Control of the combustion process and emission formation in marine gas engines

A smooth transition to the use of gas engines instead of conventional engines in marine shipping is a logical pathway for compliance with tightening environmental regulations. Currently, five major gas engine concepts are applied in maritime sector. In this paper, a review of the marine gas engine concepts was performed with a focus on the control of combustion and emission. To assess all the contributors to combustion and the emission formation process, three main factors were outlined: design, operational parameters and fuel. The assessment of gas engines was conducted based on these factors. The present paper helps to provide an understanding of the current progress in the development of marine gas engines towards improving of combustion efficiency and reducing the emissions. Moreover, the knowledge gaps, particularly in four-stroke marine high-pressure gas engines, were identified.acceptedVersio

Numerical Simulations of Sloshing and the Thermodynamic Response Due to Mixing

In this paper, we apply computational fluid dynamics (CFD) to study the thermodynamic response enhanced by sloshing inside liquefied natural gas (LNG) fuel tanks. An existing numerical solver provided by OpenFOAM is used to simulate sloshing in a model scaled tank of similar form to an LNG fuel tank. The interface area has been estimated for different sloshing regimes on three different numerical grids representing the tank in 3D. Estimating the interface area is done by performing a grid-independence study. In the most severe sloshing conditions, convergence is not achieved. By combining the results from experiments and CFD, it is found that the interface area and the condensation mass flow rate are in phase for the most severe sloshing condition. The existing CFD solver is modified to determine the pressure drop. The simulation results are compared to the experimental data, and the results are acceptable and thereby show a potential in applying CFD to predict the thermodynamic response due to sloshing. By plotting the temperature contours, indications are found that the exchange of cold bulk and saturated liquid due to sloshing has a significant influence on the thermodynamic response

Dynamic modelling of the thermal response enhanced by sloshing in marine LNG fuel tanks

This paper investigates the thermal response in marine liquefied natural gas (LNG) fuel tanks by experiments and modelling. The aim of this work is to develop phenomenological models that can predict the rapid pressure loss experienced onboard LNG fuelled vessels. Experiments have been conducted using water. A horizontally aligned tank made of steel has the same geometry as a LNG fuel tank, but at model scale. The tests are performed by supplying heat to evaporate water that is led through a closed loop from the bottom to the top passing a heating element. A lumped dynamic model is developed that can be tuned by adjusting few parameters. Uniform conditions are assumed in each phase. The model can provide useful insight and be combined with other submodels to perform system simulations. Good correspondence with the experimental data is found after tuning heat transfer coefficients, air content in the gas and the average temperatures. After validating the model, it is used to predict the necessary heat supplied to the pressure build-up unit (PBU) to maintain the tank pressure. A simple relation between the measured pressure and the PBU heat capacity is presented

Validation of Data-Driven Labeling Approaches Using a Novel Deep Network Structure for Remaining Useful Life Predictions

Today, most research studies that aim to predict the remaining useful life (RUL) of industrial components based on deep learning techniques are using piecewise linear (PwL) run-to-failure targets to model the degradation process. However, this PwL degradation model assumes a constant initial RUL value in which only time is needed to model normal operating conditions. Thus, it ignores the entire diagnostics aspect. To provide high and reliable RUL prediction accuracy, a prognostics algorithm must incorporate diagnostics information. This paper will provide the Prognostics and Health Management Community an empirical study that validates the PwL degradation model against other, more recent data-driven labeling approaches. We compare three different data-driven labeling approaches for RUL predictions. First, an unsupervised reconstruction-based fault detection algorithm is used to provide valuable diagnostics information. Then, optimized initial RUL values are calculated based on this information. Finally, these values are used to construct PwL, descriptive statistics, and anomaly score function run-to-failure targets for subset FD001 in the popular and publicly available C-MAPSS data set. A deep network structure is proposed and trained on the three different run-to-failure targets in order to predict the RUL. During the training process, a genetic algorithm approach is used to tune a selected search space of hyper-parameters. The results suggest that the network trained on PwL run-to-failure targets with the optimized initial RUL values performs the best and provides the most reliable RUL prediction accuracy. This network also outperforms the most robust results in the literature

Experimental and Numerical Investigation of Sloshing under Roll Excitation at Shallow Liquid Depths

This paper investigates sloshing at shallow-liquid depths in a rectangular container by using experimental and numerical methods. A motion platform is used to perform a prescribed periodic rotational motion to excite the liquid sloshing at a range of frequencies and filling levels. Simulated free-surface elevation is compared with the experimental results for a selection of cases. The wave mechanisms at the chosen fillings are studied by combining numerical methods and the experimental results. We find that the simulated free-surface elevation is in close agreement with experimental results inside the resonance zone. But at frequencies above the bifurcation point, with several overlapping waves, the deviation is increasing. The bifurcation point is determined for a range of filling levels through observation. The numerical results provide important information about sloshing mechanisms at these depths. Complex interaction between the bottom, the lower layer and the wave influences the amount of dissipation before the wave hits the wall. The existing theory seems to be too conservative in predicting the occurrence of hydraulic jumps in the upper limit

Virtual Prototyping of Maritime Systems and Operations: Applications of Distributed Co-Simulations

In this work, we demonstrate the use of co-simulation technology in the maritime industry through four relevant examples of applications based on the outcome of the knowledge-building project Virtual Prototyping of Maritime Systems and Operations (ViProMa). Increasing computational capabilities opens for extended use of simulators in the design processes. Even complex systems can now be analyzed at an early stage of the design process and even in real time using distributed simulation technology. We conduct an assessment of the need for co-simulation technology in the industry, present a short background in co-simulation technology, and provide a short summary of the major findings and deliverables in the ViProMa project (http://viproma.no). The four case studies presented in this work pinpoint different advantages of using co-simulations in the industry, such as combining different modeling and simulation tools, improving collaboration without revealing sensitive information by using black-box models, testing conceptual designs in a fast and consistent manner before initiating building processes, and verifying the interplay between hardware and software in the simulation environment in hardware in the loop (HIL) tests. All the case studies are simulated using the open source co-simulation software Coral developed in the project, using the Functional Mock-up Interface (FMI) standard, and the co-simulation software can be downloaded from the project’s web site.Norges forskningsrådsubmittedVersio

A Comprehensive Survey of Prognostics and Health Management based on Deep Learning for Autonomous Ships

The maritime industry widely expects to have autonomous and semiautonomous ships (autoships) in the near future. In order to operate and maintain complex and integrated systems in a safe, efficient, and cost-beneficial manner, autoships will require intelligent Prognostics and Health Management (PHM) systems. Deep learning (DL) is a potential area for this development, as it is rapidly finding applications in a variety of domains, including self-driving cars, smartphones, vision systems, and more recently in PHM applications. This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems. The purpose is to support creativity and provide inspiration toward the PHM based on DL in autoships and the maritime industry. This paper discusses benefits, challenges, suggestions, existing problems, and future research opportunities with respect to this significant new technology

Integrated multi-domain system modelling and simulation for offshore crane operations

Advanced offshore machinery, such as an offshore crane, usually involves several energy domains. Modelling and simulation of multi-domain systems is challenging because of not only the complexity in modelling of the related sub-systems but also the interfacing of these sub-models in an integrated model and the performance of simulating, especially when real-time simulation is required. This paper introduces a modelling approach for offshore crane operations based on the bond graph (BG) method. Specifically, the integrated model includes mechanical properties, hydraulic actuators and control algorithms. For the purpose of testing, particularly of advanced control algorithms, it is necessary and crucial to include the response of physical systems. In this paper, a flexible control algorithm for offshore crane operation, including functions of heave compensation and load anti-sway, was implemented

Hydrogen as a Maritime Fuel–Can Experiences with LNG Be Transferred to Hydrogen Systems?

As the use of fossil fuels becomes more and more restricted there is a need for alternative fuels also at sea. For short sea distance travel purposes, batteries may be a solution. However, for longer distances, when there is no possibility of recharging at sea, batteries do not have sufficient capacity yet. Several projects have demonstrated the use of compressed hydrogen (CH2) as a fuel for road transport. The experience with hydrogen as a maritime fuel is very limited. In this paper, the similarities and differences between liquefied hydrogen (LH2) and liquefied natural gas (LNG) as a maritime fuel will be discussed based on literature data of their properties and our system knowledge. The advantages and disadvantages of the two fuels will be examined with respect to use as a maritime fuel. Our objective is to discuss if and how hydrogen could replace fossil fuels on long distance sea voyages. Due to the low temperature of LH2 and wide flammability range in air these systems have more challenges related to storage and processing onboard than LNG. These factors result in higher investment costs. All this may also imply challenges for the LH2 supply chain

An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components

In recent years, the reliability and safety requirements of ship systems have increased drastically. This has prompted a paradigm shift toward the development of prognostics and health management (PHM) approaches for these systems' critical maritime components. In light of harsh environmental conditions with varying operational loads, and a lack of fault labels in the maritime industry generally, any PHM solution for maritime components should include independent and intelligent fault detection algorithms that can report faults automatically. In this paper, we propose an unsupervised reconstruction-based fault detection algorithm for maritime components. The advantages of the proposed algorithm are verified on five different data sets of real operational run-to-failure data provided by a highly regarded industrial company. Each data set is subject to a fault at an unknown time step. In addition, different magnitudes of random white Gaussian noise are applied to each data set in order to create several real-life situations. The results suggest that the algorithm is highly suitable to be included as part of a pure data-driven diagnostics approach in future end-to-end PHM system solutions