UC Irvine

This thesis presents a modeling, design exploration, and optimization study of novel shape memory alloy (SMA) axial actuators. Shape memory alloys are materials that can generate and recover moderate inelastic strains through temperature modulation and exhibit high actuation energy density compared to other smart material actuators. This characteristic enables SMAs to function as lightweight and compact thermomechanical actuators. The synthesis of SMA actuators for systems with specific requirements on their actuation path (displacement stroke vs. temperature) currently relies on complex and expensive material processing and characterization. First, a geometric approach for synthesizing novel SMA axial actuators, termed as hybrid SMA actuators, whose dimensions and material distribution are modulated to approximate a target actuation path. Through the combination of multiple SMA wire sections in series, the hybrid SMA actuators can exhibit actuation paths not achievable by using single monolithic SMA wires. A reduced-order numerical model for the hybrid SMA actuators that allows for efficient design evaluations is derived and implemented. An approach to incorporate uncertainty in the parameters of the actuators within the design framework is implemented to allow for the determination of robust actuator designs. A machine learning-assisted framework for the surrogate modeling of hybrid SMA actuators is then detailed. This approach allows for the prediction of their actuation path without the use of structural simulations leveraging numerical implementations of constitutive models, allowing for simplified and computationally efficient modeling and circumventing convergence issues. A surrogate model consisting of an ensemble of binary decision trees is trained using data obtained via a design of experiments performed using structural simulations. A validation test using 5000 design samples for hybrid SMA actuators with two sections demonstrates R2 values of 0.99983 and 0.99979 for the actuation displacement during heating and cooling, respectively. The evaluation time for the validation samples using the trained surrogate model is less than 8 minutes, while the evaluation time using structural simulations is 59 minutes. A surrogate-based optimization approach is then demonstrated through the synthesis of hybrid SMA actuators capable of exhibiting prescribed target actuation paths. Lastly, a modeling, experimental prototyping, and computational design exploration study of a morphing wing enabled by a tensegrity mechanism and actuated by shape memory alloy (SMA) wires is investigated. The studied wing design circumvents conventional control surfaces such as hinged flaps and ailerons through the implementation of a smooth wing shape that twists to modulate its flight characteristics. The continuous and smooth wing surface lessens aerodynamic drag to enhance aerodynamic efficiency. The morphing capability of the wing is enabled through an integrated lightweight tensegrity mechanism, which provides twisting motion through elongation/contraction of the SMA wires. Befitting for the actuation of the tensegrity mechanism due to their rod form, SMA wire actuators are incorporated to reconfigure the wing shape through thermally driven material actuation. A finite element model that integrates the wing, tensegrity mechanism, and SMA wire actuators is created to assess the stresses, maximum attainable twist angle, and structural mass of the wing. A design of experiment study is performed to evaluate the influence of the topological and geometrical design parameters on performance responses such as twist angle and mass. The most favorable design demonstrates a maximum twist angle of 15.85◦ and a mass of 2.02 kg without exceeding the material stress limits. The SMA-enabled torsional morphing capability is also demonstrated experimentally through a tensegrity twisting wing prototype equipped with commercially available SMA wire actuators.