# Search for universal minimum drag resistance underwater vehicle hull using CFD

arXiv: https://arxiv.org/abs/2302.09441 · HTML: https://mlsanity.com/paper/2302.09441/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Constrained Efficient Global Optimization of Expensive Black-box Functions](https://mlsanity.com/paper/2211.00162/) (2022)
- [Rare event failure test case generation in Learning-Enabled-Controllers](https://mlsanity.com/paper/2206.05533/) (2022)
- [Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression](https://mlsanity.com/paper/2211.10360/) (2022)
- [Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models](https://mlsanity.com/paper/2206.09247/) (2022)
- [Modeling and Optimization of a Longitudinally-Distributed Global Solar Grid](https://mlsanity.com/paper/2206.05584/) (2022)

## Built on by (most cited first, 8 of 8)

- [Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design](https://mlsanity.com/paper/2302.14732/) (2023)
- [Fusion of ML with numerical simulation for optimized propeller design](https://mlsanity.com/paper/2302.14740/) (2023)
- [Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls](https://mlsanity.com/paper/2304.12420/) (2023)
- [Vehicle lateral control using Machine Learning for automated vehicle guidance](https://mlsanity.com/paper/2303.08187/) (2023)
- [Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge](https://mlsanity.com/paper/2404.16893/) (2024)
- [Systematic design space exploration by learning the explored space using Machine Learning](https://mlsanity.com/paper/2303.08249/) (2023)
- [Optimization for truss design using Bayesian optimization](https://mlsanity.com/paper/2306.01763/) (2023)
- [Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool](https://mlsanity.com/paper/2407.02519/) (2024)

## Abstract (arXiv)

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.