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Enhancing hydrodynamic efficiency in Autonomous Underwater Vehicles (AUVs) utilizing adjoint method and proper orthogonal decomposition

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  • Shorob Alam Bhuiyan
  • Md Jahid Hasan
  • Md Araful Hoque

Abstract

In recent years, there has been a significant need for high-performing and efficient Autonomous Underwater Vehicles (AUVs). This is primarily because of their use in offshore mineral exploitation and oceanographic research. While there have been notable breakthroughs in applying the adjoint technique to optimize air and land vehicles, there is still a deficiency in optimizing AUVs using the adjoint method. The present research explores how to improve the hydrodynamic efficiency of an AUV using the gradient-based adjoint technique and Proper Orthogonal Decomposition (POD). This study especially tries to minimize drag forces on the entire AUV by exclusively employing the adjoint approach on the AUV’s wing. The simulation was conducted using computational fluid dynamics methodology utilizing the Reynolds-averaged Navier–Stokes (RANS) model, with velocities ranging from 0.5 m/s to 2 m/s. Numerical computations demonstrated significant reductions in drag force, with the most advantageous improvements obtained when the wing geometry was altered by 9%. More precisely, the optimization resulted in a 9% drop in drag force at a speed of 1 m/s, going from 98.91 N to 90.17 N. By traveling at a speed of 2 m/s, a significant 17% reduction in drag force was achieved, reducing it from 386.34 N to 320.90 N. This signifies a substantial improvement of 20.25% in power consumption. The POD technique was employed to determine the dominant modes in the flow field, resulting in improved simulations and a better comprehension of flow patterns.

Suggested Citation

  • Shorob Alam Bhuiyan & Md Jahid Hasan & Md Araful Hoque, 2025. "Enhancing hydrodynamic efficiency in Autonomous Underwater Vehicles (AUVs) utilizing adjoint method and proper orthogonal decomposition," PLOS ONE, Public Library of Science, vol. 20(3), pages 1-33, March.
  • Handle: RePEc:plo:pone00:0319321
    DOI: 10.1371/journal.pone.0319321
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    References listed on IDEAS

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    1. Chang Li & Daniel C. Coster, 2022. "Improved Particle Swarm Optimization Algorithms for Optimal Designs with Various Decision Criteria," Mathematics, MDPI, vol. 10(13), pages 1-16, July.
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