Honors College Theses

Publication Date



Mechanical Engineering (B.S.)

Document Type and Release Option

Thesis (open access)

Faculty Mentor

David Calamas



A computational fluid dynamics model was utilized to study the effect of flow direction on local flow behavior in biologically-inspired microscale flow networks. Biologically-inspired flow networks have been found to offer numerous advantages when compared with parallel flow networks, particularly in thermal management applications. Flow behavior was examined for a range of bifurcation angles and laminar inlet Reynolds numbers. The computational model was validated with existing theoretical solutions and verified for grid independence. As the bifurcation angle increased, the pressure drop, and thus pumping power, across the biologically-inspired flow networks increased. In addition, as the inlet Reynolds number increased, so did the pressure drop. Local spikes in pressure, which impacted the total pressure drop, were observed immediately following bifurcations. The magnitude of these changes in pressure was found to be dependent on bifurcation angle.