Optimizing the Variability in the Deformation of a Biomimetic Pinna

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Virginia Tech


Bats are noted for having extremely powerful biosonar systems that enable them to move through and hunt through the thick foliage. They have a single emitter (mouth or nose) and two receivers in their biosonar system (ears). Some bat species, such as those belonging to the group's rhinophid and hipposiderid, feature intricate pinna motion patterns. These pinnae are divided into two groups: stiff movements and non-rigid motions. To understand how pinna sense worked has been studied in this thesis. The rigid pinna movements displayed a significantly different rotation, with revolutions axes spanning 180° in horizontal and curvature, according to axis-angle representations. The classification of landmarks on the pinna surface has explained two types of non-stiffed pinna movements. Additionally, a bio-inspired pinna has been used to explore the acoustic impact of the stiff pinna movements. All the given results showed precise accuracy in the motion of variance bats pinnae. The research initiative was initiated with a comprehensive exploration of various design concepts, primarily focused on elucidating the intricate interplay between actuator geometry and the resultant deformation of the pinna. Employing a structured design code facilitated the generation of an array of configurations, each subject to stringent conditions and parameter settings necessitating subsequent validation. After this design exploration, a tri-tiered hierarchy of forces, encompassing nominal, intermediate, and elevated magnitudes, was applied to instigate a systematic optimization process aimed at determining the most favorable deformation pattern. Computational simulations leveraging Finite Element Analysis (FEA) were conducted, accompanied by a rigorous material characterization procedure, to effectively quantify the extent of deformation across the array of configurations. A consequential phase of the investigation involved the implementation of Principal Component Analysis (PCA) to differentiate the inherent variability within the different deformation arrangements, shedding light on their relative structural and morphological distinctions. The culmination of the study encompassed the utilization of the Genetic Algorithm (GA), a sophisticated optimization technique, to facilitate the fine-tuning of deformation patterns in pursuit of the overarching goal: the deliberate induction of substantial and diverse variations in pinna morphology. In summary, the research trajectory progressed sequentially through design conceptualization, force-induced optimization, computational simulations incorporating FEA and material characterization, Variability analysis via PCA, and culminated in the deployment of the GA to achieve the prime objective of inducing pronounced variability in pinna configuration. The work was done as following, starting with design concepts, the main benefit of this is to understand how the geometry of actuator affects the pinna deformation. Using the design code to present several configurations that must have conditions and parameters to be validated. After that applying 3 different forces (zero, medium, and high) to get the optimization for pattern. Applying the FEA simulations with help of material characterization to display the displacement of the arrangements. Finally doing the Variability analysis by using the principal component analysis. Then concluding the work by using the Genetic algorithm for optimizations to reach the main goal which is large variability in the pinna shape.



Pinna, biomimetic, and biosonar