This paper presents ISOPE's 2020 comparative study on the interaction between focused waves and a fixed cylinder. The paper discusses the qualitative and quantitative comparisons between 20 different numerical solvers from various universities across the world for a fixed cylinder. The moving cylinder cases are reported in a companion paper as part B (Agarwal, Saincher, et al., 2021). The numerical solvers presented in this paper are the recent state of the art in the field, mostly developed in-house by various academic institutes. The majority of the participants used hybrid modeling (ie, a combination of potential flow and Navier–Stokes solvers). The qualitative comparisons based on the wave probe and pressure probe time histories and spectral components between laminar, turbulent, and potential flow solvers are presented in this paper. Furthermore, the quantitative error analyzes based on the overall relative error in peak and phase shifts in the wave probe and pressure probe of all the 20 different solvers are reported. The quantitative errors with respect to different spectral component energy levels (ie, in primary, sub-, and superharmonic regions) capturing capability are reported. Thus, the paper discusses the maximum, minimum, and median relative errors present in recent solvers as regards application to industrial problems rather than attempting to find the best solver. Furthermore, recommendations are drawn based on the analysis.
The wave loads and the resulting motions of floating wave energy converters are traditionally computed using linear radiation–diffraction methods. Yet for certain cases such as survival conditions, phase control and wave energy converters operating in the resonance region, more complete mathematical models such as computational fluid dynamics are preferred and over the last 5 years, computational fluid dynamics has become more frequently used in the wave energy field. However, rigorous estimation of numerical errors, convergence rates and uncertainties associated with computational fluid dynamics simulations have largely been overlooked in the wave energy sector. In this article, we apply formal verification and validation techniques to computational fluid dynamics simulations of a passively controlled point absorber.
The phase control causes the motion response to be highly nonlinear even for almost linear incident waves. First, we show that the computational fluid dynamics simulations have acceptable agreement to experimental data. We then present a verification and validation study focusing on the solution verification covering spatial and temporal discretization, iterative and domain modelling errors. It is shown that the dominating source of errors is, as expected, the spatial discretization, but temporal and iterative errors cannot be neglected. Using hexahedral cells with low aspect ratio and 30 cells per wave height, we obtain results with less than 5% uncertainty in motion response (except for surge) and restraining forces for the buoy without phase control. The amplified nonlinear response due to phase control caused a large increase in numerical uncertainty, illustrating the difficulty to obtain reliable solutions for highly nonlinear responses, and that much denser meshes are required for such cases.
A two-dimensional (2D) Reynolds-averaged Navier–Stokes (RANS) equations solver with k–ω turbulence closure is developed, employing immersed boundary (IB) technique on Cartesian grids. Generalized wall functions are introduced to enhance computational efficiency for problems with high Reynolds numbers. To address existing challenges in applying wall functions within IB methods, a novel, effective and easy-to-implement strategy is proposed. Another distinguishing feature of this turbulent-flow solver is that it employs the highly accurate immersed-boundary generalized harmonic polynomial cell (IB-GHPC) method to solve the Poisson equation for fluid pressure. The new solver is firstly validated by simulating channel flows on both hydraulically smooth and rough walls, achieving excellent agreement with benchmark experimental and numerical studies for various flow parameters including velocity, turbulent kinetic energy and shear stress. For channel flow simulations, our implementation of generalized wall functions using the proposed strategy results in a remarkable reduction of grid nodes by over 80%. Moreover, the solver is applied to simulate flow around both smooth and rough cylinders, producing promising results for drag, lift, and pressure coefficients. Our analysis demonstrates a robust performance of the developed solver in modeling turbulent flows based on Cartesian grids, offering a substantial improvement in computational efficiency for tackling problems involving large Reynolds numbers.
Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity. In this paper, we propose a novel lightweight framework called HSI-ShipDetectionNet that is based on high-order spatial interactions and is suitable for deployment on resource-limited platforms, such as satellites and unmanned aerial vehicles. HSI-ShipDetectionNet includes a prediction branch specifically for tiny ships and a lightweight hybrid attention block for reduced complexity. Additionally, the use of a high-order spatial interactions module improves advanced feature understanding and modeling ability. Our model is evaluated using the public Kaggle and FAIR1M marine ship detection datasets and compared with multiple state-of-the-art models including small object detection models, lightweight detection models, and ship detection models. The results show that HSI-ShipDetectionNet outperforms the other models in terms of detection performance while being lightweight and suitable for deployment on resource-limited platforms.
This paper presents the design and development of a conceptual prototype of an autonomous self-driven inline inspection robot, called Smart-Spider. The primary objective is to use this type of robot for offshore oil and gas pipeline inspection, especially for those pipelines where the conventional intelligent pigging systems could not or be difficult to be deployed. The Smart-Spider, which is real-time controlled by its own on-board MCU core and power supplied by a hugged-up battery, is expected to execute pipeline inspection in an autonomous manner. A flexible mechanism structure is applied to realize the spider's flexibility to adapt to different diameters of pipelines as well as to handle some irregular situations, such as to pass through an obstructed areas or to maneuver at a corner or junction. This adaptation is automatically controlled by the MCU controller based on pressure sensors' feedback. The equipped devices, such as the selected motors and battery package, as well as the human-and-machine interface are also discussed in detail. Some preliminary laboratory testing results illustrated the feasibility and cost-effectiveness of this design and development in a very promising manner.
Existing energy management strategies (EMSs) for hybrid power systems (HPSs) in hydrogen fuel cell vessels (FCVs) are not applicable to scenarios with multiple hydrogen fuel cells (FCs) and lithium batteries (LBs) in parallel, and are difficult to achieve real-time control and optimization for multiple objectives. In this paper, a bi-layer real-time energy management strategy (BLRT-EMS) is proposed. Compared with existing EMSs, the proposed BLRT-EMS implements different control/optimization objectives distributed in the execution layer EMS (EL-EMS) and the decision layer EMS (DL-EMS), which can significantly reduce bus voltage fluctuations, decrease hydrogen consumptions, improve the system efficiency, and have potential for engineering applications. In the first EL-EMS, a decentralized optimal power allocation strategy is proposed, which allows each FC system to allocate the output power ratio according to their generation costs, ensuring consistent performance of multiple FC systems (MFCS) under long-term operating conditions, and thus delaying the degradation rate of FCs. In the second EL-EMS, a distributed cooperative control strategy is proposed to achieve dynamic SoC equalization, proportional output power allocation, and accurate bus voltage restoration among multiple battery storage systems (MBSS) to extend the service life of batteries. In the DL-EMS, an energy coordination optimization strategy between MFCS and MBSS is proposed to achieve hydrogen consumption reduction and system efficiency improvement, thus enhancing the endurance performance of FCV. Finally, test results based on the StarSim experimental platform show that the proposed BLRT-EMS has faster SoC convergence speed, smaller bus voltage deviation, lower hydrogen consumption, higher system efficiency, and lower operation stress than the state-of-the-art methods.
The present study investigates three-dimensional gap resonance between two fixed side-by-side barges under combined wave and uniform current excitation using a fully nonlinear numerical wave tank based on the Navier–Stokes equations. It examines how currents aligned with regular waves affect the gap response under head and beam seas. In beam seas, the free surface in the gap primarily exhibits a modal-type response in the form of a standing wave. The maximum gap response, consistently occurring at the midpoint of the gap, increases gradually with the current speed. Conversely, in head seas, the maximum response decreases slightly with increasing current speed, and the occurring location shifts downstream. Moreover, resonant free-surface responses along the gap in head seas manifest as propagating waves rather than modal-type standing waves, consisting of a wider spectrum of wave components around the resonant ones and traveling faster than the incident waves regardless of current speed. The wavelengths of those resonant waves tend to increase with increasing current speed. Additionally, the presence of current significantly enlarges the transverse first-harmonic and mean-drift wave forces on the barges under beam-sea conditions. The study highlights the necessity of considering current effects on three-dimensional gap resonances in marine operations at coastal and offshore locations.
From the process control point of view, any reliable and online Oil-in-Water (OiW) measurement could provoke a brand new control paradigm for produced water treatment. However, the real-time OiW monitoring is still an open and ad-hoc situation in recent decades. The fundamental issue, ie, the OiW measurement is methodology dependent, leads to numerous challenges, such as (i) how to verify the reliability and accuracy of a specific methodology/instrument; (ii) how to handle and interpret the measured data in a most objective manner; and (iii) how to keep a cost-effective on-site calibration and maintenance under the harsh offshore conditions etc. The paper reports our latest achievements and observations in usage of fluorescence- and microscopybased OiW monitoring technologies for advanced Produced Water Treatment (PWT) control and evaluation, particularly by focusing on the de-oiling hydrocyclone installations.
Our recent experimental investigations of flexible side-by-side blades under both steady and unsteady flows have observed flutter in both scenarios. Flutter significantly impacts blade kinematics and the hydrodynamic drag experienced by the blades. Our numerical approach [1], utilizing the reactive force model, successfully reproduces flutter phenomena. In contrast, the traditional Morison’s equation fails to trigger flutter. In the static regime where flutter does not occur, the bulk drag coefficients calibrated from experiments in steady and unsteady flows can be unified through an effective Cauchy number, allowing for the use of analytical models developed for steady flows in unsteady flows. In the flutter regime, using the bulk drag coefficient from steady flows underestimates the drag load in oscillatory flow.
Harbour vessel emissions are growing concerns in the maritime industry regarding environmental sustainability. Accurate emissions prediction can stand in monitoring and addressing the issue. This study proposes a machine-learning approach using Artificial Neural Network (ANN) for predicting harbour vessel emissions. The approach shows superiority over the bottom-up method introduced by the 4th IMO GHG Study regarding prediction accuracy. Actual emissions data from onboard measurements are used for training ANN models and as references for evaluating the methods. Compared to the bottom-up method, the improvement in error reduction can be up to 30% for predicting nitrogen oxides and 54% for carbon monoxide when only using ship-related factors as input variables. By adding selected meteorological factors in the experiments, the prediction accuracy enhancement can achieve up to 48% for nitrogen oxides and 62% for carbon monoxide. The proposed ANN approach could assist relevant stakeholders in improving emissions prediction and operations optimisation.