In the vast and resource-rich oceans, which serve as critical channels for material transport and strategic military spaces, the development of advanced observation and exploration technologies is paramount. As a researcher in marine engineering, I have focused on creating underwater vehicles that emulate the efficiency, agility, and low energy consumption of marine life. This paper presents my work on designing a movable-wing underwater bionic robot, inspired by the manta ray. The goal is to enhance hydrodynamic performance, particularly in reducing drag and improving stability, which are essential for prolonged and efficient underwater operations. The bionic robot leverages a unique propulsion mechanism based on oscillating pectoral fins, diverging from conventional propeller-driven systems that often suffer from inefficiencies, noise, and environmental disturbance. Through detailed structural design, fluid dynamics analysis using STAR CCM+ software, and optimization, this bionic robot aims to achieve superior maneuverability and energy efficiency, contributing to the broader field of bio-inspired robotics.
The design philosophy of this bionic robot centers on biomimicry, specifically imitating the manta ray’s morphology and locomotion. Marine creatures like manta rays have evolved over millions of years to master MPF (median and/or paired fin) propulsion, which offers high stability, excellent机动性, and stealth. In my approach, I sought to replicate these traits in a mechanical system, ensuring the bionic robot can perform tasks such as linear motion, turning, and depth adjustment with minimal energy expenditure. The bionic robot’s structure is simplified for practicality, omitting intricate biological details but retaining key features that influence hydrodynamics. The overall shape is diamond-like, with broad, thin pectoral fins resembling those of a manta ray, measuring 575 mm in length, 850 mm in maximum width, and 149 mm in thickness. This design prioritizes fluid flow management, aiming to reduce resistance and enhance lift during operation.
Internally, the bionic robot comprises several key subsystems: an equipment compartment, a pectoral fin oscillating mechanism, and a tail fin adjustment system. The equipment compartment is divided into two sections: a gravity-adjustment module for controlling the center of mass and a waterproof circuit chamber for housing electronic components. By sliding a gravity block within the module, I can alter the robot’s pitch moment, enabling depth control during ascent and descent motions. This mechanism is crucial for maintaining stability in varying underwater conditions. The pectoral fin oscillating mechanism uses a multi-joint mechanical linkage driven by a single servo motor per fin, with two servos in total for both left and right fins. This setup allows the bionic robot to mimic the flapping motion of a manta ray’s pectoral fins, generating thrust through controlled oscillations. The tail fin, actuated by another servo, adjusts its deflection angle to modify depth once forward speed is achieved. For instance, an upward tail fin deflection produces a lift force that causes the bionic robot to rise, while a downward deflection induces diving. These integrated systems ensure the bionic robot can navigate complex underwater environments effectively.
To analyze the hydrodynamic performance of this bionic robot, I employed computational fluid dynamics (CFD) based on fundamental conservation laws. Fluid flow is governed by the principles of mass conservation and momentum conservation, which are expressed mathematically. The mass conservation equation for a three-dimensional transient flow is:
$$ \frac{\partial \rho}{\partial t} + \frac{\partial (\rho u)}{\partial x} + \frac{\partial (\rho v)}{\partial y} + \frac{\partial (\rho w)}{\partial z} = 0 $$
where \( \rho \) is the fluid density, and \( u \), \( v \), and \( w \) are the velocity components in the \( x \), \( y \), and \( z \) directions, respectively. This equation ensures that the mass inflow equals outflow within a fluid element. The momentum conservation equation, derived from Newton’s second law and the Navier-Stokes equations, is:
$$ \rho \frac{D \mathbf{V}}{D t} = -\nabla p + \rho \mathbf{g} + \mu \nabla^2 \mathbf{V} $$
where \( \mathbf{V} \) is the velocity vector, \( p \) is the pressure, \( \mathbf{g} \) is the gravitational acceleration, \( \mu \) is the dynamic viscosity, and \( \nabla^2 \) is the Laplace operator. These equations form the basis for simulating the bionic robot’s interaction with water, allowing me to predict forces such as drag and lift under various conditions.
For the CFD simulations, I used STAR CCM+ to model the bionic robot in a controlled fluid domain. The domain was a rectangular volume measuring 1500 mm in length, 400 mm in width, and 900 mm in height, with the bionic robot positioned appropriately. The inlet was set as a velocity inlet located 5 characteristic lengths (L) ahead of the robot, while the outlet and sides were pressure outlets at 10 L and 5 L distances, respectively. The bottom was a wall, and the top was a free surface, both 5 L away. I employed a trimmed cell meshing approach with prism layers near the bionic robot’s surface to capture boundary layer effects, resulting in approximately 1.45 million nodes and 1.32 million volume cells, ensuring simulation accuracy. The turbulence model combined Reynolds-Averaged Navier-Stokes (RANS) equations with a Detached Eddy Simulation (DES) method, which is effective for capturing complex flow details around oscillating structures. To validate the CFD methodology, I simulated a standard NACA0018 airfoil and compared the drag coefficient with published data, as shown in Table 1. The close agreement, with a relative error of only 5.91%, confirms the reliability of my approach for analyzing the bionic robot.
| Research Method | Drag Coefficient |
|---|---|
| Theoretical Calculation | 0.009013 |
| My CFD Calculation | 0.008480 |
| Relative Error | 5.91% |
Following validation, I conducted simulations to evaluate the bionic robot’s performance at different speeds and attack angles. The results for drag and lift forces are summarized in Tables 2 and 3, respectively. As velocity increased from 1 m/s to 3 m/s, the total drag on the bionic robot rose from 0.19087 N to 1.28040 N, with both shear drag and pressure drag contributing significantly. This trend aligns with fluid dynamics theory, where drag force is proportional to the square of velocity. The lift force also increased quadratically, from 0.68302 N at 1 m/s to 5.74232 N at 3 m/s, described by the lift equation:
$$ L = \frac{1}{2} \rho v^2 S C_L $$
where \( S \) is the reference area and \( C_L \) is the lift coefficient. The high lift-to-drag ratio observed indicates that the bionic robot can achieve substantial vertical force with relatively low resistance, enhancing its energy efficiency. This is a key advantage of the biomimetic design, as it allows the bionic robot to maintain stable motion while minimizing power consumption.
| Velocity (m/s) | Shear Drag Average (N) | Pressure Drag Average (N) | Total Drag Average (N) |
|---|---|---|---|
| 1.0 | 0.08128 | 0.10959 | 0.19087 |
| 1.5 | 0.16563 | 0.21649 | 0.38213 |
| 2.0 | 0.27631 | 0.34759 | 0.62390 |
| 2.5 | 0.41208 | 0.50432 | 0.93184 |
| 3.0 | 0.57294 | 0.70799 | 1.28040 |
| Velocity (m/s) | Lift Force (N) |
|---|---|
| 1.0 | 0.68302 |
| 1.5 | 1.48997 |
| 2.0 | 2.63282 |
| 2.5 | 4.07682 |
| 3.0 | 5.74232 |
Pressure distribution analysis revealed that the bionic robot’s leading edge experienced the highest pressure, while other regions showed lower pressure or negative pressure due to flow acceleration, consistent with Bernoulli’s principle. As speed increased, the negative pressure zones slightly diminished, but the overall pattern remained stable. This pressure profile underscores the importance of reinforcing the front structure to withstand hydrodynamic loads. Additionally, I investigated the effect of attack angles on the bionic robot’s performance at a constant speed of 2.5 m/s. As the angle increased from 0° to 20°, the total drag escalated, averaging 1.033465 N at angles up to 10° and 1.745072 N at higher angles. The pressure云图 (not shown here due to format constraints) indicated that larger angles increased the contact area with incoming flow, leading to uneven pressure distribution and higher resistance. Therefore, to optimize stability and reduce drag, the bionic robot should operate at smaller attack angles, typically below 10°. This insight is crucial for control strategies in real-world applications, where maintaining a streamlined orientation can significantly enhance the bionic robot’s endurance and maneuverability.
In summary, this movable-wing underwater bionic robot demonstrates promising hydrodynamic characteristics through its biomimetic design. The imitation of manta ray morphology, coupled with an oscillatory propulsion system, results in reduced drag and improved stability compared to traditional propeller-driven robots. The CFD simulations validate the design’s efficacy, showing favorable lift-to-drag ratios and manageable pressure distributions across various operating conditions. Future work could involve prototyping and experimental testing in water tanks to further refine the bionic robot’s performance. Additionally, integrating advanced materials or adaptive control algorithms may enhance its responsiveness to dynamic environments. The development of such bionic robots not only advances underwater exploration capabilities but also inspires innovations in sustainable marine technology. By continuing to learn from nature, we can create more efficient and versatile bionic robots that navigate the oceans with minimal ecological impact, paving the way for new discoveries in marine science and engineering.