Search for universal minimum drag resistance underwater vehicle hull using CFD

TL;DR

This paper explores the use of AI-driven CFD optimization to identify a hull design for underwater vehicles that minimizes resistance across various operating and environmental conditions, aiming for a universal optimal design.

Contribution

It introduces a CFD-based optimization approach to find a hull design that performs well across diverse conditions, addressing the challenge of universal minimal resistance in AUVs.

Findings

High-velocity, high-turbulence optimal design performs near-optimally across conditions.

Low-velocity, low-turbulence optimal design performs poorly at other conditions.

Universal design at high velocity and turbulence shows promising results.

Abstract

In Autonomous Underwater Vehicles (AUVs) design, hull resistance is an important factor in determining the power requirements and range of vehicle and consequently affect battery size, weight, and volume requirement of the design. In this paper, we leverage on AI-based optimization algorithm along with Computational Fluid Dynamics (CFD) simulation to study the optimal hull design that minimizing the resistance. By running the CFD-based optimization at different operating velocities and turbulence intensity, we want to study/search the possibility of a universal design that will provide least resistance/near-optimal design across all operating conditions (operating velocity) and environmental conditions (turbulence intensity). Early result demonstrated that the optimal design found at low velocity and low turbulence condition performs very poor at high velocity and high turbulence conditions. However, a design that is optimal at high velocity and high turbulence conditions performs near-optimal across many considered velocity and turbulence conditions.