Theoretical and numerical methods for predicting ship-wave impact generated sea spray

Spray generated by ships traveling in cold oceans often leads to topside ice accretion, which can be dangerous to vessels. To develop a full methodology of goal based design for ice accretion there are two critical knowledge gaps, both of which are complex to close, and require new methods and techniques. One is a comparison of ice accretion rates for different structures in the same icing conditions. The second knowledge gap is validation data that compares predicted ice growth rates for all types of ship and offshore structures against observed values. Estimation of the spray flux is a first step in predicting icing accumulation. The amount of spray water, the duration of exposure to the spray, and the frequency at which the spray is generated are all important parameters in estimating the spray flux. Most existing spray flux formulae are based on field observations from small fishing vessels. They consider meteorological and oceanographic parameters but neglect the vessel behavior. Ship heave and pitch motions, together with ship speed and heading relative to the waves, determine the frequency of spray events. Thus the existing formulae are not generally applicable to different sizes and types of vessels. The current study develops simple methods to quantify spray properties in terms that can be applied to vessels of any size or type, which consequently addresses the first knowledge gap. Formulae to estimate water content and spray duration are derived based on principles of energy conservation and dimensional analysis. To estimate spray frequency considering ship motions, a theoretical model is proposed. The model inputs are restricted to ship’s principal particulars, operating conditions, and environmental conditions. Wave-induced motions are estimated using semi-empirical analytical expressions. A novel spray threshold is developed to separate deck wetness frequency from spray frequency. Spray flux estimates are validated against full-scale field measurements available in the open literature and reasonable agreement was obtained. The complex interaction between the structure and a multi-phase fluid, including spray are not fully understood. Limitations of field measurements and model experiments encourage the use of numerical simulation to understand the formation of such spray. In this study, full-scale simulation models of wave-generated sea spray are also developed by implementing a smooth particle hydrodynamics (SPH) method. A three-dimensional (3D) numerical wave tank equipped with a flap-type wave maker and a wave absorber is created to produce regular waves of various heights and steepness. A full-scale medium-size fishing vessel (MFV) is modeled to impact waves in head sea conditions at various forward speeds. Moving ship dynamics with three degree-of-freedom (3-DOF) in waves are resolved instead of mimicking a relative ship speed. The resultant spray water amount is measured using a numerical collection box and compared against field measurements and the theoretical model, where a reasonable agreement is found. The model is able to distinguish between green water and spray water. A multi-phase two-dimensional (2D) simulation is also performed that demonstrates the role of wind in the fragmentation of water sheets into droplets and their distributions over the deck. The simulation results indicate energy released from a surging ship significantly contributes to the generation of spray. An investigation was also performed to explore means to speed up the computationally intensive SPH simulations. A comparison with a traditional CPU (central processing unit) clusters with GPU (graphics processing unit) was performed where GPUs demonstrated faster executions. All the SPH simulations were run on GPUs

Lightning Modeling and Its Effects on Electric Infrastructures

When it comes to dealing with high voltages or issues of high electric currents, infrastructure security and people’s safety are of paramount importance. These kinds of phenomena have dangerous consequences, therefore studies concerning the effects of lightning are crucial. The normal operation of transmission and distribution systems is greatly affected by lightning, which is one of the major causes of power interruptions: direct or nearby indirect strikes can cause flashovers in overhead transmission and distribution lines, resulting in over voltages on the line conductors. Contributions to this Special Issue have mainly focused on modelling lightning activity, investigating physical causes, and discussing and testing mathematical models for the electromagnetic fields associated with lighting phenomena. In this framework, two main topics have emerged: 1) the interaction between lightning phenomena and electrical infrastructures, such as wind turbines and overhead lines; and 2) the computation of lightning electromagnetic fields in the case of particular configuration, considering a negatively charged artificial thunderstorm or considering a complex terrain with arbitrary topograph

Computational imaging and automated identification for aqueous environments

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008

Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics

Deep-learning (DL) methods for the fast inference of the temporal evolution of fluid-dynamics systems, based on the previous recognition of features underlying large sets of fluid-dynamics data, have been studied. Specifically, models based on convolution neural networks (CNNs) and graph neural networks (GNNs) were proposed and discussed. A U-Net, a popular fully-convolutional architecture, was trained to infer wave dynamics on liquid surfaces surrounded by walls, given as input the system state at previous time-points. A term for penalising the error of the spatial derivatives was added to the loss function, which resulted in a suppression of spurious oscillations and a more accurate location and length of the predicted wavefronts. This model proved to accurately generalise to complex wall geometries not seen during training. As opposed to the image data-structures processed by CNNs, graphs offer higher freedom on how data is organised and processed. This motivated the use of graphs to represent the state of fluid-dynamic systems discretised by unstructured sets of nodes, and GNNs to process such graphs. Graphs have enabled more accurate representations of curvilinear geometries and higher resolution placement exclusively in areas where physics is more challenging to resolve. Two novel GNN architectures were designed for fluid-dynamics inference: the MuS-GNN, a multi-scale GNN, and the REMuS-GNN, a rotation-equivariant multi-scale GNN. Both architectures work by repeatedly passing messages from each node to its nearest nodes in the graph. Additionally, lower-resolutions graphs, with a reduced number of nodes, are defined from the original graph, and messages are also passed from finer to coarser graphs and vice-versa. The low-resolution graphs allowed for efficiently capturing physics encompassing a range of lengthscales. Advection and fluid flow, modelled by the incompressible Navier-Stokes equations, were the two types of problems used to assess the proposed GNNs. Whereas a single-scale GNN was sufficient to achieve high generalisation accuracy in advection simulations, flow simulation highly benefited from an increasing number of low-resolution graphs. The generalisation and long-term accuracy of these simulations were further improved by the REMuS-GNN architecture, which processes the system state independently of the orientation of the coordinate system thanks to a rotation-invariant representation and carefully designed components. To the best of the author’s knowledge, the REMuS-GNN architecture was the first rotation-equivariant and multi-scale GNN. The simulations were accelerated between one (in a CPU) and three (in a GPU) orders of magnitude with respect to a CPU-based numerical solver. Additionally, the parallelisation of multi-scale GNNs resulted in a close-to-linear speedup with the number of CPU cores or GPUs.Open Acces