The sustainability policy requires the Netherlands Ministry of Defense (MoD) to become largely independent of fossil fuels in the coming decades, while at the same time the geopolitical situation requires the Armed Forces to operate globally. Therefore, the Royal Netherlands Navy needs to reconsider its fuel strategy, to gradually change to energy carriers from renewable sources, and to redesign its power and propulsion system, both to improve efficiency and to reduce its emissions and signatures. The aim of this chapter is to provide a critical review of the challenges and opportunities of the transition to alternative energy carriers and energy systems and to provide a direction for the required research and development towards the future Navy fleet that can operate independently of fossil fuels and with minimal signature. First, the chapter reviews production, expected availability and projected development of cost of the various alternative fuels, with a specific focus on hydrogen, methanol, and renewable drop-in alternatives for naval distillate fuels, according to NATO F-76 spec- ifications. Subsequently, the chapter will discuss the impact of these three fuels on three differently sized navy vessels, that are currently considered for replacement: diving vessels, seagoing support vessels, and future surface combatants. For these three use cases, the chapter also discusses the potential power and propulsion system configurations and the opportunities to reduce signatures by alternative power supplies, such as fuel cells and batteries. Based on this analysis, the chapter will propose a fuel selection strategy for future navy vessels and will discuss the required research and development that is required to achieve the identified opportunities. The chapter closes with conclusions and recommendations on the route towards a future fleet that can operate globally independent of fossil fuels.@en

The third Naval Engineering and Ship Control special edition of the Journal of Marine Engineering and Technology aims to present cutting-edge research on naval engineering and ship control, as was presented during the International Naval Engineering Conference and Exhibition (INEC) and the International Ship Control Systems Symposium (iSCSS), held together in Delft in 2022.

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The protection of DC systems in mobility applications, such as land transport, aircraft, and shipping, presents significant challenges due to the need for high power density equipment in confined spaces. This paper focuses on DC systems on board ships, for which diverse applications require different power levels, architectures, and protection strategies. Existing protection frameworks and regulations are often inadequate or outdated for the field, leading to certification issues and insufficient fault analysis. This research proposes a use case-based categorization of short circuit currents for primary systems. A reference scenario is created using a simulation model of a 5 MW system in a superyacht to provide a short circuit inventory. The study proposes three contributions. A comprehensive fault inventory, a qualitative categorization, and relevant recommendations for power converter design. The research highlights the importance of fault categorization in understanding the impact of various short circuits on shipboard DC systems. The study emphasizes the importance of the evolution of materials and power converters in developing efficient protection technologies for ships. This work addresses some fundamental gaps in shipboard DC systems, providing a foundation for improved protection strategies and regulations, ultimately contributing to the advancement of protection of shipboard DC systems.

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The path to zero-emission shipping is deeply connected to full-electric vessels. One major challenge to enable this technology for broader application is the design of optimal energy management (EM). The flexibility of operating load sharing in hybrid energy systems could lead to suboptimal solutions using rule-based control. Advanced control strategies can be used to find optimal solutions for the EM problem. In addition, the use of advanced control allows for the incorporation of multiple objectives. An important compromise is the decision between minimizing cost and emissions. A promising approach for EM is the Equivalent Consumption Minimization Strategy (ECMS), which allows for instantaneous optimization of the problem and is suitable for dealing with fast system dynamics. The strategy assigns equivalent factors in the objective function, leading to an easily expandable multi-objective control approach.This paper presents a novel ECMS-based control strategy for health-aware EM of a full-electric vessel, incorporating diesel internal combustion engines, fuel cells, and batteries with flexible changing operation conditions. To this aim, firstly, we introduce our innovative formulation of the multi-objective problem, considering fuel and electricity expenditures and CO₂ and NO_x emissions, alongside the degradation of batteries and fuel cells. Subsequently, we determine the equivalent factors by employing a Pareto Front approach. Lastly, our developed controllers are assessed against a benchmark derived from state-of-the-art strategies. A case study of a full-electric vessel showcase the potential of our proposed solution. The results demonstrate the control's effectiveness in optimizing the operation considering a variety of objectives, such as fuel consumption or emission production, under variable operational conditions.

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Navies worldwide are exploring the use of alternative fuels for their vessels, aiming to cut down greenhouse gas emissions and lessen their reliance on fossil fuels. This effort also seeks to reduce their environmental impact and emissions signatures. However, combustion engines running on low reactivity fuels, including most alternative fuels like natural gas or alcohols, are limited by their lower load acceptance compared to diesel engines. As a result, they may not meet the stringent naval requirements for dynamic load capacity in power generation. A high dynamic loading capacity is a precondition for high maneuverability of the vessel on the one hand and handling of pulsed power loads and step loads caused by rail-guns and directed energy weapons on the other hand. Previous research into the dynamic response of natural gas engines required detailed in-cylinder combustion models to predict knock. These 0D/1D simulation models rely on extensive data to calibrate the combustion model, which is generally unavailable to the naval engineer designing a propulsion or energy system. Additionally, these simulation models require a significant amount of computational power and do rarely run in real-time. This study predicts the dynamic response using a Mean Value First Principle (MVFP) engine model based on the filling and emptying approach and turbocharger performance maps derived from limited data and measurements. Initially, the model is calibrated using engine data provided by the manufacturer and experimental measurements in steady state on a spark-ignited Caterpillar 3508A gas engine driving a generator at a constant speed of 1500 rpm. Additional calibrations are performed using experimental measurements of the dynamic response to step loads. Analysis of the simulation results reveals the model’s capability to predict the dynamic response of the air intake and exhaust system while being able to run in real-time. The results further show that steady-state simulations do not consider the effect of turbocharger inertia and lagging fresh air supply on the thermal loading and knock probability of the engine sufficiently. This paper underlines the significance of implementing the air and exhaust path dynamics, including turbocharger performance models, in mean-value models when investigating combustion engines operating on low reactivity fuels. Furthermore, the paper provides guidance on the minimum required number of measurements and load steps to calibrate a mean value model for the prediction of the engine operating parameters under dynamic loads. The resulting model can be used to establish limitations for the loads on the electric grid and evaluate control strategies to improve the combustion engine generator and electrical systems’ performance.@en

This article gives an overview of challenges in primary distribution, protections, and power scalability for shipboard dc systems. Given that dc technology is in development, several aspects of shipboard systems have not yet been sufficiently devised to ensure the protection and efficiency demanded. Several issues in dc systems arise from the lack of complete relevant standardization from different regulation bodies. Unipolar and bipolar bus architectures have application-specific advantages that are discussed and compared. The placement of power electronics in dc systems creates opportunities for switchboard design, and this article compares the centralized and distributed approaches. Likewise, protection architectures for shipboard dc systems have challenges. Breaker-based protection utilizes slow fuses, mechanical circuit breakers, and solid-state circuit breakers. In addition, power-electronics-based protection embeds the protective circuit in the power converters, but its development lags. This article compares the state-of-the-art technologies, reviewing their main features. Finally, the power requirement of various applications and the low production rate of vessels force the designers to utilize commercial off-the-shelf converters to scale up power. The misuse of such converters, the modular topologies, and power electronics building blocks are exposed highlighting challenges and opportunities toward the mass adoption of dc systems onboard maritime vessels.

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Maritime industry has set ambitious goals to drastically reduce its greenhouse gas emissions through stipulating and enforcing a number of energy assessment measures. Unfortunately, measures like the EEDI, EEXI, SEEMP and CII do not account for the operational and environmental uncertainty of operations at sea, even though they do provide a first means of evaluating the carbon footprint of ships. The increasing availability of high-frequency operational data offers the opportunity to quantify and account for this uncertainty in energy performance predictions. Current methods to evaluate and predict energy performance at a whole energy system level do not sufficiently account for operational and environmental uncertainty. In this work, we propose a digital twin that accurately predicts the fuel consumption and carbon footprint of the hybrid propulsion system of an Ocean-going Patrol Vessel (OPV) of the Royal Netherlands Navy under the aggregate effect of operational and environmental uncertainty. It combines first-principle steady-state models with machine learning algorithms to reach an accuracy of less than 5% MAPE on both mechanical and electrical propulsion, while bringing a 40% to 50% improvement over a model that does not utilise machine learning algorithms. Results over actual voyage intervals indicate a prediction accuracy of consumed fuel and carbon intensity within 2.5% accounting for a confidence interval of 95%. Finally, the direct comparison between mechanical and electrical propulsion showed no clear energy-saving benefits and a strong dependency of the results on each voyage's specific operational and environmental conditions.

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Editorial Special Issue: Naval Engineering and Ship Control II@en

Progressing targets on Greenhouse Gas (GHG) emission reduction urge the Netherlands Ministry of Defense (NL MoD) to reduce GHG emissions, without sacrificing striking power. The Royal Netherlands Navy (RNLN) is investigating the replacement of the Air Defense and Command Frigate (LCF) between 2030 and 2040 by a Large Surface Combatant. As it will be impossible to achieve substantial reduction of GHG emissions through energy-saving technologies, sustainable fuels need to be implemented in the design. In this paper, a literature review is presented to establish possible directions for the strategy to migrate future naval combatants from current fossil fuels to future sustainable fuels. We examined the effect of short- and long-carbon chain sustainable fuels, sustainable methanol and sustainable diesel, respectively, on the replacement Large Surface Combatant; specifically their advantages, disadvantages, production routes, future production cost estimates and availability to give an understanding which pathways can help the NL MoD to achieve their stated GHG emissions reduction goals. Moreover, we present three different design concepts with respect to fuel composition and propulsion configuration on which the impact of the established fuels is qualitatively examined. Firstly, operating on methanol has a significant impact on the design of a large surface combatant: the endurance of the ship is more than halved or the tank capacity has to be increased by 700 to 900 m3 ; the ship might need a longer machinery space to allow for more propulsion engines to compensate for the increased power requirement and unavailability of gas turbines on methanol; and required auxiliary and safety systems add further volume area to the engine room. Secondly, sustainable diesel is a drop-in fuel, which makes blending of sustainable diesel with fossil diesel possible in the existing infrastructure allowing a gradual transfer from fossil diesel to sustainable diesel. However, the production is less efficient in a well-to-wake approach and the cost of Bio-diesel and E-diesel is 5% to 30% more expensive with a mean estimated additional cost of 6 C/GJ compared to methanol. Finally, navies could consider a two-fuel strategy: sail on methanol during operations with limited autonomy, typically in peace time, and operate on diesel during operations with high autonomy, during war time operations. In this case the design needs to include both diesel and methanol fuel systems and additional space for methanol safety measures. In order to more exactly quantify the impact of methanol on the design, a concept design iteration is required, which is identified as research for future work.@en

Monitoring and evaluating the biofouling state and its effects on the vessel's hull and propeller performance is a crucial problem that attracts the attention of both academy and industry. Effective and reliable tools to address this would allow a timely cleaning procedure able to trade off costs, efficiency, and environmental impacts. In this paper, the authors carry out a critical review, accompanied with summary tables, of the biofouling problem with a particular focus on the shipping industry and the state-of-the-art techniques for monitoring and evaluating the biofouling state and its effects on the vessel's hull and propeller performance. In particular, different techniques are grouped according to the three main families of numerical models that have been designed and exploited in the literature: Physical Models (i.e., models relying on the mechanistic knowledge of the phenomena), Data-Driven Models (i.e., models relying on historical data about the phenomena together with Artificial Intelligence), and Hybrid Models (i.e., a hybridisation between Physical and Data-Driven Models). A conclusion from the performed review, open problems, and future direction of this field of research is detailed at the end of the review.

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Ship designers hardly ever receive feedback from the actual operation of their designs apart from sea acceptance trials. Similarly, crews operating the vessels do not receive a clear picture of the energy performance and environmental footprint of different options. This paper proposes a methodology based on operational data from continuous monitoring, and applies it to an ocean patrol vessel of the Royal Netherlands Navy in order to identify the impact of diverse operational conditions on energy performance over the whole operating range, but also to examine the decision to equip the vessel with hybrid propulsion. Specifically, it introduces mean energy effectiveness indicator and mean total energy efficiency over discretised vessel speed, as the main tool in quantifying the energy gains and losses to assist in making better-advised design and operational decisions. Moreover, it demonstrates a dataset enrichment procedure, using manufacturers' information, in case not all needed sensors are available. Results suggest that electrical propulsion was 15–25% less efficient than the best mechanical propulsion mode, and on the overall energy performance of the vessel, increasing speed by 1 knot caused a 7% and 14% increase over the minimum (Formula presented.) /mile emissions between 8 and 14, and above 14 knots respectively.

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Deterministic models based on the laws of physics, as well as data-driven models, are often used to assess the current state of vessels and their systems, as well as predict their future behaviour. Predictive maintenance methodologies (i.e., Condition Based Maintenance) and advanced control strategies (i.e., Model Predictive Control) are built upon the use of such numerical tools to identify ensuing performance shifts. In fact, forecasting near-future performance can substantially contribute to enhancing operational efficiency and enabling advanced system control. Data from modern sensor technology, which has become more readily available, combined with automatic control systems capable of prescribing optimal control strategies, can improve vessel operation and reduce energy consumption. A data-driven model that relies on recent advances in Artificial Intelligence, Machine Learning, and Data Mining, leveraging historical observations is employed to forecast a vessel’s onboard power generation trends as a function of the past, present, and future behaviour of a ship and its systems. To prove the framework, the proposed methodology is tested on real data collected from the Integrated Platform Management System of an Oceangoing Patrol Vessel of the Royal Netherlands Navy. The developed data-driven model is achieves high forecasting accuracy in the near-term. The authors foresee that the proposed methodology could be used as part of an electric energy control strategy, within a more integrated and intelligent mission planning framework.

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Accurate, reliable, and computationally inexpensive models of the dynamic state of combustion engines are a fundamental tool to investigate new engine designs, develop optimal control strategies, and monitor their performance. The use of those models would allow to improve the engine cost-efficiency trade-off, operational robustness, and environmental impact. To address this challenge, two state-of-the-art alternatives in literature exist. The first one is to develop high fidelity physical models (e.g., mean value engine, zero-dimensional, and one-dimensional models) exploiting the physical principles that regulate engine behaviour. The second one is to exploit historical data produced by the modern engine control and automation systems or by high-fidelity simulators to feed data-driven models (e.g., shallow and deep machine learning models) able to learn an accurate digital twin of the system without any prior knowledge. The main issues of the former approach are its complexity and the high (in some case prohibitive) computational requirements. While the main issues of the latter approach are the unpredictability of their behaviour (guarantees can be proved only for their average behaviour) and the need for large amount of historical data. In this work, following a recent promising line of research, we describe a modelling framework that is able to hybridise physical and data driven models, delivering a solution able to take the best of the two approaches, resulting in accurate, reliable, and computationally inexpensive models. In particular, we will focus on modelling the dynamic state of a four-stroke diesel engine testing the performance (both in terms of accuracy, reliability, and computational requirements) of this solution against state-of-the-art physical modelling approaches on real-world operational data.

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Diesel engines will remain a fundamental component of propulsion systems due to their maturity, reliability, and power density. Building Digital Twins of the propulsion system is one feasible solution to pursue the optimal propulsions system operation, estimating system states and efficiency. This work will investigate a modelling approach that combines high accuracy while satisfying real-time prediction capabilities by coupling a physics-based model with a data-driven modelling approach. We will demonstrate that the proposed hybridisation framework can provide state-of-the-art prediction capabilities in real-time, utilising operational data from a turbocharged, four-stroke medium-speed diesel engine.@en

Fast diesel engine models for real-time prediction in dynamic conditions are required to predict engine performance parameters, to identify emerging failures early on and to establish trends in performance reduction. In order to address these issues, two main alternatives exist: one is to exploit the physical knowledge of the problem, the other one is to exploit the historical data produced by the modern automation system. Unfortunately, the first approach often results in hard-to-tune and very computationally demanding models that are not suited for real-time prediction, while the second approach is often not trusted because of its questionable physical grounds. In this paper, the authors propose a novel hybrid model, which combines physical and data-driven models, to model diesel engine exhaust gas temperatures in operational conditions. Thanks to the combination of these two techniques, the authors were able to build a fast, accurate and physically grounded model that bridges the gap between the physical and data driven approaches. In order to support the proposal, the authors will show the performance of the different methods on real-world data collected from the Holland Class Oceangoing Patrol Vessel.

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The Dutch Ministry of Defence has the ambition to reduce its dependency on fossil fuels in order to improve the effectiveness of its operations and reduce its impact on the environment. At the same time, the diversity of current threats during maritime operations require ships that are more silent, that manoeuvre better and require less maintenance.@en

In de Operationele Energie Strategie en de Defensie Energie en Omgevings Strategie legt het ministerie van Defensie zijn ambitie vast de afhankelijkheid van fossiele brandstoffen te verminderen om zo de effectiviteit van zijn operaties te vergroten en hiermee het milieu minder te belasten. Tevens vereist de diversiteit van de huidige dreiging tijdens maritieme operaties vaartuigen die stiller varen, beter manoeuvreren en minder onderhoud vereisen.@en

Shipping plays a crucial role in modern society, but has to reduce its impact on the environment. The commercial availability of power electronic converters and lithium-ion batteries provides an opportunity to improve performance of ships energy systems while reducing their environmental impact. However, the degrees of freedom in control for hybrid propulsion and power generation require advanced control strategies to autonomously achieve the best trade-off between fuel consumption, emissions, radiated noise, propulsion availability, manoeuvrability and maintainability.
 
This PhD thesis proposes dynamic simulation models, benchmark manoeuvres and measures of performance (MOP) to quantify energy system performance. These simulation models and MOPs are used to quantify the improvements with three novel control strategies: adaptive pitch control, parallel adaptive pitch control and energy management for hybrid propulsion and power generation. Finally, a layered control strategy is proposed that can autonomously adapt to changing ship functions, using the proposed control strategies. The proposed energy systems and control strategies can thus significantly reduce the impact of shipping on the environment, while more autonomously achieving its increasingly diverse missions at sea@en

Condition Based Maintenance on diesel engines can help to reduce maintenance load and better plan maintenance activities in order to support ships with reduced or no crew. Diesel engine performance models are required to predict engine performance parameters in order to identify emerging failures early on and to establish trends in performance reduction. In this paper, a novel approach is proposed to accurately predict engine temperatures during operational dynamic manoeuvring. In this hybrid modelling approach, the authors combine the mechanistic knowledge from physical diesel engine models with the statistic knowledge from engine measurements on a sound engine. This simulation study, using data collected from a Holland class patrol vessel, demonstrates that existing models cannot accurately predict measured temperatures during dynamic manoeuvring, and that the hybrid modelling approach outperforms a purely data driven approach by reducing the prediction error during a typical day of operation from 10% to 2%.

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