Session: 11-02-03: CFD Applications for Optimization and Control II

Paper Number: 165155

Effect of Flight Speed on Aerodynamic Performance, Vortex Structures, and Odor Intensity in Blue Bottle Flies 

Odor-guided navigation is a crucial survival mechanism for many flying insects, enabling them to locate essential resources such as food, identify potential mates, and evade predators. Among these insects, flies exhibit a remarkable reliance on their olfactory system to detect and process airborne odor cues, which are vital for their ecological interactions. The flight kinematics of flies, including their speed, maneuverability, and wingbeat patterns, play a significant role in shaping the airflow around their bodies and directly influence the transport of odor molecules to their antennae. Despite extensive research on the aerodynamics of insect flight and the mechanisms of odor tracking, the interplay between flight speed, aerodynamic performance, and olfactory navigation remains poorly understood. Specifically, how variations in flight speed affect the aerodynamic efficiency, wake dynamics, and the resulting odor concentration around the antennae has not been thoroughly investigated.

This study aims to bridge this gap by exploring the relationship between flight speed and olfactory navigation in blue bottle flies (Calliphora vomitoria), a model organism known for its robust flight capabilities and acute olfactory sensitivity. By systematically varying flight speeds and analyzing the corresponding aerodynamic performance, wake structures, and odor intensity patterns around the antennae, we seek to uncover how these factors collectively influence the flies' ability to detect and respond to odor cues. The findings from this research will not only enhance our understanding of the sensory ecology of flying insects but also provide insights into the biomechanical and aerodynamic principles that underlie odor-guided navigation in complex environments. This knowledge could have broader implications for the development of bio-inspired technologies, such as autonomous drones equipped with advanced olfactory sensors for applications in search-and-rescue operations or environmental monitoring. We use high-fidelity computational fluid dynamics (CFD) simulations using an in-house immersed-boundary-method-based solver to analyze forward flight at different speeds. The wing kinematics and shape are reconstructed from high-speed video recordings, providing biologically accurate motion inputs for the simulations. Flight speeds are varied from 1.25 m/s to 0.58 m/s, allowing us to evaluate the impact of speed on key aerodynamic performance, vortex dynamics, and odor intensity.

Our results show that flight speed plays an important role in the insect’s olfactory navigation in the complex environment. A reduction in flight speed leads to an increase in odor concentration around the antennae, suggesting that slower flight increases olfactory sensitivity. However, decreasing flight speed also reduces the oscillation amplitude of odor signals, potentially affecting the efficiency of odor tracking. Aerodynamically, lower flight speeds result in weaker leading-edge vortex (LEV) structures and altered wake patterns, which may influence both lift production and stability. Additionally, changes in wingbeat kinematics at different speeds impact the way odor plumes are advected toward the antennae, highlighting the complex coupling between flight dynamics and olfactory perception.

These findings provide new insights into how insects control their flight speed to optimize odor field, showing a potential trade-off between olfactory sensitivity and signal consistency. This research not only advances our understanding of insect flight and olfactory navigation but also offers inspiration for the design of bio-inspired robotic systems for autonomous odor guided navigation. By mimicking the flight-speed-dependent odor acquisition strategies observed in flies, future robotic platforms could achieve augmented environmental sensing and tracking capabilities in dynamic flow environments.

Presenting Author: Naeem Haider Case Western Reserve University

Presenting Author Biography: Naeem Haider is a 2nd year PhD student in the Department of Mechanical and Aerospace Engineering at Case Western Reserve University.

Authors:

Naeem Haider Case Western Reserve University
Zhipeng Lou Case Western Reserve University
Bo Cheng Pennsylvania State University
Chengyu Li Case Western Reserve University