Computational fluid dynamics

The capability to navigate in the proximity of solid surfaces while avoiding collision and maintaining high efficiency is essential for the effective design and operation of underwater vehicles. The underlying capability involves a variety of challenges, and a potential approach to overcome such obstacles is to rely on biomimetic or bio-inspired design. Through evolution, organisms have developed methods of locomotion optimized for their specific environment. One of the common forms of locomotion found in underwater organisms is undulatory swimming. These undulatory swimmers display different swimming behaviors based on the flow conditions in their environment. These behaviors take advantage of changes in the flow field caused by the presence of obstructions and obstacles upstream or adjacent to the swimmer. For example, a free swimmer in near-proximity to a flat plane can experience changes in lift and drag during locomotion. The reduced drag can benefit the swimmer, however, changes in lift may lead to a collision with obstacles. Despite the abundance of qualitative data from observing these undulatory swimmers, there is a lack of quantitative data, creating a disconnect in understanding how these organisms have evolved to exploit the presence of walls and obstacles. By employing a combination of traditional computational fluid dynamics and novel neural network-based techniques it is possible to emulate the evolution of learned behavior in biological organisms. The current work uses deep reinforcement learning coupled with two-dimensional numerical simulations of self-propelled swimmers to better understand behavior observed in nature.

Aquatic organisms are able to achieve swimming efficiencies that are much higher than any underwater vehicle that has been designed by humans. This is mainly due to the adaptive swimming patterns that they display in response to changes in their environment and their behaviors, i.e., hunting, fleeing, or foraging. In this work, we explore these adaptations from a hydrodynamics standpoint, using numerical simulations to emulate self-propelled artificial swimmers in various flow fields. Apart from still or uniform flow, the most likely flow field encountered by swimmers are those formed by the wakes of solid objects, such as roots of aquatic vegetation, or underwater structures. Therefore, a simplified bio-inspired design of porous structures consisting of nine cylinders was considered to identify arrangements that could produce wakes of varying velocities and enstrophy, which in turn might provide beneficial environments for underwater swimmers. These structures were analyzed using a combination of numerical simulations and experiments, and the underlying flow physics was examined using a variety of data-analysis techniques.
Subsequently, in order to recreate the adaptations of natural swimmers in different flow regimes, artificial swimmers were positioned in each of these different types of flow fields and then trained to optimize their movements to maximize swimming efficiency using deep reinforcement learning. These artificial swimmers utilize a sensory input system that allows them to detect the velocity field and pressure on the surface of their body, which is similar to the lateral line sensing system in biological fish. The results demonstrate that the information gleaned from the simplified lateral line system was sufficient for the swimmer to replicate naturally found behaviors such as K´arm´an gaiting. The phenomenon of schooling in underwater organisms is similarly thought to provide opportunities for swimmers to increase their energy efficiency, along with the other associated benefits. Thus, multiple swimmers were trained using multi-agent reinforcement learning to discover optimal swimming patterns at the group level as well as the individual level.

A computational investigation of the hydrodynamic and seakeeping performance of a catamaran in calm, and in the presence of transforming head and following seas in waters of constant and varying depths is described. Parametric studies were conducted for a selected WAM-V 16 catamaran geometry using OpenFOAM® to uncover the physical phenomena. In the process a methodology has been developed for simulating the interactions between the vehicle and the shallow water environment akin to that in the coastal environment. The multiphase flow around the catamaran, including the six degrees-of-freedom motion of the vehicle, was modeled using a Volume of Fluid (VoF) method and solved using a dynamic mesh. The numerical approach was validated through computing benchmark cases and comparing the results with previous work. It is found that in a calm shallow water environment the total resistance, dynamic trim and sinkage of a catamaran in motion can be significantly impacted by the local water depth. The variations of the impact with depth and length-based Froude numbers are characterized. The impact varies as the vehicle moves from shallow waters to deep water or vice versa. In the presence of head and following small-amplitude seas, interesting interactions between incident waves and those generated by the vehicle are observed and are characterized for their variation with Froude number and water depth.

This study analyzes the hydrodynamic performance of an advanced catamaran vehicle using computational fluid dynamics (CFD) simulations and experimental testing data in support of system identification and development of a physics-based control system for unmanned surface vehicle (USV) operations in coastal waters. A series of steps based on increasing complexity are considered sequentially in this study. First the steady flow past the static vehicle, then the vehicle with a fixed orientation advancing in calm water, and finally the vehicle moving with two degrees of freedom (DOF) in calm water as well as head seas.
The main objective of the study is to assess the role of general multiphase unsteady Reynolds Averaged Navier Stokes (RANS) as a predictive tool for the hydrodynamic performance of an USV. A parametric analysis of the vehicle performance at different Froude number and wave steepness in shallow waters is conducted. The characteristics of the wave resistance, heaving and pitching motion, wave-hull interactions, and free surface flow patterns are investigated. The study will aid in the design of a robust physics-based control system for the vehicle and provide a tool for prediction of its performance.

Surface pressure fluctuations developed by turbulent flow within a boundary layer is a major cause of flow noise from a body and an issue which reveals itself over a wide range of engineering applications. Modified boundary layers (MBLs) inspired by the down coat of an owl’s wing has shown to reduce the acoustic effects caused by flow noise. This thesis investigates the mechanisms that modified boundary layers can provide for reducing the surface pressure fluctuations in a boundary layer. This study analyzes various types of MBLs in a wall jet wind tunnel through computational fluid dynamics and numerical surface pressure spectrum predictions. A novel surface pressure fluctuation spectrum model is developed for use in a wall jet boundary layer and demonstrates high accuracy over a range of Reynolds numbers. Non-dimensional parameters which define the MBL’s geometry and flow environment were found to have a key role in optimizing the acoustic performance.

This project is intended to demonstrate the current state of knowledge in the prediction of the tonal and broadband noise radiation from a Sevik rotor. The rotor measurements were made at the Virginia Tech Stability Wind Tunnel. Details of the rotor noise and flow measurements were presented by Wisda et al(2014) and Murray et al(2015) respectively. This study presents predictions based on an approach detailed by Glegg et al(2015) for the broadband noise generated by a rotor in an inhomogeneous flow, and compares them to measured noise radiated from the rotor at prescribed observer locations. Discrepancies between the measurements and predictions led to comprehensive study of the flow in the wind tunnel and the discovery of a vortex upstream of the rotor at low advance ratios. The study presents results of RANS simulations. The static pressure and velocity profile in the domain near the rotor's tip gap region were compared to measurements obtained from a pressure port array and a PIV visualization of the rotor in the wind tunnel.

Super-tall buildings located in high velocity wind regions are highly vulnerable to large lateral loads. Designing for these structures must be done with great engineering judgment by structural professionals. Present methods of evaluating these loads are typically by the use of American Society of Civil Engineers 7-10 standard, field measurements or scaled wind tunnel models. With the rise of high performance computing nodes, an emerging method based on the numerical approach of Computational Fluid Dynamics has created an additional layer of analysis and loading prediction alternative to conventional methods. The present document uses turbulence modeling and numerical algorithms by means of Reynolds-averaged Navier-Stokes and Large Eddy Simulation equations applied to a square prismatic prototype structure in which its dynamic properties have also been investigated. With proper modeling of the atmospheric boundary layer flow, these numerical techniques reveal important aerodynamic properties and enhance flow visualization to structural engineers in a virtual environment.

The numerical method presented in this study attempts to predict the mean, non-uniform flow field upstream of a propeller partially immersed in a thick turbulent boundary layer with an actuator disk using CFD based on RANS in ANSYS FLUENT. Three different configurations, involving an infinitely thin actuator disk in the freestream (Configuration 1), an actuator disk near a wall with a turbulent boundary layer (Configuration 2), and an actuator disk with a hub near a wall with a turbulent boundary layer (Configuration 3), were analyzed for a variety of advance ratios ranging from J = 0.48 to J =1.44. CFD results are shown to be in agreement with previous works and validated with experimental data of reverse flow occurring within the boundary layer above the flat plate upstream of a rotor in the Virginia Tech’s Stability Wind Tunnel facility. Results from Configuration 3 will be used in future aero-acoustic computations.

For aerospace and naval applications where low radiated noise levels are a
requirement, rotor noise generated by inflow turbulence is of great interest. Inflow
turbulence is stretched and distorted as it is ingested into a thrusting rotor which can have
a significant impact on the noise source levels. This thesis studies the distortion of
subsonic, high Reynolds number turbulent flow, with viscous effects ignored, that occur
when a rotor is embedded in a turbulent boundary layer. The analysis is based on Rapid
Distortion Theory (RDT), which describes the linear evolution of turbulent eddies as they
are stretched by a mean flow distortion. Providing that the gust does not distort the mean
flow streamlines the solution for a mean flow with shear is found to be the same as the
solution for a mean potential flow with the addition of a potential flow gust. By
investigating the inflow distortion of small-scale turbulence for various simple flows and
rotor inflows with weak shear, it is shown that RDT can be applied to incompressible
shear flows to determine the flow distortion. It is also shown that RDT can be applied to more complex flows modeled by the Reynolds Averaged Navier Stokes (RANS)
equations.

Boundary layers are regions where turbulence develops easily. In the case where the flow occurs on a surface showing a certain degree of roughness, turbulence eddies will interact with the roughness elements and will produce an acoustic field. This thesis aims at predicting this type of noise with the help of the Computational Fluid Dynamics (CFD) simulation of a wall jet using the Reynolds Average Navier-Stokes (RANS) equations. A frequency spectrum is reconstructed using a representation of the turbulence with uncorrelated sheets of vorticity. Both aerodynamic and acoustic results are compared to experimental measurements of the flow. The CFD simulation of the flow returns consistent results but would benefit from a refinement of the grid. The surface pressure spectrum presents a slope in the high frequencies close to the experimental spectrum. The far field noise spectrum has a 5dB difference to the experiments.