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Hovering is a miracle of insects that is observed for all sizes of flying insects. Sizing effect in insect hovering on flapping-wing aerodynamics is of interest to both the micro-air-vehicle (MAV) community and also of importance to comparative morphologists. In this study, we present an integrated computational study of such size effects on insect hovering aerodynamics, which is performed using a biology-inspired dynamic flight simulator that integrates the modelling of realistic wing-body morphology, the modelling of flapping-wing and body kinematics and an in-house Navier-Stokes solver. Results of four typical insect hovering flights including a hawkmoth, a honeybee, a fruit fly and a thrips, over a wide range of Reynolds numbers from O(10(4)) to O(10(1)) are presented, which demonstrate the feasibility of the present integrated computational methods in quantitatively modelling and evaluating the unsteady aerodynamics in insect flapping flight. Our results based on realistically modelling of insect hovering therefore offer an integrated understanding of the near-field vortex dynamics, the far-field wake and downwash structures, and their correlation with the force production in terms of sizing and Reynolds number as well as wing kinematics. Our results not only give an integrated interpretation on the similarity and discrepancy of the near- and far-field vortex structures in insect hovering but also demonstrate that our methods can be an effective tool in the MAVs design.

This article presents a wide-ranging review of the simulation-based biological fluid dynamic models that have been developed and used in animal swimming and flying. The prominent feature of biological fluid dynamics is the relatively low Reynolds number, e.g. ranging from 100 to 104 for most insects; and, in general, the highly unsteady motion and the geometric variation of the object result in large-scale vortex flow structure. We start by reviewing literature in the areas of fish swimming and insect flight to address the usefulness and the difficulties of the conventional theoretical models, the experimental physical models, and the computational mechanical models. Then we give a detailed description of the methodology of the simulation-based biological fluid dynamics, with a specific focus on three kinds of modeling methods: (1) morphological modeling methods, (2) kinematic modeling methods, and (3) computational fluid dynamic methods. An extended discussion on the verification and validation problem is also presented. Next, we present an overall review on the most representative simulation-based studies in undulatory swimming and in flapping flight over the past decade. Then two case studies, of the tadpole swimming and the hawkmoth hovering analyses, are presented to demonstrate the context for and the feasibility of using simulation-based biological fluid dynamics to understanding swimming and flying mechanisms. Finally, we conclude with comments on the effectiveness of the simulation-based methods, and also on its constraints.

Micro Air Vehicles (MAVs) have the potential to revolutionize our sensing and information gathering capabilities in environmental monitoring and homeland security areas. Due to the MAVs" small size, flight regime, and modes of operation, significant scientific advancement will be needed to create this revolutionary capability. Aerodynamics, structural dynamics, and flight dynamics of natural flyers intersects with some of the richest problems in MAVs, including massively unsteady three-dimensional separation, transition in boundary layers and shear layers, vortical flows and bluff body flows, unsteady flight environment, aeroelasticity, and nonlinear and adaptive control are just a few examples. A challenge is that the scaling of both fluid dynamics and structural dynamics between smaller natural flyer and practical flying hardware/lab experiment (larger dimension) is fundamentally difficult. In this paper, we offer an overview of the challenges and issues, along with sample results illustrating some of the efforts made from a computational modeling angle.