Precision flow pumps have been widely studied over the last three
decades. They have been applied as essential components in thermal
management solutions for cooling electronic devices offering better
performance with low noise and low power consumption. In this work,
a novel configuration of a miniature piezoelectrically actuated flow
pump with the purpose of cooling a LED set inside a head light
system for medical applications has been studied and it will be
presented. The complete cycle of pump development was conducted. In
the design step, the ANSYS finite element analysis software
has been applied to simulate and study the fluid-structure
interaction inside the pump, as well as the bimorph piezoelectric
actuator behavior. In addition, an optimization process was carried
out through Altair Hyperstudy software to find a set of
parameter values that maximizes the pump performance measured in
terms of flow rate. The prototype manufacturing was guided based on
computational simulations. Flow characterization experimental tests
were conducted, generating data that allows us to analyze the
influence of frequency and amplitude parameters in the pump
performance. Comparisons between numerical and experimental results
were also made.
Electrical Impedance Tomography (EIT) seeks to recover the impedance distribution within a body using boundary data. More specifically, given the measured potentials, the model of the body - an elliptic partial differential equation - and the boundary conditions, this technique solves a non-linear inverse problem for the unknown impedance. In this work, an algorithm called Topology Optimization Method (TOM) is applied to EIT and compared to the Gauss-Newton Method (GNM). The Topology Optimization has solved some non-linear inverse problems and some of its procedures were not investigated for EIT, for instance, the use of Sequential Linear Programming. Assuming a pure resistive medium, the static resistivity distribution of a phantom was estimated using a 2-D finite element model. While the first method (GNM) essentially solves several algebraic systems, the second (TOM) solves several linear programming problems. Results using experimental data are shown and the quality of the images obtained, time and memory used are compared for both algorithms. We intend to use these methods, in future works, for the visualization of a human lung subjected to mechanical ventilation.
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