Optimization of Energy Harvesting Potentials of Flow Induced Vibrations For Micro Power Generation

Abstract

In this study introduction of ns on a blu trapezoid is studied and an optimized tail length is sought to ensure that the maximum possible energy is extracted from the ambient environment.Existing research in regards to blu body vortex shedding pertains to the simple trapezoid body.The addition of ns is studied in previous research however a formal optimization study with respect to vibration amplitudes utilizing some sort of optimization technique to nd a relative optimum is lacking.This study builds on the previous work in numerically simulating trapezoid bodies oscillating in both vortex induced and galloping regimes and aims to nd an optimum n length using the Genetic Algorithm as the active optimization technique. The structure is modeled as elastically mounted supported by linear spring and damper. Incompressible Navier-Stokes equations are the governing equations for the ow. Geometry and mesh are created in Gambit.The mass ratio (m ) is set as 15.1 while the damping ratio ( ) is 0.00295 giving mass-damping ratio (m ) of 0.0000695.The ow eld is simulated using Spalart-Allmaras turbulence model. The solution procedure modelling the 1 Degree of Freedom( DOF ) vibration equation is programmed by a user de ne function (UDF) dynamically hooked to ANSYS Fluent.The Genetic Algorithm is implemented via embedding the complete simulation process within a tness function such that the output of the function is an eventual root mean squared amplitude value of the vibration experienced by the blu body.The complete tness function is programmed within the MATLAB environment.The tness function is programmed such that its input corresponds to the tail length of the trapezoid body which the tness function subsequently uses to produce a trapezoid with the speci ed tail length.The tness function automatically generates a mesh and solution using Gambit and Fluent by using meshing and solution schemes established previously.The tness function then extracts the vibration data from the completed simulation which serves as its output.The Genetic Algorithm is utilized using a Population Size of 5 with a total of 25 Generations to achieve an optimum.The Genetic Algorithm was run using a linear feasible creation function, Tournament selection function and a Heuristic crossover function with an adaptive feasible function for mutations across all generation of individuals.The Algorithm was run for an lower and upper bound of 0.1D and 2D tail length where D is cross sectional length of the trapezoid to ensure that some local maxima was found and to limit the amount of computations from becoming unbounded.