Numerical Simulation of a Marine Current Turbine in Turbulent Flow

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorThe marine current turbine (MCT) is an exciting proposition for the extraction of renewable tidal and marine current power. However, the numerical prediction of the performance of the MCT is difficult due to its complex geometry, the surrounding turbulent flow and the free surface. The main purpose of this research is to develop a computational tool for the simulation of a MCT in turbulent flow and in this thesis, the author has modified a 3D Large Eddy Simulation (LES) numerical code to simulate a three blade MCT under a variety of operating conditions based on the Immersed Boundary Method (IBM) and the Conservative Level Set Method (CLS). The interaction between the solid structure and surrounding fluid is modelled by the immersed boundary method, which the author modified to handle the complex geometrical conditions. The conservative free surface (CLS) scheme was implemented in the original Cgles code to capture the free surface effect. A series of simulations of turbulent flow in an open channel with different slope conditions were conducted using the modified free surface code. Supercritical flow with Froude number up to 1.94 was simulated and a decrease of the integral constant in the law of the wall has been noticed which matches well with the experimental data. Further simulations of the marine current turbine in turbulent flow have been carried out for different operating conditions and good match with experimental data was observed for all flow conditions. The effect of waves on the performance of the turbine was also investigated and it has been noticed that this existence will increase the power performance of the turbine due to the increase of free stream velocity

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics