MoodyMarine is a weakly nonlinear potential flow model for wave-body and mooring simulations with a graphical user interface. In this work we present the extension of the model to deal with constrained multi-body dynamics. By combining different translation and rotation constraints most joints can be modelled. As the constraints are imposed through springs and dampers in the explicit time-stepping algorithm, a slight manual tuning is required to make sure the bodies are constrained properly. Nevertheless, this tuning is shown not to influence the final results. In the paper we compare to existing test cases in literature as well as against experimental data. In all test cases there is a good agreement between the target solutions and MoodyMarine.
The influence of directional spreading of waves is significant for wave-induced loads, wave breaking and nonlinearity of the waves. For physical model testing performed at test facilities such as the Ocean and Coastal Engineering Laboratory at Aalborg University, it is crucial to validate if the test conditions match the target sea states by measurement and analysis of the generated directional wave field. Most of the existing methods assume a double summation sea state to be present which is valid in the prototype. However, waves in the laboratory are usually generated by single summation. The current paper presents a method to analyze short-crested waves generated by the single summation method. Compared to similar methods oblique reflections are considered instead of only in-line reflections. The results show that the method successfully decomposes the incident and reflected wave fields in the time domain. Thus, for example the incident wave height distribution may be obtained. The sensitivity of the new method to additional reflective directions, noise, calibration errors and positional errors of the wave gauges was found small.
In hydraulic model tests, it is common practice to relate the response of the tested structure to the incident wave parameters at the toe. Estimation of the incident wave parameters at the toe is thus an essential part of the analysis of hydraulic model testing. In many cases, the design conditions at the toe are given by waves that are highly nonlinear or even depth limited. Modelling such conditions requires reproducing the prototype foreshore slope in the model. The present paper provide guidelines on the accuracy of a nonlinear reflection separation algorithm when applied to nonlinear waves over sloping foreshores. A simple methodology has been established to estimate the expected errors on the incident wave parameters.
Gathering real-world high-quality data from underwater environments is cost-intensive, as is labeling this data for machine learning. Given this, synthetic data represents a possible solution that delivers ground-truth training data. Nevertheless, rendering and modeling of underwater environments are challenging due to several factors, including attenuation, scattering, and turbidity. The focus of this study is on the creation of a simulated underwater environment constructed for the purposes of simulating marine growth on offshore structures. The main requirement is the creation of renderings of sufficient quality and quantity with respect to the representation of marine-species distribution and intra-class variation, and sufficiently accurate recreation of lighting and turbidity (Jerlov water type) conditions underwater. Underwater rendering has been implemented using Blender, with marine growth from 2D/3D scanned and hand-modelled entities combined with a CAD model of an actual offshore installation. The proposed approach provides for the generation of synthetic images usable for training computer vision models in marine-growth inspection applications as well as other related underwater applications. This has been demonstrated in a case study, wherein the utility of the rendered dataset has been briefly demonstrated in a neural network marine-growth segmentation task. The produced renderings are available as a dataset of 1038 scene renders, using varying poses and randomized representative marine growth; each render includes RGB images, ground-truth segmentation masks, water-free RGB images, and depth information. In future work, the expansion with additional species and objects in other oceanic and coastal environments is envisioned.
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.
Physical wave basin tests with a focus on uncertainty estimation have been conducted on a fixed sphere subjected to wave loads at Aalborg University as part of the effort of the OES Wave Energy Converters Modeling Verification and Validation (formerly, OES Task 10) working group to increase credibility of numerical modeling of WECs.
The present note defines an idealized test case formulated to accurately represent the physical tests in a simple way. The test case consists of a fixed, rigid sphere half submerged in water subjected to regular waves of three different levels of linearity. The objective of the present note is to allow for numerical tests of the idealized test case.
In previous research, there have been more investigations on methanol blended with other fuels such as diesel, biodiesel, gasoline, etc., but fewer investigations on methanol with ignition additives as a mono-fuel. To better understand the methanol mono-fuel combustion characteristics and to further apply them, a combined experimental and simulation study of methanol in a Scania heavy-duty compression ignition (CI) engine was carried out in this work. The experiments consisted of four groups with variable injection timings, variable fraction of ignition additives, variable charge air temperatures, and variable overall excess air ratios/power sweeps. Heat release rate (HRR), cylinder pressure, ignition delay and indicated efficiency were analyzed for each case. The analysis showed that the combustion type was partially premixed combustion (PPC) in some cases and diesel-like combustion in the rest. By observing all cases, the shortest ignition delay was 14.1°, and the longest was 22.8°. The indicated efficiencies were in the range of 0.35 to 0.43. Simulations and validation analyses were performed for all cases by a multi-packets model. The physical and chemical ignition delays were predicted. The physical ignition delays were in the range of 4.25 to 8.10°, and the chemical ignition delays were in the range of 6.66 to 17.1°. The chemical ignition delay was always longer than the physical one. This indicates that chemical ignition delay has to be prioritized to improve the ignition performance of methanol fuel.
A practical estimation methodology of the mean added resistance in irregular waves is shown, and the present paper provides statistical analyses of estimates for ships in actual conditions. The study merges telemetry data of more than 200 in-service container vessels with ocean re-analysis data from ERA5. Theoretical estimates relying on spectral calculations of added resistance are made for both long- and short-crested waves and are based on a combination of a parametric expression for the wave spectrum and a semi-empirical formula for the added resistance transfer function. The theoretical estimates are compared to predictions from an indirect calculation of added resistance relying on shaft power measurements and empirical estimates of the remaining resistance components. Overall, the comparison reveals a bias in bow oblique waves and higher sea states of the spectral estimates as well as the large variance of the empirically derived predictions — particularly in beam-to-following waves. One of the study’s main findings, confirming previous studies but based on a much larger dataset than in earlier similar studies, is that added resistance assessment based on in-service data is complex due to significant associated uncertainties.
Rotor dynamic force coefficients of gas seals strongly depend on the machine operational conditions. These force coefficients influence the overall dynamical response and modal properties of machines, consequently defining the machine vibration levels. Accurate estimations of the rotor dynamic coefficients are required for designing machines with low vibration amplifications and well-defined stability margins throughout the operational range. Experimental methods applied to test benches are used to validate such force coefficients and they normally rely on (i) the quality of the measurements and (ii) the assumption that the mathematical model is able to capture the whole system dynamics. If relevant dynamical contributions in a system are neglected by the mathematical model, the contribution will erroneously be concluded to originate from the seal being tested. The theoretical and experimental investigation in this paper focuses on quantifying and qualifying the effect of neglected system dynamics modelling on the estimation of seals force coefficients and stability margins. The in-situ identification of seal forces shows that the direct stiffness, cross-coupling stiffness, and direct damping coefficient estimations for a gas seal with high preswirl are statistically significantly affected by the baseline model. Nevertheless, the baseline model leads to small deviations of the seal force coefficient estimations. The prediction accuracy of stability margins is found to be more influenced by the baseline model describing the system dynamics than by the deviations between the seal force coefficient estimations.