A large body of evidence implicates fluid dynamic forces in the genesis and progression of atherosclerosis, the leading cause of death in the United States. To understand the mechanism by which hemodynamics influences the disease process, and to identify the specific flow variable(s) responsible for its localization, it is essential to know the distribution of hemodynamic variables in susceptible regions of the vasculature. Vascular flow models have been used more than any other means to gain insight into the details of arterial hemodynamics. The first flow models were not very realistic. Our first attempt, reported at an early Biomechanics Symposium, was probably the most unrealistic of all: a “2-D branch” that was constructed to validate a 2-D computed flow field. Most of the first models were made of cylindrical tubes, and their geometry too only approximated that of real arteries. Much was learned about the fluid dynamics in branches and bends using such models, but measurements in them could be related only generally to the fluid dynamics in living vessels. Accordingly, we began to make flow field measurements in replicas prepared from human arteries. Others challenged their glassblowers and shops to make models more representative of real vessels. These flow-through casts and fabricated models were initially rigid and perfused with Newtonian fluids. Using these more realistic systems, we and others were able to demonstrate relationships between specific hemodynamic variables and localized arterial pathology. The fidelity of flow simulations today exceeds that of only a few years ago. We now perfuse compliant replicas as small as coronary diagonal branches with fluids whose rheology mimics blood. This level of fidelity is harder to justify for the present application than the switch from tubes to flow-through casts. There is no evidence that the disease has kept secrets from the rigid casts that will be exposed in compliant ones. Nonetheless, there is comfort in simulating the real world as faithfully as possible, and one never knows until one tries whether the next increment of reality will yield unexpected new insights.

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