Design and Optimization of Nature-inspired Piezoelectric Generators: Fractal design
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Examensarbete för masterexamen
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Sammanfattning
A piezoelectric energy harvester or generator is a device with no need for maintenance
or external energy being provided since it utilizes vibrations generated by industrial machines
to produce an electrical voltage output through a piezoelectric material. The
piezoelectric energy harvester can be used to power small Internet of Things (IoT) components,
without need for an external battery or connecting cables. The optimization
solutions are meant to develop a proof-of-concept for the chosen fractal tree energy harvester
design. The aim of this is to investigate if that design can produce a sufficiently
high electric output (voltage and power) and a high enough stress in longitudinal branch
direction as Frequency Response Functions (FRFs).
The chosen material for the overall structure is structural steel, whereas the piezoelectric
material for energy harvesting is Polyvinylidene Fluoride (PVDF). The fractal tree
design is optimized by running a MATLAB code called SAMO which carries out first
a Sensitivity Analysis (SA) followed by a Multi-objective Optimization (MO) using an
Elitist Genetic Algorithm (GA). The coupling between MATLAB and the COMSOL
Multyphysics fractal tree model is ensured by the COMSOL feature called LiveLink for
MATLAB. The optimal design solutions form a set which are referred to as a Pareto
set, and they are associated to two minimized objective functions and multiple design
variables. In the first phase of the optimization setup (Phase 1), for the minimization
of two given objective functions per optimization process, multiple fractal tree geometry
design variables are tested. For that first phase, at each optimization procedure iteration,
the overall geometry of the fractal design is updated according to the design variables
values, and if the geometry is acceptable, a static analysis is computed. Then, if the maximal
static load is permissible, a modal analysis (eigenfrequency study) and a frequency
domain analysis are also carried out. For the second optimization phase (Phase 2) of
the project, two general designs of the fractal tree are chosen, one with and one without
proof masses (PM) positioned at the end of the top branches. The piezoelectric material
placement is optimized by considering an area coverage variation for each branch of the
fractal tree design in order to favour the least negative voltage output generation and
maximal branch longitudinal stress in the frequency domain.
In the end, the best optimized fractal tree design in terms of calculated longitudinal
stress and voltage output will be fabricated after completion of the project. Simulated
FRFs responses will then be validated in the MC2 Laboratory by being compared with
experimental FRFs obtained by Laser Doppler Vibrometer tests.
Beskrivning
Ämne/nyckelord
Energy harvester, piezoelectric, bandwidth, fractal design, Genetic Algorithm, Pareto set, objective function, Frequency Response, Function