showed that results are less sensitive to the target pressure and, in addition, proposed a method for estimating the optimal target shear stress.