2.4 Computational fluid dynamics for underwater robotics
5 min read•july 30, 2024
Computational fluid dynamics (CFD) is a game-changer for underwater robotics. It uses math and computers to solve complex fluid flow problems, helping designers create better underwater vehicles. CFD simulations analyze drag, propulsion, and water interactions, optimizing robot shapes for peak performance.
This chapter dives into CFD's role in hydrodynamics for submerged vehicles. We'll cover the basics, turbulence modeling, and practical applications. You'll learn how CFD helps create more efficient, maneuverable, and stable underwater robots for real-world missions.
CFD for Underwater Robotics
Principles and Applications
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Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems involving fluid flows
The fundamental principles of CFD are based on the conservation laws of physics: conservation of mass, momentum, and energy
The continuity equation describes the conservation of mass, stating that the rate of change of fluid density in a control volume is equal to the net mass flux through its boundaries
The describe the conservation of momentum, relating the acceleration of a fluid particle to the forces acting on it (pressure gradients, viscous stresses, and body forces)
The energy equation describes the conservation of energy, accounting for heat transfer and work done by the fluid
CFD simulations discretize the fluid domain into a mesh of small elements and solve the governing equations iteratively using numerical methods (finite difference, finite volume, or finite element methods)
Turbulence Modeling and Applications in Underwater Robotics
Turbulence modeling is a crucial aspect of CFD for underwater robotics, as it captures the complex, chaotic motion of fluids at high Reynolds numbers
Common turbulence models include:
Reynolds-Averaged Navier-Stokes (RANS) models (k-epsilon and k-omega)
(LES)
CFD is applied in underwater robotics to study various phenomena:
Hydrodynamic drag, lift, and moment forces
Propulsion efficiency
CFD simulations help designers optimize the shape, size, and placement of underwater vehicle components to improve hydrodynamic performance and energy efficiency:
Hulls
Fins
Propellers
Control surfaces
CFD Modeling of Underwater Vehicles
Modeling Process and Geometry Creation
The CFD modeling process involves several steps: problem definition, geometry creation, , boundary condition specification, solver setup, and post-processing
The problem definition stage requires a clear understanding of the physical problem, the desired outcomes, and the simplifying assumptions to be made:
Steady-state or transient flow
Incompressible or compressible fluid
Laminar or turbulent regime
Geometry creation involves constructing a digital representation of the underwater vehicle and its surrounding fluid domain using computer-aided design (CAD) tools or importing existing models
Mesh Generation and Boundary Conditions
Mesh generation is the process of discretizing the fluid domain into a collection of small elements (tetrahedra or hexahedra)
The mesh quality, refinement, and resolution are critical factors affecting the accuracy and convergence of the CFD solution:
Structured meshes have regular connectivity and are suitable for geometries
Unstructured meshes have irregular connectivity and are more flexible for complex shapes
Mesh refinement techniques (local refinement and adaptive meshing) help capture flow details in regions of high gradients or interest
Boundary conditions specify the fluid properties and flow conditions at the domain boundaries:
Solver Setup and Post-Processing
Solver setup involves choosing the appropriate numerical schemes, convergence criteria, and solution methods for the specific CFD problem
Common solution algorithms include:
SIMPLE
Post-processing involves visualizing and analyzing the CFD results to gain insights into the fluid flow behavior and hydrodynamic performance of the underwater vehicle:
CFD Simulation Validation
Validation Techniques and Metrics
Validation is the process of assessing the accuracy and reliability of CFD simulations by comparing them with experimental measurements or real-world observations
Experimental validation techniques for underwater robotics include:
Towing tank tests: measure the resistance, propulsion, and maneuvering characteristics of scale models or full-size vehicles in controlled conditions
Water tunnel experiments: use particle image velocimetry (PIV) or laser Doppler velocimetry (LDV) to measure the velocity fields and turbulence properties around underwater vehicles
Field trials: test the vehicle in real-world environments (lakes, rivers, or oceans) to assess its performance under various operating conditions
Validation metrics compare the CFD results with experimental data using statistical measures:
Uncertainty Quantification and Iterative Refinement
(UQ) techniques help assess the impact of input uncertainties on the CFD simulation results:
Geometry variations
Fluid properties
Boundary conditions
Validation studies should cover a range of operating conditions and vehicle configurations to establish the credibility and applicability of the CFD model
Iterative refinement of the CFD model, based on the validation findings, helps improve its predictive capabilities and reliability for future design and analysis tasks
CFD Optimization for Underwater Vehicles
Optimization Techniques and Objectives
CFD-based optimization involves using numerical simulations to find the best design parameters that maximize the performance objectives while satisfying the constraints
Design parameters for underwater vehicles include:
Hull shape
Fin size and placement
Propeller geometry
Control surface configurations
Performance objectives may include:
Minimizing drag
Maximizing propulsive efficiency
Improving maneuverability
Enhancing stability
Constraints may include:
Size limitations
Weight budgets
Structural integrity
Manufacturing feasibility
Optimization Algorithms and Multidisciplinary Approaches
Optimization algorithms search the design space by iteratively modifying the design parameters and evaluating the performance using CFD simulations:
Multi-objective optimization techniques help find trade-offs between conflicting objectives:
Robust optimization approaches account for uncertainties in the design parameters or operating conditions to ensure the vehicle's performance is insensitive to variations
CFD-based optimization can be applied to various underwater scenarios:
High-speed cruising
Low-speed maneuvering
Hovering
Energy harvesting
Coupling CFD with other analysis tools enables a multidisciplinary optimization approach for underwater vehicle design:
Structural mechanics
Control systems
Case Studies and Success Stories
Case studies and success stories demonstrate the benefits of CFD-based optimization in improving the efficiency, reliability, and performance of underwater robots in real-world applications: