Role of Active Dancers in Tension Controls of Webs

Mechanical and Aerospace Engineerin

Proactive Collision Avoidance for Autonomous Ships: Leveraging Machine Learning to Emulate Situation Awareness

Autonomous ship technology is developing at a rapid pace, with the aim of facilitating safe ship operations. Collision avoidance is one of the most critical tasks that autonomous ships must handle. To support the level of safety associated with collision avoidance, this study suggests to provide autonomous ships with the ability to conduct proactive collision avoidance maneuvers. Proactive collision avoidance entails predicting future encounter situations, such that they can preemptively be avoided. However, any such actions must adhere to relevant navigation rules and regulations. As such, it is suggested to predict encounter situations far in advance, i.e. prior to risk of collision existing. Any actions can, therefore, be conducted prior to the applicability of the COLREGs. As such, simple corrective measures, e.g. minor speed and/or heading alterations, can prevent close encounter situations from arising, reducing the overall risk associated with autonomous ship operations, as well as improving traffic flow. This study suggests to facilitate this ability by emulating the development of situation awareness in ship navigators through machine learning. By leveraging historical AIS data to serve as artificial navigational experience, long-range trajectory predictions can be facilitated in a similar manner those conducted by human navigators, where such predictions provide the basis for proactive collision avoidance actions. The development of human situation awareness is, therefore, presented, and relevant machine learning techniques are discussed to emulate the same mechanisms

Life cycle emission and cost assessment for LNG-retrofitted vessels: the risk and sensitivity analyses under fuel property and load variations

There are various energy efficiency and emission reduction regulations enforced by the national and international maritime authorities for the shipping industry to adopt greener technologies. In this light, LNG-fueled vessels can be a promising alternative for ocean going diesel operated ships. It will be more beneficial if the price of LNG is lower than diesel to make that an economically viable fuel. Otherwise, there are concerns over the emission/economic considerations under the cost-benefit analyses of such fuels during their lifetimes with the initial investment risk for the technology, related infrastructure including fueling facilities and technology retrofitting processes. This study is an attempt to address the respective emission, energy, and cost concerns of LNG as a possible greener fuel with innovative dual-fuel engines within the SeaTech H2020 project (seatech2020. eu) initiative. The fuel life cycle of LNG in two scenarios of fuel property modification and load management for the cost analysis is considered. The life cycle assessment (LCA) section is designed to compare typical diesel and LNG fuels with selected short and deep-sea ship routes. Moreover, it is found that the effect of the ship travel distance on the amount of emissions is not significant when compared with the respective ratio. The life cycle cost assessment (LCCA) indicated that the fuel quality is more influential than the load variations in ship navigation. A 39% GHG emission reduction and up to a 22% fuel efficiency can be achieved under more optimal operational conditions by replacing LNG with diesel. The results also showed that the feasibility of using good quality LNG (higher Wobbe Index) instead of poor diesel characteristics in a selected ship is guaranteed within 30% of the sensitivity range. The fuel consumption variations under different engine loads (50% max to 85% min) can decrease the payback period from 6-years to 4-years as per the LCCA

Energy-Efficient Marine Engine and Dynamic Wing Evaluation Under Laboratory Conditions to Achieve Emission Reduction Targets in Shipping

There is a requirement to comply with the forthcoming IMO & EU requirements to reduce ship emissions by at least 40% in 2030 compared to the 2008 levels. Such medium-term emission reduction targets can only be achieved by introducing novel technologies into the shipping industry. The SeaTech H2020 project (seatech2020.eu) introduces two main innovations that can support the same emission reduction objectives. Those innovations consist of integrating an energy-efficient marine combustion engine with a renewable energy recovery device, i.e. dynamic wing. However, these two technologies are not evaluated in an actual environment in a selected ocean-going vessel. On the other hand, various data sets are collected from both innovations and can be used to quantify their energy efficiencies in a data science environment. Furthermore, it is expected that both innovations should interact with each other in the same data science environment as well as in the respective testing platforms, therefore more realistic vessel operational conditions can be introduced. Hence, this study introduces realistic head wave conditions in both innovations, where the dynamic wing creates adequate thrust to push the vessel forward under the same ocean wave conditions. The same thrust and ocean wave conditions have been applied to marine engine testing as the main contribution of this study. Finally, the data sets collected from the engine testing platform under its loading situations for both wave and thrust conditions of the selected ocean-going vessel are presented in this study

Multiple Model Adaptive Estimation Coupled With Nonlinear Function Approximation and Gaussian Mixture Models for Predicting Fuel Consumption in Marine Engines

Digital twin type models can be developed for physical systems that are complex nonlinear a system of systems (SoS). However, such models are usually difficult to represent by linear equations. Therefore, an adequate linearization technique should be introduced. Therefore, linear models as digital twins can be interpreted easily and need much less computational power when applied to various industrial applications. On the other hand, a linearization approach can increase the respective system-model errors and impose significant constraints on the models of SoS, i.e., since linear models can be applicable only in limited operating regions. This research study aims to combine positive characteristics of both linear and nonlinear modelling into a digital twin development framework by having the properties of linear digital twin models locally while the model framework is covering the whole operating region of the SoS. An industrial application of marine engines as an SoS is considered for this study, where the respective models have been used to predict engine fuel consumption. For this purpose, firstly, a dataset is selected from a marine engine of a selected ocean-going vessel. Then, several localized linear operational regions of the respective data set are identified using an unsupervised data-driven technique, i.e., on the engine propeller combinator diagram. For developing the localized models: firstly, the Gaussian Mixture Models method is used to cluster the data points into different operational regions of the engine propeller combinator diagram. Then, a nonlinear model of the relationship between features is developed in each cluster using the polynomial regression approach. Then, these models are combined using the Multiple Model Adaptive Estimation (MMAE) method to create an overall model for the marine engine as an SoS. The same model is utilized to predict the respective fuel consumption based on engine operational conditions

Data-driven modeling of energy-exergy in marine engines by supervised ANNs based on fuel type and injection angle classification

The application of artificial neural networks with the involvement of a modified homogeneity factor to predict exergetic terms from combustive and/or mixing dynamics in a marine engine is considered in this study. This is a significant step since the mathematical formulation of exergy in combustion is complicated and even unconvincing due to the turbulent and highly nonlinear nature of the combustion process. The computational simulations are carried out on a marine CI (compression ignition) engine and the respective data per different fuel types that are used for thermodynamic exergetic computations as well as energetic simulations. A new parameter namely the modified homogeneity factor derived by an artificial neural network (ANN) is considered for the mixing dynamics, i.e. as an input parameter for the availability and irreversibility predictions. This parameter is based on the standard deviation from an ideal air-fuel mixture formed within the combustion chamber of the marine engine. Furthermore, spray and injection quantities along with the combustion process and its heat transfer parameters are served to predict the exergetic terms for two study cases: (a) fuel type and (b) injection orientation. It is shown that using data analytics that consists of neural networks can provide an adequate approach in diesel engines for improving energy efficiency and reducing emissions

Particle Filter Based Ship State and Parameter Estimation for Vessel Maneuvers

Vessel states and parameters estimation is essential for maneuvering and collision avoidance. This study presents an application of particle filter (PF) algorithm to estimate vessel states and parameters. Particularly, to reduce the impact of the vessel’s underactuated property and complex environmental disturbance, the estimation process contains a kinematic curvilinear motion model that describes vessel’s motion. The estimated result can help navigators or ship onboard computers well comprehend the current vessel maneuvering condition. Besides, it can also serve as the necessary data source for vessel’s future trajectory prediction. Therefore, it can be integrated into vessel’s situation awareness (SA) module that supports safety navigation for both conventional and autonomous vessels

Kinematic motion models based vessel state estimation to support advanced ship predictors

Advanced ship predictors can generally be considered as a vital part of the decision-making process of autonomous ships in the future, where the information on vessel maneuvering behavior can be used as the source of information to estimate current vessel motions and predict future behavior precisely. As a result, the navigation safety of autonomous vessels can be improved. In this paper, vessel maneuvering behavior consists of continuous-time system states of two kinematic motion models—the Curvilinear Motion Model (CMM) and Constant Turn Rate & Acceleration (CTRA) Model. Two state estimation algorithms—the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are implemented on these two models with certain modifications so that they can be compatible with discrete-time measurements. Four scenarios, created by combining different models and algorithms, are implemented using simulated ship maneuvering data from a bridge simulator. These scenarios are then verified through the proposed stability and consistency tests. The simulation results show that the EKF tends to be unstable combined with the CMM. The estimates from the other three scenarios can generally be considered more stable and consistent, unless sudden actions or variations in vessel heading occurred during the simulation. The CTRA is also proven to be more robust compared to the CMM. As a result, a suitable combination of mathematical models and estimation filters can be considered to support advanced ship predictors in future ship navigation

Life-cycle cost analysis of an innovative marine dual-fuel engine under uncertainties

As innovative technologies are being deployed to accelerate shipping decarbonization in response to air emission regulations, there is considerable concern about the cost effectiveness of such technologies from a life-cycle perspective. This study conducts a life-cycle cost analysis (LCCA) on an innovative marine dual-fuel engine under uncertainties, comparing the total life-cycle cost performance of such an engine with that of a conventional diesel engine. By proposing several economic Key Performance Indicators (KPIs) such as the Net Present Cost (NPC), the Net Saving (NS) and the Saving-to-Investment Ratio (SIR), the findings indicate that the dual-fuel engine is more cost-effective than the diesel engine under a given fuel price scenario. The uncertainties are meticulously treated by using scenario sensitivity analyses and a Monte Carlo simulation. The scenario sensitivity analyses reveal that the cost effectiveness of the dual-fuel engine is sensitive to the high gas price scenarios. It is uncovered from the Monte Carlo simulation that there is an adequate degree of confidence when opting for the dual-fuel engine. Furthermore, fuel prices are found to be the most influential cost driver. Different foreseeable carbon pricing scenarios are also simulated to show that the dual-fuel engine is still the most favorable option. Regardless of fuel prices and carbon pricing scenarios, the dual-fuel engine provides a considerable environmental benefit with a CO2 emission reduction potential of 33%. The findings of this study are of interest within the field of shipping investment appraisals and relevant to decision-makers (i.e. ship-owners and investors)

Advanced Data Analytics towards Energy Efficient and Emission Reduction Retrofit Technology Integration in Shipping

An overview of integrating two energy efficient and emission reduction technologies to improve ship energy efficiency under advanced data analytics is presented in this study. The proposed technologies consist of developing engine and propulsion innovations that will be experimented under laboratory conditions and large-model-scale sea trials, respectively. These experiments will collect large amount of data sets that will be used to quantify the performance of both innovations under the advanced data analytics framework (ADAF). Hence, extensive details on the ADAF along with preliminary data sets collected from a case study vessel are presented in this study