Zq Leong

Effect of RANS-based turbulence models on nonlinear wave generation in a two-phase numerical wave tank

Ocean waves are the most important exciting source acting on marine structures such as ships, offshore platforms, wave energy converters and wavebreakers. In order to efficiently design the aforementioned structures, accurate modelling of these waves is of importance. In this paper a two dimensional Numerical Wave Tank (NWT) has been established based on the Reynoldsaveraged NavierStokes (RANS) equations for viscous, incompressible fluid and Volume of Fluid (VOF) method and a commercial software code ANSYS FLUENT (Release 15.0) has been used to numerically investigate ocean wave generation. Impact of different turbulence models such as standard k-ɛ, realizable k-ɛ, Shear Stress Transport (SST) and Reynolds Stress Models (RSM) on the generated ocean surface waves were investigated. With all uncertainties associated with various numerical setting aspects, experimental wave measurements over a wide range of wave conditions covering intermediate and deep water regimes have been conducted in a physical wave basin to validate the numerical results. The excessive generation of eddy viscosity resulted from using eddy viscosity turbulence models especially at the free surface interface, leads to a significant unphysical damping on the generated waves. Good numerical agreement with both experimental measurements and analytical wave theory was successfully achieved either with the RSM or implementing artificial turbulence damping at the airwater interface with the SST model.

Autonomous underwater vehicle motion response: a nonacoustic tool for blue water navigation

Autonomous underwater vehicles (AUVs) use secondary velocity over ground measurements to aid the Inertial Navigation System (INS) to avoid unbounded drift in the point-to-point navigation solution. When operating in deep open ocean (i.e., in blue water—beyond the frequency-specific instrument range), the velocity mea- surements are either based on water column velocities or completely unavailable. In such scenarios, the velocity-relative-to-water measurements from an acoustic Doppler current profiler (ADCP) are often used for INS aiding. ADCPs have a blank- ing distance (typically ranging between 0.5 and 5 m) in proximity to the device in which the flow velocity data are undetectable. Hence, water velocities used to aid the INS solution can be significantly different from that near the vehicle and are subjected to significant noise. Previously, the authors introduced a nonacoustic method to cal- culate the water velocity components of a turbulent water column within the ADCP dead zone using the AUV motion response (referred to as the WVAM method). The current study analyzes the feasibility of incorporating the WVAM method within the INS by investigating the accuracy of it at different turbulence levels of the water column. Findings of this work demonstrate that the threshold limits of the method can be improved in the nonlinear ranges (i.e., at low and high levels of energy); however, by estimating a more accurate representation of vehicle hydrodynamic coefficients, this method has proven robust in a range of tidally induced flow con- ditions. The WVAM method, in its current state, offers significant potential to make a key contribution to blue water navigation when integrated within the vehicle’s INS.

Computational fluid dynamics re-mesh method to generating hydrodynamic models for maneuvering simulation of two submerged bodies in relative motion

An Autonomous Underwater Vehicle (AUV) operating closer to a larger vessel experiences significant hydrodynamic forces requiring an adaptive control mechanism to maintain acceptable trajectory. It is therefore important that the designer understands the hydrodynamic characteristics of the vehicle in this scenario in order to develop appropriate control algorithms to deal with its dynamic behaviour. This requires developing simulations of the vehicles behaviour close to the larger vessel, the control algorithms, and the dynamic interface between the two. This paper presents a method to generate a complete hydrodynamic model of underwater vehicles using the Computational Fluid Dynamics (CFD) code ANSYS CFX, which can then be interfaced with the vehicles control algorithms within a simulation environment. The essential aspect of the method is the re-mesh approach, where the mesh deforms locally around the bodies using an Arbitrary Lagrangian-Eulerian form of the governing fluid equations and re-meshes when the deformation significantly compromises the quality of the mesh. This overcomes the motion limitations imposed by a pure deforming mesh approach. Preliminary work to validate the method is based on two smooth spheres moving relative to each other. It shows that this method is able to adequately simulate the fluid behaviour around the bodies. The paper also describes the future work focused on a 6 degrees-of-freedom (6-DOF) AUV modelled in CFD to obtain its hydrodynamic behaviour to be interfaced to the control system within MATLAB.

Technologies for under-ice AUV navigation

Approximately 12% of the worlds oceans are covered by ice. Understanding the physical processes, ecosystem structure, mixing dynamics and the role of these inaccessible environments in the context of global climate change is extremely important. Autonomous Underwater Vehicles (AUVs) play a major role in the potential exploration of these water systems due to the challenges of human access and relatively high associated risk. That said, AUV navigation and localization is challenging in these environments due to the unavoidable growth of navigational drift associated with inertial navigation systems, especially in long range missions under ice where surfacing in open water is not possible. While acoustic transponders have been used, they are time consuming and difficult to deploy. Terrain Relative Navigation (TRN) and Simultaneous Localization and Mapping (SLAM) based technologies are emerging in recent years as promising navigation solutions as they neither require deploying navigational aids or calculating the distance travelled from a reference point to determine location. One of the key challenges of underwater or under-ice image based localization results from the unstructured nature and lack of significant features in underwater environments. This issue has motivated the review presented in this paper, which outlines a potential area of under-ice AUV navigation and localization by combining TRN and SLAM with image matching methods for navigation in featureless environments.

Estimating flow velocities of the water column using the motion response of an Autonomous Underwater Vehicle (AUV)

The WVAM method is a nonacoustic method to calculate the velocity components of a turbulent water column using the motion response of an Autonomous Underwater Vehicle (AUV) without the aid of an Acoustic Doppler Current Profiler (ADCP). This study analyses water velocity measurements estimated using the WVAM method as a function of the turbulence level of the environment by testing the method in an estuary that exhibits strong tidal currents (around 2 m/s). The uncertainty of this method at different water column conditions was computed by comparing the velocity measurements from the WVAM method with those obtained from the AUV mounted ADCP. The WVAM method determines the water velocities by comparing the motion response of the vehicle when operating within turbulent and calm water environments respectively. The motion of the vehicle in the calm water environment was obtained by conducting simulations of the vehicles in calm water under the same control commands executed during the field experiments in turbulent conditions. A reduction in the accuracy of the method in rougher water environments was observed due to the hydrodynamic coefficients of the simulation model reaching their nonlinear range limits. A possible strategy to overcome this limitation and improve the WVAM methods ability to accurately estimate the flow field in the vicinity of AUVs operating in highly turbulent environments is also provided.