Design optimization and wind tunnel investigation of a flapping system based on the flapping wing trajectories of a beetle's hindwings

Structural response and mechanical properties of the hind wing of the beetle Protaetia brevitarsis

The folding/unfolding mechanism and collision recovery effect of the beetle's hind wings can provide biomimetic inspiration for the optimization of wing deplorability and the investigation of collision prevention recovery mechanism of new amphibious morphing vehicle. In this study, a method is described to investigate the structural response and mechanical properties of the hind wings of the beetle Protetia brevitarsis under natural conditions. The specially processed test samples were conducted to tensile testing, which facilitates the evaluation of the mechanical properties of specific areas of the hind wing. The micro geometric morphological characteristics of the cross-section of the specimen after tensile fracture were observed by scanning electron microscopy. The three-dimensional morphology of the ventral and dorsal sides of the hind wing was characterized using three-dimensional scanning and reverse modeling methods. The finite element model of the hind wing is developed to investigate the structural deformation and modal response characteristics of its flapping. The uniformly distributed load on the hind wing surface is derived from the lift characteristics obtained from the computational fluid dynamics simulation of flapping wing motion. RESEARCH HIGHLIGHTS: Scanning electron microscope is used to observe the cross-sectional characteristics of the veins and membranes. The material properties of the wing membranes and veins of the hind wings were measured using the tensile testing system. The three-dimensional morphology of the hind wing was characterized using 3D scanning and reverse modeling methods. The finite element model of the hind wing is developed to investigate the structural deformation and modal response characteristics of its flapping.

Research on Deployable Wings for MAVs Bioinspired by the Hind Wings of the Beetle Protaetia brevitarsis

Deployable hind wings of beetles led to a bio-inspired idea to design deployable micro aerial vehicles (MAVs) to meet the requirement of miniaturization. In this paper, a bionic deployable wing (BD-W) model is designed based on the folding mechanism and elliptical wing vein structure of the Protaetia brevitarsis hindwing, and its structural static and aerodynamic characteristics are analyzed by using ANSYS Workbench. Finally, the 3D-printed bionic deployable wing was tested in a wind tunnel and compared with simulation experiments to explore the effects of different incoming velocity, flapping frequency, and angle of attack on its aerodynamic characteristics, which resulted in the optimal combination of the tested parameters, among which, the incoming velocity is 3 m/s, the flapping frequency is 10 Hz, the angle of attack is 15°, and the lift-to-drag ratio of this parameter combination is 4.91. The results provide a theoretical basis and technical reference for the further development of bionic flapping wing for MAV applications.

Bio-Model Selection, Processing and Results for Bio-Inspired Truck Streamlining

We introduce a method for the selection and processing of a biological model to derive an outline that provides morphometric information for a novel aerodynamic truck design. Because of the dynamic similarities, our new truck design will be inspired by biological shapes with a known high level of streamlining and low drag for operation near the seabed, i.e., the head of a trout, but other model organisms will also be used later. Demersal fish are chosen because they live near the bottom of rivers or the sea. Complementary to many biomimetic studies so far, we plan to concentrate on reshaping the outline of the fish's head and extend it to a 3D design for the tractor that, at the same time, fits within EU regulations and maintains the truck's normal use and stability. We intend to explore this biological model selection and formulization involving the following elements: (i) the reason for selecting fish as a biological model for a streamlined truck design; (ii) The choice of a fish model via a functional similarity method; (iii) biological shape formulization based on the morphometric information of models in (ii) outline pick-up, a reshaping step and a subsequent design process; (iv) modify the biomimetic designs and test utilizing CFD; (v) further discussion, outputs and results from the bio-inspired design process.

Numerical Simulations of the Effect of the Asymmetrical Bending of the Hindwings of a Hovering C. buqueti Bamboo Weevil with Respect to the Aerodynamic Characteristics

The airfoil structure and folding pattern of the hindwings of a beetle provide new transformation paths for improvements in the aerodynamic performance and structural optimization of flapping-wing flying robots. However, the explanation for the aerodynamic mechanism of the asymmetrical bending of a real beetle's hindwings under aerodynamic loads originating from the ventral and dorsal sides is unclear. To address this gap in our understanding, a computational investigation into the aerodynamic characteristics of the flight ability of C. buqueti and the large folding ratio of their hindwings when hovering is carried out in this article. A three-dimensional (3D) pressure-based SST k-ω turbulence model with a biomimetic structure was used for the detailed analysis, and a refined polyhedral mesh was used for the simulations. The results show that the fluid around the hindwings forms a vortex ring consisting of a leading-edge vortex (LEV), wing-tip vortex (TV) and trailing-edge vortex (TEV). Approximately 61% of the total lift is generated during the downstroke, which may be closely related to the asymmetric bending of the hindwings when they are subjected to pressure load.

Micro-structures, nanomechanical properties and flight performance of three beetles with different folding ratios

When beetles are not in flight, their hind wings are folded and hidden under the elytra to reduce their size. This provided inspiration for the design of flapping-wing micro aerial vehicles (FWMAVs). In this paper, microstructures and nanomechanical properties of three beetle species with different wing folding ratios living in different environments were investigated. Factors affecting their flight performance, that is, wind speed, folding ratio, aspect ratio, and flapping frequency, were examined using a wind tunnel. It was found that the wing folding ratio correlated with the lift force of the beetles. Wind speed, folding ratio, aspect ratio, and flapping frequency had a combined effect on the flight performance of the beetles. The results will be helpful to design new deployable FWMAVs.

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Purpose

A large portion of the adult population is thought to suffer from obstructive sleep apnoea syndrome (OSAS), a sleep-related breathing disorder associated with increased morbidity and mortality. International guidelines include the polysomnography and the cardiorespiratory monitoring (CRM) as diagnostic tools for OSAS, but they are unfit for a large-scale screening, given their invasiveness, high cost and lengthy process of scoring. Current screening methods are based on self-reported questionnaires that suffer from lack of objectivity. On the contrary, commercial smartbands are wearable devices capable of collecting accelerometric and photoplethysmographic data in a user-friendly and objective way. We questioned whether machine-learning (ML) classifiers trained on data collected through these wearable devices would help predict OSAS severity.

Patients and Methods

Each of the patients (n = 78, mean age ± SD: 57.2 ± 12.9 years; 30 females) underwent CRM and concurrently wore a commercial wrist smartband. CRM's traces were scored, and OSAS severity was reported as apnoea hypopnoea index (AHI). We trained three pairs of classifiers to make the following prediction: AHI <5 vs AHI ≥5, AHI <15 vs AHI ≥15, and AHI <30 vs AHI ≥30.

Results

According to the Matthews correlation coefficient (MCC), the proposed algorithms reached an overall good correlation with the ground truth (CRM) for AHI <5 vs AHI ≥5 (MCC: 0.4) and AHI <30 vs AHI ≥30 (MCC: 0.3) classifications. AHI <5 vs AHI ≥5 and AHI <30 vs AHI ≥30 classifiers' sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and diagnostic odds ratio (DOR) are comparable with the STOP-Bang questionnaire, an established OSAS screening tool.

Conclusion

Machine learning algorithms showed an overall good performance. Unlike questionnaires, these are based on objectively collected data. Furthermore, these commercial devices are widely distributed in the general population. The aforementioned advantages of machine-learning algorithms applied to smartbands' data over questionnaires lead to the conclusion that they could serve a population-scale screening for OSAS.