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1. The Dragonfly Wing Project

   1.Zheng, Hao ; Akbarzadeh, Masoud ( January 2022 , Architectural design)

   Del Campo, Matias ; Leach, Neil (Ed.)

   Special Issue: Machine Hallucinations: Architecture and Artificial Intelligence Nature has always been the master of design skills to which humans only aspire, but new approaches bring that aspiration closer to our reach than ever before. Through 4.5 billion years of iterations, nature has shown us its extraordinary craftsmanship, breeding a variety of species whose body structures have gradually evolved to adapt to natural phenomena and make full use of their unique characteristics. The dragonfly wing, among body structures, is an extreme example of efficient use of materials and minimal weight while remaining strong enough to withstand the tremendous forces of flight. It has long been the object of scientific research examining its structural advantages to apply its principles to fabricated designs.1 We can imitate its form and create duplicates, but thoroughly understanding the dragonfly wing’s mechanism, behavior, and design logic is no trivial task. 

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2. Lightweight Structures and the Geometric Equilibrium in Dragonfly Wings

   Zheng, Hao ; Hablicsek, Marton ; Akbarzadeh, Masoud ( October 2021 , International Association of Shell and Spatial Structures)

   This research investigates the use of graphic statics in analyzing the structural geometry of a natural phenomenon to understand their performance and their relevant design parameters. Nature has always been inspiring for designers, engineers, and scientists. Structural systems in nature are constantly evolving to optimize themselves with their boundary conditions and the applied loads. Such phenomena follow certain design rules that are quite challenging for humans to formulate or even comprehend. A dragonfly wing is an instance of a high-performance, lightweight structure that has intrigued many researchers to investigate its geometry and its performance as one of the most light-weight structures designed by nature [1]. There are extensive geometrical and analytical studies on the pattern of the wing, but the driving design logic is not clear. The geometry of the internal members of the dragonfly wings mainly consists of convex cells which may, in turn, represent a compression-only network on a 2D plane. However, this phenomenon has never been geometrically analyzed from this perspective to confirm this hypothesis. In this research, we use the methods of 2D graphic statics to construct the force diagram from the given structural geometry of the wing. We use algebraic and geometric graphic statics to unfold the topological and geometric properties of the form and force diagrams such as the degrees of indeterminacies of the network [2]. We then reconstruct the compression-only network of the wing for more than 300 cases for the same boundary conditions and the edge lengths of the independent edges of the network. Comparing the magnitude of the internal forces of the reconstructed network with the actual structure of the wing using the edge length of the force diagram will shed light on the performance of the structure. Multiple analytical studies will be provided to compare the results in both synthetic and natural networks and drive solid conclusions. The success in predicting the internal force flow in the natural structural pattern using graphic statics will expand the use of these powerful methods in reproducing the exact geometry of the natural structural system for use in many engineering and scientific problems. It will also ultimately help us understand the design parameters and boundary conditions for which nature produces its masterpieces. 

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3. An Adaptable Flying Fish Robotic Model for Aero- and Hydrodynamic Experimentation

   https://doi.org/10.1093/icb/icac101  

   Saro-Cortes, Valeria ; Cui, Yuhe ; Dufficy, Tierney ; Boctor, Arsanious ; Flammang, Brooke E ; Wissa, Aimy W ( June 2022 , Integrative and Comparative Biology)

   Abstract Flying fishes (family Exocoetidae) are known for achieving multi-modal locomotion through air and water. Previous work on understanding this animal’s aerodynamic and hydrodynamic nature has been based on observations, numerical simulations, or experiments on preserved dead fish, and has focused primarily on flying pectoral fins. The first half of this paper details the design and validation of a modular flying fish inspired robotic model organism (RMO). The second half delves into a parametric aerodynamic study of flying fish pelvic fins, which to date have not been studied in-depth. Using wind tunnel experiments at a Reynolds number of 30,000, we investigated the effect of the pelvic fin geometric parameters on aerodynamic efficiency and longitudinal stability. The pelvic fin parameters investigated in this study include the pelvic fin pitch angle and its location along the body. Results show that the aerodynamic efficiency is maximized for pelvic fins located directly behind the pectoral fins and is higher for more positive pitch angles. In contrast, pitching stability is neither achievable for positive pitching angles nor pelvic fins located directly below the pectoral fin. Thus, there is a clear a trade-off between stability and lift generation, and an optimal pelvic fin configuration depends on the flying fish locomotion stage, be it gliding, taxiing, or taking off. The results garnered from the RMO experiments are insightful for understanding the physics principles governing flying fish locomotion and designing flying fish inspired aerial-aquatic vehicles. 

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4. Model Predictive Control-Based Path-Following for Tail-Actuated Robotic Fish

   https://doi.org/10.1115/1.4043152  

   Castaño, Maria L. ; Tan, Xiaobo ( July 2019 , Journal of Dynamic Systems, Measurement, and Control)

   There has been an increasing interest in the use of autonomous underwater robots to monitor freshwater and marine environments. In particular, robots that propel and maneuver themselves like fish, often known as robotic fish, have emerged as mobile sensing platforms for aquatic environments. Highly nonlinear and often under-actuated dynamics of robotic fish present significant challenges in control of these robots. In this work, we propose a nonlinear model predictive control (NMPC) approach to path-following of a tail-actuated robotic fish that accommodates the nonlinear dynamics and actuation constraints while minimizing the control effort. Considering the cyclic nature of tail actuation, the control design is based on an averaged dynamic model, where the hydrodynamic force generated by tail beating is captured using Lighthill's large-amplitude elongated-body theory. A computationally efficient approach is developed to identify the model parameters based on the measured swimming and turning data for the robot. With the tail beat frequency fixed, the bias and amplitude of the tail oscillation are treated as physical variables to be manipulated, which are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the sector-shaped constraints of the control inputs while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results support the efficacy of the proposed approach. In particular, the advantages of the control projection method are shown via comparison with alternative approaches. 

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5. Sub-GHz In-Body to Out-of-Body Communication Channel Modeling for Ruminant Animals for Smart Animal Agriculture

   https://doi.org/10.1109/TBME.2022.3213262  

   Datta, Arunashish ; Kaur, Upinder ; Malacco, Victor ; Nath, Mayukh ; Chatterjee, Baibhab ; Donkin, Shawn S. ; Voyles, Richard M. ; Sen, Shreyas ( October 2022 , IEEE Transactions on Biomedical Engineering)

   Sensors in and around the environment becoming ubiquitous has ushered in the concept of smart animal agriculture which has the potential to greatly improve animal health and productivity using the concepts of remote health monitoring which is a necessity in times when there is a great demand for animal products. The data from in and around animals gathered from sensors dwelling in animal agriculture settings have made farms a part of the Internet of Things space. This has led to active research in developing efficient communication methodologies for farm networks. This study focuses on the first hop of any such farm network where the data from inside the body of the animals is to be communicated to a node dwelling outside the body of the animal. In this paper, we use novel experimental methods to calculate the channel loss of signal at sub-GHz frequencies of 100 - 900 MHz to characterize the in-body to out-of-body communication channel in large animals. A first-of-its-kind 3D bovine modeling is done with computer vision techniques for detailed morphological features of the animal body is used to perform Finite Element Method based Electromagnetic simulations. The results of the simulations are experimentally validated to come up with a complete channel modeling methodology for in-body to out-of-body animal body communication. The experimentally validated 3D bovine model is made available publicly on https://github.com/SparcLab/Bovine-FEM-Model.git GitHub. The results illustrate that an in-body to out-of-body communication channel is realizable from the rumen to the collar of ruminants with $\leq {90}~{\rm dB}$ path loss at sub-GHz frequencies ( $100-900~MHz$ ) making communication feasible. The developed methodology has been illustrated for ruminants but can also be used for other related in-body to out-of-body studies. Using the developed channel modeling technique, an efficient communication architecture can be formed for in-body to out-of-body communication in animals which paves the way for the design and development of future smart animal agriculture systems. 

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