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.
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.
Design
Our data set, collected via surveys from top managers and project managers involved in 86 NPD projects in 85 firms, is analyzed using PLS structural equation modeling.
Purpose
This study examines how technical drivers as well as social drivers influence organic communication and top management involvement (TMI) in new product development (NPD) projects. Technical drivers are strategic importance and product innovativeness and social drivers are intrinsic and extrinsic relevance. Organic communication is defined as continuous, bi-directional, and informal communication between top management and the NPD teams. Further, arguing that TMI must be studied as multi-faceted construct, TMI is conceptualized to occur as guidance, active motivation, providing resources, and creating a tolerant climate. Subsequently, the effect of TMI and organic communication on NPD performance is investigated.
Findings
We show that the strategic importance of the project has a positive influence on TMI through active motivation, providing resources, and creating a tolerant climate for innovation, but does not have an effect on guidance. Results also show that active motivation and organic communication improve budget and schedule adherence, whereas providing guidance and stimulating a tolerant climate have detrimental effects. In summary, our results show that only active motivation enhances all types of performance while stimulating a tolerant climate appears to have the opposite effect. The results revealed that organic communication between top management and the NPD team has a strong positive effect on all elements of TMI (providing guidance, actively motivating the NPD team, providing resources, and creating a tolerant climate). In other words, when top management communicates with the NPD team throughout the project in an informal way and listens to the team in addition to engaging in a one-way communication, they are more likely to be seen by the team as being deeply involved in the project.
Savonius hydrokinetic turbines (SHTs), categorized as emerging cyclic-type wave energy converters (WECs), have demonstrated notable potential in achieving elevated energy conversion efficiency and consistent power output. This performance is particularly observed when operating under the initial phase-locked strategy (IPLS), marking a significant advancement in the realm of wave energy harvesting. However, a thorough exploration of the influences stemming from wave conditions and turbine design remains an area that warrants further investigation for advancing the performance of SHT-WECs under the proper operational strategy. This study undertakes an exhaustive analysis of geometric parameters, encompassing turbine diameter, blade number, and thickness. An experiment-validated numerical model based on the unsteady two-phase Reynolds-averaged Navier-Stokes equations is adopted in the research. Comprehensive investigations include analyzes of flow fields around the turbine, pressure distributions on blade surfaces, and dynamic torque variations. These analyzes serve to elucidate the variation rules of hydrodynamic characteristics and their influential mechanisms. The results highlight the notable impact of the proposed "relative-short wavelength impact" on the performance of SHT-WECs operating under IPLS conditions. Notably, no significant impact is observed when the relative wavelength exceeds 17. Optimal performance is achieved with the thinnest and two-bladed turbine configuration. Moreover, optimizing the turbine diameter significantly enhances SHT-WEC conversion efficiency, with the attained maximum value reaching approximately 18.6%. This study offers a concise guideline for designing turbine diameters in alignment with specific wave conditions.
Autonomous unmanned underwater vehicles (UUVs) play a vital role in diverse underwater operations; localization is of great interest for UUVs mirroring the trend seen in self-driving surface and aerial vehicles. Unlike their land and aerial counterparts, underwater environments lack reliable Global Navigation Satellite Systems (GNSS) due to radio wave attenuation in water. Hence, alternative localization methods are imperative for both navigation and operational purposes. This study thoroughly reviews sensor technologies for underwater localization, including sonar, Doppler velocity log, cameras, and more. Different operations necessitate distinct localization accuracies and vehicle and sensor choices. Environmental factors, such as turbidity, waves, and sound disturbances, impact sensor performance. Conclusions are given on the coincidence between operational requirements and sensor specifications, with special attention to the open concerns. These considerations include aspects such as the line of sight for acoustic positioning systems and the requirement for a feature-rich environment for visual sensors. Lastly, a prediction for the future of underwater localization is given, where the tendencies indicate lower costs for sensors, making operation-specific vehicles more attractive, which aligns with an increased demand for cost-efficient autonomous offshore operations.
This study investigates how the recognition and exploitation of entrepreneurial opportunities influence small business performance via interactions with firm-level innovation capability and learning orientation. We frame the study within the maritime-sector context and seek to contribute to the understanding of how the interplay between opportunity recognition, exploitation, innovation capability and learning orientation affects the entrepreneurial performance of local businesses when there is a technological policy change. The study further frames its arguments from a dynamic capability perspective and tests its arguments with data from 284 local businesses operating in the Port of Tema. Findings reveal that opportunity exploitation and learning orientation as well as their interplay have a positive and significant effect on entrepreneurial performance. The study consequently presents local micro-entrepreneurial reactions to macro-level policy changes within the maritime sector – an issue that has largely remained uninvestigated in the African business literature due to maritime blindness.