Abstract
A predictive model of thermal actuator behavior has been developed and validated that can be used as a design tool to customize the performance of an actuator to a specific application. Modeling thermal actuator behavior requires the use of two sequentially or directly coupled models, the first to predict the temperature increase of the actuator due to the applied voltage and the second to model the mechanical response of the structure due to the increase in temperature. These models have been developed using ANSYS for both thermal response and structural response. Consolidation of FEA (finite element analysis) results has been carried out using an ANN (artificial neural network) in MATLAB. It is seen that an ANN can be successfully employed to interpolate and predict FEA results, thus avoiding necessity of running FEA code for every new case. Furtheroptimization of geometry for maximum actuation length has been carried out using a GA (genetic algorithm) in MATLAB. The results of the GA were verified against the ANN and FEA results.
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