J.J. Zwaginga
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The maritime energy transition, characterised by the shift toward alternative fuels to reduce emissions, presents the maritime industry with a complex and uncertain decision-making problem. It is complicated by multiple external factors, including the uncertain development of emission-reduction regulations, the performance of emission-reduction measures, and the availability of necessary infrastructural- and economic support. Further challenges arise from maritime industry-specific characteristics, such as the capital-intensive nature, long lifecycles, differences in operational requirements and the technical complexity of ships.
From the perspective of the vessel-level decision-maker, these interdependent and continuously evolving factors create deep uncertainty in emission-reduction decisions. For many, this resulted in a decision paralysis that is reflected in postponed fleet renewal investments, and the ageing of the global fleet. Consequently, the main research question this thesis addresses is: How can decision-making in the maritime energy transition be supported to enable timely ship design- and retrofit decisions under deep uncertainty? To address the deep uncertainty in the maritime energy transition, this thesis explores how to enable the use of changeability as a strategic response. This shifts the perspective from reactive compliance to strategic preparation, increasing awareness of when, what, and how to adopt emission-reduction measures.
A literature review categorises decision-making challenges and proposes a theoretical framework that subdivides the decision space into a context space, object space, and value space, including the mappings between them. Within these spaces, two primary challenge categories are identified: complexity and uncertainty. Although conceptually distinct, their interaction can result in deep uncertainty, reinforcing decision paralysis. Building upon this foundation, the Framework for Exploration of Adaptive Robustness (FEAR) was developed to support vessel-level decision-makers. The framework structures the decision problem into three interconnected modules: What, How, and When, which are used to iteratively explore the integration of emission-reduction systems.
The What-module investigates alternative emission-reduction measures and the required modifications to the ship system architecture. System representations are constructed using models from a system library, and system architecture evolution is analysed using graph and set theory to compare alternatives qualitatively and quantitatively. The How-module addresses the integration of system architectures and their changeability within the constraints of ship design. An automated ship layout methodology has been developed that explicitly incorporates system changeability considerations. This method quantifies the trade-offs between preparatory investments and adaptation costs, and identifies investments that reduce future retrofit expenditures.
The When-module evaluates emission-reduction pathways under uncertainty using adaptive robust optimisation. The optimisation is used to investigate which initial and retrofit selections of emission reduction measures remain robust under uncertain fuel costs and emission taxation, thereby providing insight into the value of changeability throughout the ship design lifecycle.
The modules are combined into the FEAR framework, which can be used to iteratively explore alternative system architectures and changeability during the concept design phase. As new technologies and information become available, the framework can be reapplied, enabling continuous evaluation of emission-reduction strategies and previously integrated change enablers. The practical use of the framework is investigated through a case study.
Incorporating change enablers during the initial design phase resulted in approximately 20-46% reduction in relative material and labour retrofit costs compared to a design without future preparation. This reduction is further influenced when accounting for lost revenue, retrofit timing, and additional yard costs. The results from the case study were discussed in an interview with expert designers, they agreed that it offers valuable tools to explore alternative emission-reduction measures and system- and ship-level preparations. The FEAR was found to be mainly beneficial to support decision argumentation. However, they also noted that the current form is not yet applicable in practice, as it requires a dedicated interface and further validation across multiple vessel types and system architectures.
In conclusion, FEAR provides a theoretically substantiated, practical framework for structuring decision-making under deep uncertainty. By integrating considerations of existing alternatives, how they can be prepared for, and when they should be implemented, the framework enables proactive and adaptive decision-making in the maritime energy transition. ...
From the perspective of the vessel-level decision-maker, these interdependent and continuously evolving factors create deep uncertainty in emission-reduction decisions. For many, this resulted in a decision paralysis that is reflected in postponed fleet renewal investments, and the ageing of the global fleet. Consequently, the main research question this thesis addresses is: How can decision-making in the maritime energy transition be supported to enable timely ship design- and retrofit decisions under deep uncertainty? To address the deep uncertainty in the maritime energy transition, this thesis explores how to enable the use of changeability as a strategic response. This shifts the perspective from reactive compliance to strategic preparation, increasing awareness of when, what, and how to adopt emission-reduction measures.
A literature review categorises decision-making challenges and proposes a theoretical framework that subdivides the decision space into a context space, object space, and value space, including the mappings between them. Within these spaces, two primary challenge categories are identified: complexity and uncertainty. Although conceptually distinct, their interaction can result in deep uncertainty, reinforcing decision paralysis. Building upon this foundation, the Framework for Exploration of Adaptive Robustness (FEAR) was developed to support vessel-level decision-makers. The framework structures the decision problem into three interconnected modules: What, How, and When, which are used to iteratively explore the integration of emission-reduction systems.
The What-module investigates alternative emission-reduction measures and the required modifications to the ship system architecture. System representations are constructed using models from a system library, and system architecture evolution is analysed using graph and set theory to compare alternatives qualitatively and quantitatively. The How-module addresses the integration of system architectures and their changeability within the constraints of ship design. An automated ship layout methodology has been developed that explicitly incorporates system changeability considerations. This method quantifies the trade-offs between preparatory investments and adaptation costs, and identifies investments that reduce future retrofit expenditures.
The When-module evaluates emission-reduction pathways under uncertainty using adaptive robust optimisation. The optimisation is used to investigate which initial and retrofit selections of emission reduction measures remain robust under uncertain fuel costs and emission taxation, thereby providing insight into the value of changeability throughout the ship design lifecycle.
The modules are combined into the FEAR framework, which can be used to iteratively explore alternative system architectures and changeability during the concept design phase. As new technologies and information become available, the framework can be reapplied, enabling continuous evaluation of emission-reduction strategies and previously integrated change enablers. The practical use of the framework is investigated through a case study.
Incorporating change enablers during the initial design phase resulted in approximately 20-46% reduction in relative material and labour retrofit costs compared to a design without future preparation. This reduction is further influenced when accounting for lost revenue, retrofit timing, and additional yard costs. The results from the case study were discussed in an interview with expert designers, they agreed that it offers valuable tools to explore alternative emission-reduction measures and system- and ship-level preparations. The FEAR was found to be mainly beneficial to support decision argumentation. However, they also noted that the current form is not yet applicable in practice, as it requires a dedicated interface and further validation across multiple vessel types and system architectures.
In conclusion, FEAR provides a theoretically substantiated, practical framework for structuring decision-making under deep uncertainty. By integrating considerations of existing alternatives, how they can be prepared for, and when they should be implemented, the framework enables proactive and adaptive decision-making in the maritime energy transition. ...
The maritime energy transition, characterised by the shift toward alternative fuels to reduce emissions, presents the maritime industry with a complex and uncertain decision-making problem. It is complicated by multiple external factors, including the uncertain development of emission-reduction regulations, the performance of emission-reduction measures, and the availability of necessary infrastructural- and economic support. Further challenges arise from maritime industry-specific characteristics, such as the capital-intensive nature, long lifecycles, differences in operational requirements and the technical complexity of ships.
From the perspective of the vessel-level decision-maker, these interdependent and continuously evolving factors create deep uncertainty in emission-reduction decisions. For many, this resulted in a decision paralysis that is reflected in postponed fleet renewal investments, and the ageing of the global fleet. Consequently, the main research question this thesis addresses is: How can decision-making in the maritime energy transition be supported to enable timely ship design- and retrofit decisions under deep uncertainty? To address the deep uncertainty in the maritime energy transition, this thesis explores how to enable the use of changeability as a strategic response. This shifts the perspective from reactive compliance to strategic preparation, increasing awareness of when, what, and how to adopt emission-reduction measures.
A literature review categorises decision-making challenges and proposes a theoretical framework that subdivides the decision space into a context space, object space, and value space, including the mappings between them. Within these spaces, two primary challenge categories are identified: complexity and uncertainty. Although conceptually distinct, their interaction can result in deep uncertainty, reinforcing decision paralysis. Building upon this foundation, the Framework for Exploration of Adaptive Robustness (FEAR) was developed to support vessel-level decision-makers. The framework structures the decision problem into three interconnected modules: What, How, and When, which are used to iteratively explore the integration of emission-reduction systems.
The What-module investigates alternative emission-reduction measures and the required modifications to the ship system architecture. System representations are constructed using models from a system library, and system architecture evolution is analysed using graph and set theory to compare alternatives qualitatively and quantitatively. The How-module addresses the integration of system architectures and their changeability within the constraints of ship design. An automated ship layout methodology has been developed that explicitly incorporates system changeability considerations. This method quantifies the trade-offs between preparatory investments and adaptation costs, and identifies investments that reduce future retrofit expenditures.
The When-module evaluates emission-reduction pathways under uncertainty using adaptive robust optimisation. The optimisation is used to investigate which initial and retrofit selections of emission reduction measures remain robust under uncertain fuel costs and emission taxation, thereby providing insight into the value of changeability throughout the ship design lifecycle.
The modules are combined into the FEAR framework, which can be used to iteratively explore alternative system architectures and changeability during the concept design phase. As new technologies and information become available, the framework can be reapplied, enabling continuous evaluation of emission-reduction strategies and previously integrated change enablers. The practical use of the framework is investigated through a case study.
Incorporating change enablers during the initial design phase resulted in approximately 20-46% reduction in relative material and labour retrofit costs compared to a design without future preparation. This reduction is further influenced when accounting for lost revenue, retrofit timing, and additional yard costs. The results from the case study were discussed in an interview with expert designers, they agreed that it offers valuable tools to explore alternative emission-reduction measures and system- and ship-level preparations. The FEAR was found to be mainly beneficial to support decision argumentation. However, they also noted that the current form is not yet applicable in practice, as it requires a dedicated interface and further validation across multiple vessel types and system architectures.
In conclusion, FEAR provides a theoretically substantiated, practical framework for structuring decision-making under deep uncertainty. By integrating considerations of existing alternatives, how they can be prepared for, and when they should be implemented, the framework enables proactive and adaptive decision-making in the maritime energy transition.
From the perspective of the vessel-level decision-maker, these interdependent and continuously evolving factors create deep uncertainty in emission-reduction decisions. For many, this resulted in a decision paralysis that is reflected in postponed fleet renewal investments, and the ageing of the global fleet. Consequently, the main research question this thesis addresses is: How can decision-making in the maritime energy transition be supported to enable timely ship design- and retrofit decisions under deep uncertainty? To address the deep uncertainty in the maritime energy transition, this thesis explores how to enable the use of changeability as a strategic response. This shifts the perspective from reactive compliance to strategic preparation, increasing awareness of when, what, and how to adopt emission-reduction measures.
A literature review categorises decision-making challenges and proposes a theoretical framework that subdivides the decision space into a context space, object space, and value space, including the mappings between them. Within these spaces, two primary challenge categories are identified: complexity and uncertainty. Although conceptually distinct, their interaction can result in deep uncertainty, reinforcing decision paralysis. Building upon this foundation, the Framework for Exploration of Adaptive Robustness (FEAR) was developed to support vessel-level decision-makers. The framework structures the decision problem into three interconnected modules: What, How, and When, which are used to iteratively explore the integration of emission-reduction systems.
The What-module investigates alternative emission-reduction measures and the required modifications to the ship system architecture. System representations are constructed using models from a system library, and system architecture evolution is analysed using graph and set theory to compare alternatives qualitatively and quantitatively. The How-module addresses the integration of system architectures and their changeability within the constraints of ship design. An automated ship layout methodology has been developed that explicitly incorporates system changeability considerations. This method quantifies the trade-offs between preparatory investments and adaptation costs, and identifies investments that reduce future retrofit expenditures.
The When-module evaluates emission-reduction pathways under uncertainty using adaptive robust optimisation. The optimisation is used to investigate which initial and retrofit selections of emission reduction measures remain robust under uncertain fuel costs and emission taxation, thereby providing insight into the value of changeability throughout the ship design lifecycle.
The modules are combined into the FEAR framework, which can be used to iteratively explore alternative system architectures and changeability during the concept design phase. As new technologies and information become available, the framework can be reapplied, enabling continuous evaluation of emission-reduction strategies and previously integrated change enablers. The practical use of the framework is investigated through a case study.
Incorporating change enablers during the initial design phase resulted in approximately 20-46% reduction in relative material and labour retrofit costs compared to a design without future preparation. This reduction is further influenced when accounting for lost revenue, retrofit timing, and additional yard costs. The results from the case study were discussed in an interview with expert designers, they agreed that it offers valuable tools to explore alternative emission-reduction measures and system- and ship-level preparations. The FEAR was found to be mainly beneficial to support decision argumentation. However, they also noted that the current form is not yet applicable in practice, as it requires a dedicated interface and further validation across multiple vessel types and system architectures.
In conclusion, FEAR provides a theoretically substantiated, practical framework for structuring decision-making under deep uncertainty. By integrating considerations of existing alternatives, how they can be prepared for, and when they should be implemented, the framework enables proactive and adaptive decision-making in the maritime energy transition.
Shipowners need to prepare for low-emission fuel alternatives to meet the IMO 2050 goals. This is a complex problem due to conflicting objectives and a high degree of uncertainty. To help navigate this problem, this paper investigates how methods that take uncertainty into account, like robust optimization and stochastic optimization, could be used to address uncertainty while taking into account multiple objectives. Robust optimization incorporates uncertainty using a scalable measure of conservativeness, while stochastic programming adds an expected value to the objective function that represents uncertain scenarios. The methods are compared by applying them to the same dataset for a Supramax bulk carrier and taking fuel prices and market-based measures as uncertain factors. It is found that both offer important insights into the impact of uncertainty, which is an improvement when compared to deterministic optimization, that does not take uncertainty into account. From a practical standpoint, both methods show that methanol and LNG ships allow a cheap but large reduction in emissions through the use of biofuels. More importantly, even though there are limitations due to the parameter range assumptions, ignoring uncertainty with respect to future fuels is worse as a starting point for discussions.
...
Shipowners need to prepare for low-emission fuel alternatives to meet the IMO 2050 goals. This is a complex problem due to conflicting objectives and a high degree of uncertainty. To help navigate this problem, this paper investigates how methods that take uncertainty into account, like robust optimization and stochastic optimization, could be used to address uncertainty while taking into account multiple objectives. Robust optimization incorporates uncertainty using a scalable measure of conservativeness, while stochastic programming adds an expected value to the objective function that represents uncertain scenarios. The methods are compared by applying them to the same dataset for a Supramax bulk carrier and taking fuel prices and market-based measures as uncertain factors. It is found that both offer important insights into the impact of uncertainty, which is an improvement when compared to deterministic optimization, that does not take uncertainty into account. From a practical standpoint, both methods show that methanol and LNG ships allow a cheap but large reduction in emissions through the use of biofuels. More importantly, even though there are limitations due to the parameter range assumptions, ignoring uncertainty with respect to future fuels is worse as a starting point for discussions.
To integrate and assist the system and automation design phases of complex marine vessels, this paper proposes a two-level semantically enhanced scheme. At the design level, the system components are described and automatically connected by a developed graph-making tool using semantic 'knowledge'. Decisions regarding the system selection are made based on certain Quality of Service Criteria (QoS) and enforced in the final semantic database using a dedicated cognitive agent. The automation level leverages the selected systems semantic information with that of the associated automation components and reuses the graph-making tool to update the connection graph. The resulting knowledge-graph is then used to 'reason' for the creation of feasible closed-loop control architectures while a cognitive agent determines which closed-loop architecture to use based on various QoS criteria. The chosen closed-loop architecture can then change in an online manner during the vessel operation in case that system reconfiguration is required either due to malfunctioning components, or aiming to satisfy mission's goals. The applicability and efficiency of the proposed method are shown using a case study for marine propulsion.
...
To integrate and assist the system and automation design phases of complex marine vessels, this paper proposes a two-level semantically enhanced scheme. At the design level, the system components are described and automatically connected by a developed graph-making tool using semantic 'knowledge'. Decisions regarding the system selection are made based on certain Quality of Service Criteria (QoS) and enforced in the final semantic database using a dedicated cognitive agent. The automation level leverages the selected systems semantic information with that of the associated automation components and reuses the graph-making tool to update the connection graph. The resulting knowledge-graph is then used to 'reason' for the creation of feasible closed-loop control architectures while a cognitive agent determines which closed-loop architecture to use based on various QoS criteria. The chosen closed-loop architecture can then change in an online manner during the vessel operation in case that system reconfiguration is required either due to malfunctioning components, or aiming to satisfy mission's goals. The applicability and efficiency of the proposed method are shown using a case study for marine propulsion.
It is essential to use alternative fuels if we are to reach the emission reduction targets set by the IMO. Hydrogen carriers are classified as zero-emission, while having a higher energy density (including packing factor) than pure hydrogen. They are often considered as safe alternative fuels. The exact definition of what safety entails is often lacking, both for hydrogen carriers as well as for ship safety. The aim of this study is to review the safety of hydrogen carriers from two perspectives, investigating potential connections between the chemical and maritime approaches to safety. This enables a reasoned consideration between safety aspects and other design drivers in ship design and operation. The hydrogen carriers AB, NaBH4, KBH4 and two LOHCs (NEC and DBT) are taken into consideration, together with a couple reference fuels (ammonia, methanol and MDO). After the evaluation of chemical properties related to safety and the scope of the current IMO safety framework, it can be concluded that safety remains a vague and non-explicit concept from both perspectives. Therefore, further research is required to prove the safe application of hydrogen carriers onboard ships.
...
It is essential to use alternative fuels if we are to reach the emission reduction targets set by the IMO. Hydrogen carriers are classified as zero-emission, while having a higher energy density (including packing factor) than pure hydrogen. They are often considered as safe alternative fuels. The exact definition of what safety entails is often lacking, both for hydrogen carriers as well as for ship safety. The aim of this study is to review the safety of hydrogen carriers from two perspectives, investigating potential connections between the chemical and maritime approaches to safety. This enables a reasoned consideration between safety aspects and other design drivers in ship design and operation. The hydrogen carriers AB, NaBH4, KBH4 and two LOHCs (NEC and DBT) are taken into consideration, together with a couple reference fuels (ammonia, methanol and MDO). After the evaluation of chemical properties related to safety and the scope of the current IMO safety framework, it can be concluded that safety remains a vague and non-explicit concept from both perspectives. Therefore, further research is required to prove the safe application of hydrogen carriers onboard ships.
The maritime energy transition presents deep uncertainties that are difficult to deal with in the current ship design process. Even though other fields have stressed using adaptive strategies and explorative methods to deal with deep uncertainty, it is rarely included in ship design. Therefore, this paper compares three applicable methods to investigate how such aspects could support the design process. Each method is found to offer specific improvements to decision making, but no separate method meets the established criteria to the desired degree. The methods are found to be complementary, and by developing a combined method for ship design, ships can be better prepared to deal with deep uncertainty.
...
The maritime energy transition presents deep uncertainties that are difficult to deal with in the current ship design process. Even though other fields have stressed using adaptive strategies and explorative methods to deal with deep uncertainty, it is rarely included in ship design. Therefore, this paper compares three applicable methods to investigate how such aspects could support the design process. Each method is found to offer specific improvements to decision making, but no separate method meets the established criteria to the desired degree. The methods are found to be complementary, and by developing a combined method for ship design, ships can be better prepared to deal with deep uncertainty.
Conference paper
(2022)
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S. Brans, Austin A. Kana, Philip Bronkhorst, Nicole Charisi, I. Ting Kao, Laurentiu Lupoae, Casper van Lynden, Joan le Poole, Jesper Zwaginga
This paper describes two new modular ship design activities for graduate education at Delft University of Technology that have been developed during COVID. First, a new 2-hour hybrid format (in-person and virtual participation) game was designed to teach students modular design for offshore support vessels (OSVs). Second, an 8-week MSc-level ship design project was redeveloped to cover the design of a small fleet of modular OSVs for offshore wind. The paper discusses the drivers behind these new design educational activities, the details of the activities themselves, and concludes with lessons learned focused on improving graduate education for masters students studying ship design.
...
This paper describes two new modular ship design activities for graduate education at Delft University of Technology that have been developed during COVID. First, a new 2-hour hybrid format (in-person and virtual participation) game was designed to teach students modular design for offshore support vessels (OSVs). Second, an 8-week MSc-level ship design project was redeveloped to cover the design of a small fleet of modular OSVs for offshore wind. The paper discusses the drivers behind these new design educational activities, the details of the activities themselves, and concludes with lessons learned focused on improving graduate education for masters students studying ship design.
To decrease Europe's harmful emissions, the European Union aims to substantially increase its offshore wind energy capacity. To further develop offshore wind energy, investment in ever-larger construction vessels is necessary. However, this market is characterised by seemingly unpredictable growth of market demand, turbine capacity and distance from shore. Currently it is difficult to deal with such market uncertainty within the ship design process. This research aims to develop a method that is able to deal with market uncertainty in early ship design by increasing knowledge when design freedom is still high. The method uses uncertainty modelling prior to the requirement definition stage by performing global research into the market, and during the concept design stage by iteratively co-evolving the vessel design and business case in parallel. The method consists of three parts; simulating an expected market from data, modelling multiple vessel designs, and an uncertainty model that evaluates the performance of the vessels in the market. The case study into offshore wind foundation installation vessels showed that the method can provide valuable insight into the effect of ship parameters like main dimensions, crane size and ship speed on the performance in an uncertain market. These results were used to create a value robust design, which is capable of handling uncertainty without changes to the vessel. The developed method thus provides a way to deal with market uncertainty in the early ship design process.
...
To decrease Europe's harmful emissions, the European Union aims to substantially increase its offshore wind energy capacity. To further develop offshore wind energy, investment in ever-larger construction vessels is necessary. However, this market is characterised by seemingly unpredictable growth of market demand, turbine capacity and distance from shore. Currently it is difficult to deal with such market uncertainty within the ship design process. This research aims to develop a method that is able to deal with market uncertainty in early ship design by increasing knowledge when design freedom is still high. The method uses uncertainty modelling prior to the requirement definition stage by performing global research into the market, and during the concept design stage by iteratively co-evolving the vessel design and business case in parallel. The method consists of three parts; simulating an expected market from data, modelling multiple vessel designs, and an uncertainty model that evaluates the performance of the vessels in the market. The case study into offshore wind foundation installation vessels showed that the method can provide valuable insight into the effect of ship parameters like main dimensions, crane size and ship speed on the performance in an uncertain market. These results were used to create a value robust design, which is capable of handling uncertainty without changes to the vessel. The developed method thus provides a way to deal with market uncertainty in the early ship design process.