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.
Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.
Design waves have been used in the past for the probabilistic assessment of wave-induced loads and responses of offshore structures. Various response-conditioning techniques have been employed to determine suitable wave episodes, typically based on linear response transfer functions. Nevertheless, extreme events are not always driven by linear phenomena but can be triggered by near-resonant effects, as in the case of the slow-drift motions of moored floating bodies. Limited research has been devoted to addressing this class of responses using response-conditioned waves (RCW). This paper presents a new approach for deriving RCWs that accounts for combined wave- and low-frequency responses. Both the response amplitude operator (RAO) and the quadratic transfer function (QTF) are employed in an iterative response-conditioning procedure. That permits the identification of appropriate short-duration wave episodes that excite resonant slow-drift motions. These wave episodes are then used in a two-step multi-fidelity design wave methodology for the probabilistic evaluation of the fully nonlinear extreme responses. The proposed approach is validated experimentally for predicting the surge excursions of a moored container ship, and good agreement is found against Monte Carlo results in irregular waves.
What Is the Issue?
Sustaining long-term growth requires marine suppliers to define their pricing strategies in a holistic fashion. However, pricing is an under-managed activity in many companies. Especially when moving towards servitization, services or integrated solutions are frequently underpriced or promised at performance levels that cannot be delivered profitably.
Why Is It Important?
Pricing is one of the most important elements for all business and everything in the business works to justify the input value for a price and turn it into a profit. It therefore has a dramatic but frequently underappreciated effort on achieving profitability and keeping business thriving.
What Can Be Done?
The marine supplies industry needs radical change in pricing by thinking about customer’s needs and aligning the incentives between suppliers and customers for long-term relationship. Value-based pricing is the way forward. An intensive discussion has been made with regard to the key challenges of applying value-based pricing in the marine supplies industry. Understanding these challenges is crucial for a move towards value-based pricing and will shed light on how to tackle these challenges.
In recent years, shipboard microgrids (MGs) have become more flexible, efficient, and reliable. The next generations of future shipboards are required to be equipped with more focuses on energy storage systems to provide all-electric shipboards. Therefore, the shipboards must be very reliable to ensure the operation of all parts of the system. A reliable shipboard MG should be pro-tected from system faults through protection selectivity to minimize the impact of faults and facili-tate detection and location of faulty zones with the highest accuracy and speed. It is necessary to have an across-the-board overview of the protection systems in DC shipboards. This paper provides a comprehensive review of the issues and challenges faced in the protection of shipboard MGs. Furthermore, given the different types of components utilized in shipboard MGs, the fault behavior analysis of these components is provided to highlight the requirements for their protection. The protection system of DC shipboards is divided into three sub-systems, namely, fault detection, lo-cation, and isolation. Therefore, a comprehensive comparison of different existing fault detection, location, and isolation schemes, from traditional to modern techniques, on shipboard MGs is presented to highlight the advantages and disadvantages of each scheme.
To comply with regulations stated by the United Nation's International Maritime Organization, ballast water discharged by ships must be treated to avoid the spread of invasive organisms including algae. In this study, Raman spectroscopy and multivariate data analysis was used to make a Partial Least Squares Discriminant Analysis (PLS-DA) classification model for discrimination between viable (potential invasive) and UV exposed non-viable organisms. UV exposure is commonly used as a ballast water treatment strategy and a UV based exposure method was developed such that non-viable (and dying) algae consistently could be obtained. Raman spectra from both viable and UV treated algae of Rhodomonas salina and Tetraselmis suecica were measured. A PLS-DA model was obtained to form the normalized dataset, and Cross-Validated using Venetian blinds. Based on their individual Raman spectra, it was possible to obtain 100 % discrimination between the two algal species. The model classified 92 and 91 % of the viable algae correctly for R. salina and T. suecica, respectively, as opposed to 82 and 94 % for non-viable algae. In conclusion, in this proof of concept study, Raman spectroscopy was found to have a potential for algae species identification as well as discrimination between viable and non-viable algae.
The current offshore oil & gas multi-phase production and transportation installations have big challenges related to the slugging flow: An unstable multi-phase flow regime where the flow rates, pressures and temperatures oscillate in the considered processes. Slug can be caused by different operating conditions and installation structures. The most severe slugs are often induced in long vertical risers or production wells, where liquid blocks gas at the riser/well base and correspondingly it causes the pressure to accumulate and hence originates the oscillating performance. There are many severe consequences to the production processes because of the slugging flow. This paper reviews some observed latest status and key challenges about slug detection, dynamical modeling and elimination of slugging flows. Mathematical modeling of slug has been used to investigate the slug mechanism and anti-slug control. Most of available models are based on mass-balance formulations, which often require sufficient data for reliable parameter tuning/identification. Slug elimination and control have been investigated for many years and there exist many solutions to eliminate the slug, but some of these methods can simultaneously reduce the oil & gas production, which is a very big concern as the production rate is the key evaluation parameter for offshore production. We conclude that the slugging flow is a well-defined phenomenon, even though this subject has been extensively investigated in the past decades, the cost-effective and optimal slug modeling and control are still open topics with many related challenges.
Fouling release coatings (FRCs) can become damaged and diminished over exposure. Quantifying adverse effect of scratches on FRCs is crucial for damage control. This study investigated the effect of four pre-defined scratches on the re-fouling of a silicone-based FRC (SiFR) undergoing underwater cleaning utilizing a novel automated underwater cleaning system (AUCS). Moreover, barnacle adhesion and coating detachment formation of scratched SiFR were evaluated. Field testing at the CoaST Maritime Test Centre (CMTC) demonstrated that the scratches varying in depths and widths can significantly affect the biofouling behavior and cleaning efficiency of SiFR surface. For wide scratches (i.e. 3-mm-wide), hard fouling (e.g. barnacles, mussels) was more prone to accumulate, and underwater cleaning was effective in preventing hard fouling but not soft fouling on SiFR surface. Additionally, the re-fouling and cleaning difficulty of hard fouling increased with the depth of wide scratches. For narrow scratches (i.e. <50-μm-wide), SiFR was primarily attached by soft fouling (e.g. biofilm, algae), and underwater cleaning performed positive fouling resistance of algae but not biofilm on SiFR surface. Besides, algae became difficult to remove with the depth of narrow scratches. Notably, biweekly cleaning proved to be highly effective in biofouling control of SiFR with narrow and shallow scratches.
There are many uncertainties associated with the estimation of extreme loads acting on a wave energy converter (WEC). In this study we perform a sensitivity analysis of extreme loads acting on the Uppsala University (UU) WEC concept. The UU WEC consists of a bottom-mounted linear generator that is connected to a surface buoy with a taut mooring line. The maximum stroke length of the linear generator is enforced by end-stop springs. Initially, a Variation Mode and Effect Analysis (VMEA) was carried out in order to identify the largest input uncertainties. The system was then modeled in the time-domain solver WEC-SIM coupled to the dynamic mooring solver Moody. A sensitivity analysis was made by generating a surrogate model based on polynomial chaos expansions, which rapidly evaluates the maximum loads on the mooring line and the end-stops. The sensitivities are ranked using the Sobol index method. We investigated two sea states using equivalent regular waves (ERW) and irregular wave (IRW) trains. We found that the ERW approach significantly underestimates the maximum loads. Interestingly, the ERW predicted wave height and period as the most important parameters for the maximum mooring tension, whereas the tension in IRW was most sensitive to the drag coefficient of the surface buoy. The end-stop loads were most sensitive to the PTO damping coefficient.
We present a Spectral Element Fully Nonlinear Potential Flow (FNPF-SEM) model developed for the simulation of wave-body interactions between nonlinear free surface waves and impermeable structures. The solver is accelerated using an iterative p-multigrid algorithm. Two cases are considered: (i) a surface piercing box forced into vertical motion creating radiated waves and (ii) a rectangular box released above its equilibrium resulting in freely decaying heave motion. The FNPF-SEM model is validated by comparing the computed hydrodynamic forces against those obtained by a Navier-Stokes solver. Although not perfect agreement is observed the results are promising, a significant speedup due to the iterative algorithm is however seen.