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We consider the statistics of extreme ship motions in a nonlinear irregular wave field. While an accurate computation is possible by using a full Monte-Carlo method to cover all individual wave conditions, the computational cost may become prohibitively high (when coupled with high-fidelity simulations) due to the rareness of the extreme events. In this work, following existing methods of sequential sampling and wave group parameterization, we implement a framework incorporating nonlinear wave simulation and ship response CFD simulation, which allows the extreme ship motion statistics in nonlinear wave field to be computed efficiently. We test the validity of the framework for the cases of ship response calculated by a nonlinear roll equation, and show the importance of wave nonlinearity to the extreme response statistics. Finally, the framework is coupled with the CFD model to demonstrate its applicability to more realistic and general ship motion problems.
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A stochastic approach is implemented to address the problem of a marine structure exposed to water wave impacts. The focus is on (i) the average frequency of wave impacts, and (ii) the related probability distribution of impact kinematic variables. The wave field is assumed to be Gaussian. The seakeeping motions of the considered body are taken into account in the analysis. The coupling of the stochastic model with a water entry model is demonstrated through the case study of a foil exposed to wave impacts.
Empirical data reveals a broad variety of hull shapes among the different ship categories. We present a minimal theoretical approach to address the problem of ship hull optimisation. We show that optimal hull aspect ratios result -- at given load and propulsive power -- from a subtle balance between wave drag, pressure drag and skin friction. Slender hulls are more favourable in terms of wave drag and pressure drag, while bulky hulls have a smaller wetted surface for a given immersed volume, by that reducing skin friction. We confront our theoretical results to real data and discuss discrepancies in the light of hull designer constraints, such as stability or manoeuvrability.
The prediction capability of recurrent-type neural networks is investigated for real-time short-term prediction (nowcasting) of ship motions in high sea state. Specifically, the performance of recurrent neural networks, long-short term memory, and gated recurrent units models are assessed and compared using a data set coming from computational fluid dynamics simulations of a self-propelled destroyer-type vessel in stern-quartering sea state 7. Time series of incident wave, ship motions, rudder angle, as well as immersion probes, are used as variables for a nowcasting problem. The objective is to obtain about 20 s ahead prediction. Overall, the three methods provide promising and comparable results.
We present direct numerical simulations of turbulent channel flow with passive Lagrangian polymers. To understand the polymer behavior we investigate the behavior of infinitesimal line elements and calculate the probability distribution function (PDF) of finite-time Lyapunov exponents and from them the corresponding Cramers function for the channel flow. We study the statistics of polymer elongation for both the Oldroyd-B model (for Weissenberg number $Wi <1$) and the FENE model. We use the location of the minima of the Cramers function to define the Weissenberg number precisely such that we observe coil-stretch transition at $Wiapprox1$. We find agreement with earlier analytical predictions for PDF of polymer extensions made by Balkovsky, Fouxon and Lebedev [Phys. Rev. Lett., 84, 4765 (2000).] for linear polymers (Oldroyd-B model) with $Wi<1$ and by Chertkov [Phys. Rev. Lett., 84, 4761 (2000).] for nonlinear FENE-P model of polymers. For $Wi>1$ (FENE model) the polymer are significantly more stretched near the wall than at the center of the channel where the flow is closer to homogenous isotropic turbulence. Furthermore near the wall the polymers show a strong tendency to orient along the stream-wise direction of the flow but near the centerline the statistics of orientation of the polymers is consistent with analogous results obtained recently in homogeneous and isotropic flows.
Practically all flows are turbulent in nature and contain some kind of irregularly-shaped particles, e.g. dirt, pollen, or life forms such as bacteria or insects. The effect of the particles on such flows and vice-versa are highly non-trivial and are not completely understood, particularly when the particles are finite-sized. Here we report an experimental study of millimetric fibers in a strongly sheared turbulent flow. We find that the fibers show a preferred orientation of $-0.38pi pm 0.05pi$ ($-68 pm 9^circ$) with respect to the mean flow direction in high-Reynolds number Taylor-Couette turbulence, for all studied Reynolds numbers, fiber concentrations, and locations. Despite the finite-size of the anisotropic particles, we can explain the preferential alignment by using Jeffereys equation, which provides evidence of the benefit of a simplified point-particle approach. Furthermore, the fiber angular velocity is strongly intermittent, again indicative of point-particle-like behavior in turbulence. Thus large anisotropic particles still can retain signatures of the local flow despite classical spatial and temporal filtering effects.
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