Enterprise AI Analysis
Flapping Foil-Based Propulsion and Power Generation: A Comprehensive Review
This review consolidates the current understanding of flapping foil technology, highlighting its dual applications in bio-inspired propulsion and flow energy harvesting. By adopting a unified unsteady-aerodynamic perspective, it clarifies the operational duality: propulsion (momentum transfer, feathering parameter χ < 1) versus power generation (kinetic energy extraction, χ > 1). Experimental findings demonstrate that passive flexibility enhances propulsion but faces synchronization issues in power generation. Numerical studies reveal complex 3D vortex dynamics and the profound impact of density stratification on performance, where optimal kinematics shift due to resonant interactions with the Brunt–Väisälä frequency. The review advocates for a paradigm shift from traditional parametric sweeps to advanced computational fluid dynamics (CFD) (e.g., 3D Large-Eddy Simulations, LES) and Deep Reinforcement Learning (DRL) for optimal trajectory discovery. Future research should focus on tunable stiffness mechanisms, multi-phase environmental modeling, and AI-driven digital twins to bridge the gap between computational ideals and real-world deployment for next-generation marine robotics and sustainable power generation.
Executive Impact: Key Findings at a Glance
0%
Propulsive Efficiency Achieved
0.2-0
Optimal Strouhal Range
0%
Power Extraction Efficiency
Deep Analysis & Enterprise Applications
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87%
Peak Propulsive Efficiency (Anderson et al., Re=40k)
Enterprise Process Flow
Oscillating Foil Generates Effective Angle of Attack
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Leading-Edge Vortex (LEV) Forms
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Reverse Von Kármán Vortex Street in Wake
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Net Momentum Transfer to Fluid
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Thrust Generated
| Feature | Rigid Foil | Flexible Foil |
|---|---|---|
| Thrust Generation |
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| Wake Stability |
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| Dynamic Stall |
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Wave-Devouring Propulsion (WDP) for Ship Augmentation
Belibassakis et al. [51] demonstrated that an actively controlled NACA 0012 dynamic wing mounted at the bow of a ferry model could harness energy from ship's vertical motions in waves. This system acted as a damper, significantly reducing heaving and pitching amplitudes, and resulted in an overall performance enhancement (reduction in total resistance) of up to 30% in head waves around the ship's resonance frequency. This highlights the dual benefit of propulsion and stabilization.
30% Reduction in Total Resistance
45.4%
Max Power Extraction Efficiency (Semi-passive, Boudreau et al.)
Enterprise Process Flow
Foil Oscillates Against Fluid Flow
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Effective Angle of Attack Becomes Negative (χ > 1)
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Lift Force Aligns with Heaving Motion
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Fluid Performs Work on Foil
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Kinetic Energy Extracted
| Aspect | Low AR (e.g., AR=2) | High AR (e.g., AR=7.9) |
|---|---|---|
| 3D Effects |
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| Efficiency |
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Optimizing Semi-Active Flapping Airfoil Power Generators (FAPG)
Jiacheng et al. [54] investigated a semi-active FAPG, finding that the pitching period significantly impacts net output power. Shorter periods increased power consumed by pitching, reducing overall harvesting performance. This underscores the need for careful tuning and design to minimize energy losses in active components.
Pitching Period Critical for Net Power
18.3%
Efficiency Increase in Moderate Stratification (Wang et al.)
Enterprise Process Flow
Foil Interacts with Density Stratification
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Brunt-Väisälä Frequency Influences Resonance
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Optimal Kinematics Shifted for Performance
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Fluid Entrainment Enhances Thrust
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AI Optimizes Trajectories for Complex Flows
| Condition | Homogeneous Fluid | Strongly Stratified Fluid (Low Fr) |
|---|---|---|
| Optimal Strouhal Number (St) |
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| Underlying Mechanism |
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| Efficiency |
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AI-Driven Optimization for Flapping Foils
Bao et al. [73] and Wang et al. [74] demonstrated Deep Reinforcement Learning (DRL) algorithms coupled with CFD could autonomously discover optimal flapping trajectories. These AI-optimized paths outperformed human-designed sinusoidal motions, achieving superior thrust and efficiency by adaptively manipulating wake morphology. This represents a paradigm shift from traditional parametric sweeps to intelligent, data-driven control.
Outperforms Human-Designed Trajectories
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Estimated Annual Savings
$0
Hours Reclaimed Annually
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Phase 3: Scaled Deployment
Full-scale integration of AI solutions across relevant departments, comprehensive training, and continuous optimization.
Phase 4: Advanced Integration & Digital Twin
Leveraging AI for real-time adaptive systems, exploring multi-phase environmental modeling, and developing AI-driven digital twin frameworks for autonomous adaptation.
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