Fluid-Structure Coupled Computational Analysis of Submerged Body Approaching a Moving Wall with Application to Dynamic UUV Docking
Abstract
The goal of this three-year NURP project is to develop a three-dimensional computational model capable of predicting the unsteady hydrodynamic interaction induced by a submerged solid body approaching a moving wall boundary, with application to the control of unmanned underwater vehicles (UUVs). To achieve this goal, the project will utilize both commercial computational fluid dynamics (CFD) software, such as Ansys Fluent, and an in-house fluid-structure interaction (FSI) simulation framework developed by the PI and collaborators. Equipped with an embedded boundary method for interface tracking and a Robin interface condition to ensure numerical stability, the in-house FSI framework is well-suited for investigating the current problem, which features large structural motion, strong added-mass effect, and complex vortex-dominated flows. The project will start with modeling and simulating simplified example problems with a cylindrical body and a stationary flat plate in a crossflow. Prescribed cylinder speeds will be comparable to the freestream flow velocity, with Reynolds numbers of the order of 10^4 for the cylinder and 10^5 for the plate. The numerical tests will be designed based on recent water tunnel experiments conducted at NUWCDIVNPT and University of Rhode Island. The experimental results will be used to validate the numerical methods and solvers. Next, a two-way fluid-structure coupled simulation model will be developed, featuring a six-degree-of-freedom underwater rigid body that moves towards a wall boundary at various speeds and angles. As the body approaches and enters the boundary layer of the wall, the variation of fluid pressure, velocity, and vorticity will be examined using flow visualization methods and data-based feature extraction algorithms (e.g., principal component analysis, dynamic mode decomposition). Using the Tinkercliffs supercomputing resource at Virginia Tech, a comprehensive parametric study will be conducted to investigate the two-way interaction between the fluid flows generated by the underwater body and the moving wall, as well as the impact of this flow interaction on the body#s dynamics and stability. Using the computational model and the features extracted from the parametric study, a physics-based, data-driven reduced-order model will also be developed, to estimate the hydrodynamic loads on underwater rigid bodies (e.g. simplified vehicle representations) moving at different speeds and directions.Potential Impact to Navy Capabilities: This project is closely aligned with two NURP Research Concentration Areas, namely hydrodynamics and autonomy. Currently, the range and functionality of UUVs are heavily limited by the capacity of onboard batteries. One promising approach to overcome this issue is dynamic docking of UUVs to a larger, possibly moving carrier vessel. This NURP project supplements an ongoing research effort at NUWCDIVNPT on understanding and predicting the hydrodynamic loads on an UUV during this operation. The new computational models, codes, and data generated from this project can be applied to guide the design of UUV docking protocols and control algorithms. In particular, the efficiency of the developed computational framework will be uniquely suited to the generation and analysis of the multitude of launch and recovery scenarios necessary to confidently cover the full parameter space associated with these operations. The proposed work will advance the knowledge base of fluid dynamics and FSI by elucidating the hydrodynamic interference of multiple solid bodies in close vicinity and in contact. Leveraging the collaboration between NUWC and Virginia Tech, this project will also provide graduate students an outstanding multidisciplinary learning environment with exposure to fluid dynamics, high-performance computing, experimental validation, and large-scale data analytics.Approved for Public Release
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 15, 2023
- Source ID
- N000142312447
Entities
People
- Kevin G. Wang
Organizations
- Office of Naval Research
- United States Navy
- Virginia Tech