We numerically simulate the hydrodynamic response of a floating offshore wind turbine (FOWT) using CFD. The FOWT under consideration is a slack-moored 1:70 scale model of the UMaine VolturnUS-S semisubmersible platform. This set-up has been experimentally tested in the COAST Laboratory Ocean Basin at the University of Plymouth, UK. The test cases under consideration are (i) static equilibrium load cases, (ii) free decay tests and (iii) two focused wave cases with different wave steepness. The FOWT is modeled using a two-phase Navier-Stokes solver inside the OpenFOAM-v2006 framework. The catenary mooring is computed by dynamically solving the equations of motion for an elastic cable using the MoodyCore solver. The results of the static and decay tests are compared to the experimental values with only minor differences in motions and mooring forces. The focused wave cases are also shown to be in good agreement with measurements. The use of a one-way fluid-mooring coupling results in slightly higher mooring forces, but does not influence the motion response of the FOWT significantly.
There are many uncertainties associated with the estimation of extreme loads acting on a wave energy converter (WEC). In this study we perform a sensitivity analysis of extreme loads acting on the Uppsala University (UU) WEC concept. The UU WEC consists of a bottom-mounted linear generator that is connected to a surface buoy with a taut mooring line. The maximum stroke length of the linear generator is enforced by end-stop springs. Initially, a Variation Mode and Effect Analysis (VMEA) was carried out in order to identify the largest input uncertainties. The system was then modeled in the time-domain solver WEC-SIM coupled to the dynamic mooring solver Moody. A sensitivity analysis was made by generating a surrogate model based on polynomial chaos expansions, which rapidly evaluates the maximum loads on the mooring line and the end-stops. The sensitivities are ranked using the Sobol index method. We investigated two sea states using equivalent regular waves (ERW) and irregular wave (IRW) trains. We found that the ERW approach significantly underestimates the maximum loads. Interestingly, the ERW predicted wave height and period as the most important parameters for the maximum mooring tension, whereas the tension in IRW was most sensitive to the drag coefficient of the surface buoy. The end-stop loads were most sensitive to the PTO damping coefficient.
We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modeling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from an LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduces errors that propagate through the hierarchical modeling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.
Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using eg Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modeling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactorily predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.
Linear potential flow (LPF) models remain the tools-of-the-trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (viscous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.
The depth-integrated shallow water equations are frequently used for simulating geophysical flows, such as storm-surges, tsunamis and river flooding. In this paper a parallel shallow water solver using an unstructured high-order discontinuous Galerkin method is presented. The spatial discretization of the model is based on the Nektar++ spectral/hp library and the model is numerically shown to exhibit the expected exponential convergence. The parallelism of the model has been achieved within the Cactus Framework. The model has so far been executed successfully on up to 128 cores and it is shown that both weak and strong scaling are largely independent of the spatial order of the scheme. Results are also presented for the wave flume interaction with five upright cylinders.
Computational fluid dynamics (CFD) is becoming an increasingly popular tool in the wave energy sector, and over the last five years we have seen many studies using CFD. While the focus of the CFD studies have been on the validation phase, comparing numerically obtained results against experimental tests, the uncertainties associated with the numerical solution has so far been more or less overlooked. There is a need to increase the reliability of the numerical solutions in order to perform simulation based optimization at early stages of development. In this paper we introduce a well-established verification and validation (V&V) technique. We focus on the solution verification stage and how to estimate spatial discretization errors for simulations where no exact solutions are available. The technique is applied to the cases of a 2D heaving box and a 3D moored cylinder. The uncertainties are typically acceptable with a few percent for the 2D box, while the 3D cylinder case show double digit uncertainties. The uncertainties are discussed with regard to physical features of the flow and numerical techniques.
Low-speed marine diesel engines are mostly operated on heavy fuel oils, which have a high content of sulfur and ash, including trace amounts of vanadium, nickel, and aluminum. In particular, vanadium oxides could catalyze in-cylinder oxidation of SO2 to SO3, promoting the formation of sulfuric acid and enhancing problems of corrosion. In the present work, the kinetics of the catalyzed oxidation was studied in a fixed-bed reactor at atmospheric pressure. Vanadium oxide nanoparticles were synthesized by spray flame pyrolysis, i.e., by a mechanism similar to the mechanism leading to the formation of the catalytic species within the engine. Experiments with different particle compositions (vanadium/sodium ratio) and temperatures (300–800 °C) show that both the temperature and sodium content have a major impact on the oxidation rate. Kinetic parameters for the catalyzed reaction are determined, and the proposed kinetic model fits well with the experimental data. The impact of the catalytic reaction is studied with a phenomenological zero-dimensional (0D) engine model, where fuel oxidation and SOx formation is modeled with a comprehensive gas-phase reaction mechanism. Results indicate that the oxidation of SO2 to SO3 in the cylinder is dominated by gas-phase reactions and that the vanadium-catalyzed reaction is at most a very minor pathway.
Corrosive wear of cylinder liners in large two-stroke marine diesel engines that burn heavy fuel oil containing sulfur is coupled to the formation of gaseous sulfur trioxide (SO3) and subsequent combined condensation of sulfuric acid (H2SO4) and water (H2O) vapor. The present work seeks to address how fuel sulfur content, charge air humidity and liner temperature variations affects the deposition of water and sulfuric acid at low load operation. A phenomenological engine model is applied to simulate the formation of cylinder/bulk gas combustion products and dew points comply with H2O–H2SO4 vapor liquid equilibrium. By assuming homogenous cylinder gas mixtures condensation is modeled using a convective heat and mass transfer analogy combined with realistic liner temperature profiles. Condensation of water is significantly altered by the liner temperature and charge air humidity while sulfuric acid condensation (the order is a few mg per cylinder every cycle) is proportional to the fuel sulfur content. Condensation takes place primarily in the upper part of the cylinder liner where a reduction of the surface temperature or saturated charge air provides that the deposited acid can be highly diluted with water.
A major challenge in next-generation industrial applications is to improve numerical analysis by quantifying uncertainties in predictions. In this work we present a formulation of a fully nonlinear and dispersive potential flow water wave model with random inputs for the probabilistic description of the evolution of waves. The model is analyzed using random sampling techniques and nonintrusive methods based on generalized polynomial chaos (PC). These methods allow us to accurately and efficiently estimate the probability distribution of the solution and require only the computation of the solution at different points in the parameter space, allowing for the reuse of existing simulation software. The choice of the applied methods is driven by the number of uncertain input parameters and by the fact that finding the solution of the considered model is computationally intensive. We revisit experimental benchmarks often used for validation of deterministic water wave models. Based on numerical experiments and assumed uncertainties in boundary data, our analysis reveals that some of the known discrepancies from deterministic simulation in comparison with experimental measurements could be partially explained by the variability in the model input. Finally, we present a synthetic experiment studying the variance-based sensitivity of the wave load on an offshore structure to a number of input uncertainties. In the numerical examples presented the PC methods exhibit fast convergence, suggesting that the problem is amenable to analysis using such methods.