Optimizing Unmanned Underwater Vehicle Hull Designs through Sample-Efficient Optimization and Surrogate Modeling

Core Concepts

This work explores two approaches to efficiently optimize the design of unmanned underwater vehicle (UUV) hulls: sample-efficient optimization and surrogate modeling. The authors find that Bayesian Optimization with Lower Confidence Bound (BO-LCB) is the most sample-efficient optimization method, and they develop a deep neural network-based surrogate model that can predict drag forces with high accuracy, enabling a two-orders-of-magnitude speedup in the design optimization process.

Abstract

The authors present an integrated toolchain for automated UUV hull design optimization, combining parametric CAD modeling, CFD simulations, and various optimization algorithms.
For the first approach, they evaluate the sample efficiency and convergence behavior of different optimization methods, including Monte Carlo sampling, Latin Hypercube, Genetic Algorithm, Nelder-Mead, and Bayesian Optimization (BO-LCB and BO-EI). They find that BO-LCB is the most sample-efficient and has the best convergence behavior.
For the second approach, the authors generate a large dataset of UUV hull designs and their corresponding drag forces computed via CFD simulations. They then train a deep neural network (DNN) surrogate model to accurately predict drag forces, achieving a mean absolute percentage error (MAPE) of 1.85% on test data.
The authors demonstrate that using the trained DNN surrogate in the optimization loop provides a two-orders-of-magnitude speedup compared to the CFD-in-the-loop optimization, while maintaining comparable accuracy in the final optimal design.

Stats

The steady-state drag force F experienced by the UUV hull as it flows through water is the key metric used to evaluate the designs.

Quotes

"To our knowledge, this is the first study applying Bayesian optimization and DNN-based surrogate modeling to the problem of UUV design optimization, and we share our developments as open-source software."

Key Insights Distilled From

Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls

by Harsh Vardha... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2304.12420.pdf

Deeper Inquiries

How could the parametric CAD model be extended to support more complex hull features, such as dimples, and how would that impact the optimization and surrogate modeling approaches

To extend the parametric CAD model to support more complex hull features like dimples, several adjustments would be necessary. Dimples are known to reduce drag by controlling the boundary layer separation and turbulence, leading to improved hydrodynamics. In the context of the UUV hull design, incorporating dimples would require additional parameters in the CAD model to define their size, shape, and distribution on the hull surface. These parameters could include dimple diameter, depth, spacing, and pattern.
Integrating dimples into the parametric CAD model would impact the optimization and surrogate modeling approaches in several ways. Firstly, the design space would expand to include dimple-related parameters, increasing the complexity of the optimization problem. The surrogate model would need to be trained on a larger dataset that includes designs with dimples to accurately capture the relationship between dimple features and drag force. This would require more computational resources for training and validation.
In the optimization process, the inclusion of dimples would introduce additional design constraints and objectives related to dimple optimization for drag reduction. The optimization algorithms would need to be modified to handle these new constraints effectively. The surrogate model would play a crucial role in evaluating the performance of designs with dimples, providing quick feedback on their impact on drag force. Overall, incorporating dimples into the parametric CAD model would enhance the design space complexity and optimization challenges while potentially leading to more efficient and hydrodynamically optimized UUV hull designs.

What challenges would arise in attempting to train a single surrogate model capable of handling all classes of UUVs, from small to large, and how could those challenges be addressed

Training a single surrogate model capable of handling all classes of UUVs, from small to large, presents several challenges. One major challenge is the diversity in design parameters and characteristics across different classes of UUVs. Small UUVs may have different hull shapes, dimensions, and operating conditions compared to large UUVs, leading to variations in the flow behavior and drag forces. A single surrogate model would need to generalize well across this wide range of design spaces and fluid dynamics scenarios.
To address these challenges, a few strategies could be implemented. Firstly, the training dataset for the surrogate model would need to be diverse and representative of the entire spectrum of UUV classes. This would involve collecting data from various UUV designs, including small, medium, and large classes, to ensure the model captures the nuances of each class. Additionally, the surrogate model architecture should be flexible and adaptable to different design parameters and flow conditions, allowing it to learn complex relationships effectively.
Furthermore, incorporating transfer learning techniques could help the surrogate model leverage knowledge gained from one class of UUVs to improve performance on another class. By fine-tuning the model on specific classes or using domain adaptation methods, the surrogate could enhance its predictive capabilities across different UUV categories. Regular validation and testing on diverse datasets would be essential to ensure the surrogate's robustness and accuracy across all UUV classes.

How would the optimization and surrogate modeling approaches need to be adapted to handle more complex flow conditions, such as higher turbulence or particulate flow, that are more representative of real-world UUV operating environments

Handling more complex flow conditions, such as higher turbulence or particulate flow, in the optimization and surrogate modeling approaches would require specific adaptations to account for the increased fluid dynamics complexity. In the context of UUV design optimization, these challenges could significantly impact the accuracy and efficiency of the models.
For optimization, the algorithms would need to incorporate turbulence models that accurately represent the flow behavior in high-turbulence environments. This may involve using advanced turbulence models in the CFD simulations to capture the turbulent effects on drag force more effectively. The optimization process would also need to consider the impact of particulate flow on the hull design, potentially introducing new design constraints related to particle interaction and sediment transport.
In surrogate modeling, the training dataset would need to include data from simulations with higher turbulence levels and particulate flow conditions to ensure the model captures these complex dynamics. The neural network architecture may need to be adapted to handle the increased variability and non-linearity in the data generated from these simulations. Techniques like ensemble learning or hybrid models combining physics-based and data-driven approaches could be explored to improve the surrogate model's performance in handling complex flow conditions.
Overall, adapting the optimization and surrogate modeling approaches to handle more complex flow conditions would require a deeper understanding of the fluid dynamics involved, advanced modeling techniques, and a comprehensive dataset that represents the diverse scenarios encountered in real-world UUV operating environments.