Hydrodynamic Analysis of Autonomous Underwater Vehicles
Summary
Autonomous underwater vehicles (AUVs) rely critically on optimised hydrodynamic design to achieve extended range, precise manoeuvring and minimal environmental disturbance. Hydrodynamic analysis encompasses the study of resistance, lift, added mass and damping forces acting on an AUV hull and its control surfaces as it moves through water. Contemporary approaches combine computational fluid dynamics (CFD) simulations with experimental towing-tank tests and empirical modelling to characterise viscous drag, pressure distribution and wake development. Advances in turbulence modelling, unsteady flow simulation and data-driven techniques have enabled more accurate predictions of flow separation, vortex dynamics and hydro-acoustic noise. The integration of machine-learning frameworks has further accelerated design cycles by generating rapid, high-fidelity approximations of hydrodynamic behaviour. These methodologies inform hull form optimisation, control-surface layout and propulsion integration to enhance energy efficiency and stability in varied ocean conditions. A comprehensive hydrodynamic framework is essential for the deployment of AUVs in scientific exploration, environmental monitoring, offshore inspection and defence operations, where endurance, stealth and precise control dictate mission success.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Hydrodynamic Analysis of Autonomous Underwater Vehicles publication trend
The graph below shows the total number of articles in hydrodynamic analysis of autonomous underwater vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
Computational fluid dynamics (CFD): numerical method for solving the Navier–Stokes equations to predict flow patterns, pressure distributions and forces around submerged bodies.
Hydrodynamic derivatives: coefficients relating changes in vehicle motions (such as surge, sway and yaw) to resulting forces and moments, essential for dynamic stability and control analysis.
Drag coefficient: dimensionless parameter expressing the ratio of fluid drag force to dynamic pressure and reference area, used to compare resistance across different hull shapes.
Added mass: additional inertial load imposed on a moving body due to acceleration of the surrounding fluid, affecting transient response and control.
Reynolds-averaged Navier–Stokes (RANS) equations: time-averaged form of the Navier–Stokes equations that models turbulent flows by introducing closure approximations for Reynolds stresses.
References
- Examination of the vessel's shape, resistive force and volumetric-aqueous efficiencies to optimize the vessels' foil under noise propagation. Heliyon (2024).
- Establishment of empirical formulae for hydrodynamic derivatives of submarine considering design parameters. International Journal of Naval Architecture and Ocean Engineering (2023).
- A novel deep U-Net-LSTM framework for time-sequenced hydrodynamics prediction of the SUBOFF AFF-8. Engineering Applications of Computational Fluid Mechanics (2022).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.