Monitoring methods, such as seabed bottom-towed cameras, sediment grabs, and benthic sledges, have limitations in spatial coverage, cause seabed disturbance, are restricted to soft-bottom substrates, and offer low flexibility for marine seabed monitoring. In this study, we investigate the potential of a non-invasive and simple underwater remotely operated vehicle (ROV) to enhance marine seabed monitoring. A tethered ROV equipped with a GoPro camera was deployed in three areas of Skagerrak at depths from 15-34 m to assess accuracy in species identification and substrate classification identified from still frames. The quality of still frames varied between areas due to turbidity, motion blur, and marine snow, which reduced the number of high-quality frames by approximately 20%. Classification of substrates and taxa identification were possible in the remaining still frames. Two different substrates were detected: sand and stone reef. Stone reefs had a lower occurrence compared to sand. A total of 10 taxa were detected in the two substrate types. The highest abundance was observed in the stone reef substrate compared to the sand substrate. Identification at the species level was limited due to the quality of the still frames, which affected the detectability of morphological traits. This study demonstrates that a widely accessible ROV can be used for marine monitoring. The ROV can be used in different substrates, and still frames provide valuable information on species composition, which can enhance the replicability of monitoring programs.
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.
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.
The term ‘innovation ecosystem’ has become popular among stakeholders involved in innovation. The core idea is that innovation does not thrive through isolated actions of individual companies, but rather depends on a broad array of interrelated actors, institutions and policies. In this paper, we apply the concept of innovation ecosystems to ports by first providing a theoretical overview of its components and then comparing the efforts to build such an ecosystem in the port cities of Rotterdam and Valencia. Our main findings are as follows. First, the importance of innovation for the ability of ports to continue to create ‘value for society’ is widely acknowledged. Second, research and development (R&D) activities in both Rotterdam and Valencia are relatively limited and the dominant innovation challenge is the early application of new technologies developed outside the ports industry. Third, a ‘systemic approach’ is required to understand the innovation ecosystem in ports, given the strong interrelations among companies in the port and the need for broad coalitions to implement new technologies. Fourth and fifth, human capital formation and research cooperation, respectively, play a central role in improving the port innovation ecosystem. Finally, the ecosystem in Rotterdam is ‘distributed and connected’ while Valencia is more centralised.
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.
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.
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.
This study exploits service modularity in front-end logistics services in e-fulfillment, from a customer-centric approach, particularly in order management, delivery, and return. Through an online survey of UK customers, the service priorities of 494 respondents via AHP (Analytic Hierarchical Process) were analyzed. Extracting customers' service priorities, ordering behavior, and demographic information as input data, the clustering algorithm KAMILA (KAy-means for MIxed LArge data sets) was further applied. The three identified customer clusters (multichannel shoppers, infrequent shoppers, and online fans) provide preliminary evidence on how commonality and variability aspects of service modularity in front-end logistics services can optimize the number of service options and their performance levels. Therefore, our study, building on value co-creation and modularity, proposes a systematic way of exploiting service modularity for the customer segmentation process that addresses heterogeneous customer preferences cost-efficiently and uncomplicatedly. Furthermore, we provide a framework for the governance of front-end logistics services, guiding outsourcing decisions. Accordingly, it reveals the implications of customer priorities and service decomposition logic choices on value creation. Finally, the propositions formulated aim to develop theoretical foundations for explaining how the heterogeneity in customer priorities for logistics services can be managed with modularity, creating value both for customers and retailers.
This work presents the verification and validation of the freely available simulation tool MoodyMarine, developed to help meet some of the demands for early stage development of MRE devices. MoodyMarine extends the previously released mooring module MoodyCore (Discontinuous Galerkin Finite Elements) with linear radiation-diffraction bodies, integrated pre-processing workflows and a graphical user interface. It is a C++ implementation of finite element mooring dynamics and Cummins equations for floating bodies with weak nonlinear corrections. A newly developed nonlinear Froude-Krylov implementation is verified in the paper, and MoodyMarine is compared to CFD simulations for two complex structures: a slack-moored floating offshore wind turbine and a self-reacting point-absorber with hybrid mooring.