Search for universal minimum drag resistance underwater vehicle hull using CFD
This addresses the challenge of reducing power requirements and improving range for AUVs, though it appears incremental as it builds on existing CFD and optimization methods.
The paper tackled the problem of designing an Autonomous Underwater Vehicle (AUV) hull with minimal drag resistance across varying operating velocities and turbulence intensities, using AI-based optimization with CFD simulations, and found that a design optimal at high velocity and high turbulence conditions performs near-optimally across many conditions.
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.