This study investigates the impact of lowered lock capacity on lock demand shifts within the Weurt- Grave system, a critical link in the Southeast inland waterway corridor of the Netherlands. It answers the following research question: What impact does a reduction in capacity, caused by low water levels, have on alternative locks within the inland waterway system, with a focus on the Weurt- Grave system? Understanding the interactions is important for improving waterway infrastructure planning. Lock ca-pacity constraints caused by extreme water levels are becoming more likely to occur due to climate change. This increases the importance of being able to predict the effects these disruptions will have on the larger waterway system. This research shows from historical data how lock capacity decreases can affect other locks. This helps give insight in the scope that might need to be assessed when researching a single lock that is part of a larger system. During dry periods, low water levels in the Waal limit lock Weurt’s capacity, redirecting larger vessels to lock Grave. This redirection alters waiting times, fleet composition, and traffic patterns within the system. It shows the system’s sensitivity to environmental conditions. Using lock usage and water level data from Rijkswaterstaat, the study applies statistical methods, including logit regression, time series analysis, and moving averages, to quantify water level impacts on lock utilization and traffic patterns. The findings reveal redistributions in vessel traffic and load, primarily driven by vessel draught, origin- destination patterns. While the system shows adaptability, capacity constraints at Grave raise concerns about infrastructure durability under increased demand. Furthermore, the research identifies limitations in existing traffic models, such as SIVAK and BIVAS, which fail to capture dynamic interdependencies between locks under changing environmental effects. This study shows that lock capacity constraints should not be viewed as an isolated problem. These constraints could be causing network wide effects, changing traffic flow and fleet compositions at other points in the system. By revealing how vessel behaviour responds to changing capacities, the research emphasizes the necessity of network wide approaches to lock modelling. Knowing of these possible system-wide effects is essential for modelling waterway infrastructure that need to handle the results of climate change and changing transport demands. This will improve the resilience and efficiency of the inland waterway network.

As the frequency and intensity of natural disasters increase, there is growing recognition of the need to address climate change and limit the increase in global average temperature. The shipping industry, which contributes 2.9% to global anthropogenic greenhouse gas emissions, plays a significant role by releasing substantial amounts of CO2, other harmful gases, and fine particles, negatively impacting climate change and the health of those near ports or waterways. As part of a wider initiative to reduce those effects, the International Maritime Organization has set the goal for the shipping industry to achieve net zero emissions by 2050. In order to accomplish this, accurate and comprehensive information on emissions is crucial.

Various methods have been developed to estimate emissions in the shipping industry. Top-down methods are applied using large-scale data to estimate emissions over a wide area. This approach provides a comprehensive overview but lacks the specific details required for local interventions. In contrast, bottom-up methods are applied and start with detailed data at the source and aggregate information to estimate the total emissions. Bottom-up methods offer more precise insights, however, more extensive data and complex modeling is required. To obtain more precise understandings, the modern bottom-up models use Automatic Identification System as input, a globally used system for tracking vessels. AIS data consists, among others, of time-dependent variables such as speed, location, and ship identification number. The data is used to assign an operational mode to the ship (sailing, maneuvering, anchoring, berthing). Based on this mode, it is determined if the main engines of the ship are on. If they are, the engine power is calculated from the resistance force acting on the moving ship. It is possible to convert engine power to fuel consumption and later to emissions with Specific Fuel Oil Consumption and emission factors.

Currently used emission models calculate engine power with the use of empirical formulas. So far, these models have mostly been validated by comparing them either to a noon report, or to another model. As a result, it gives no insight into the capability of a model to predict with a high spatial resolution, which is important in port areas and inland waterways.

There is little room for improvement in current semi-empirical bottom-up methods. However, in recent years, machine learning has shown the capability of replacing and often outperforming empirical models in other fields of research. This is due to the ability to model nonlinear behavior, find relations that humans can not, and work in higher dimensions. As a ship's engine power is reliant on a multitude of factors, machine learning might be the solution towards more accurate predictions.

This research aims to assess the capability of machine learning models to predict a ship's engine power with a high spatial resolution, with a focus on LSTM models. To do so, onboard sensor data from two ships was used; one sea-going container vessel and one inland tanker. The measured data was used to validate the semi-empirical method. Afterwards, the machine learning model was trained against the same sensor data. The predictions of the semi-empirical and machine learning models were then compared to one another.

Comparisons showed that the error for total energy used for the container vessel went from +62.87% to -0.82% when using a Bi-LSTM model with speed, acceleration, draught and depth as input. For the assessment of the spatiotemporal predictions, the MAE and RMSE were used. Based on these performance indicators, a normal LSTM network with speed, acceleration, draught and depth as input performed best. The MAE compared to the reference went down from 9542 to 2863, or about 7% of the maximum engine power.

The second case study highlighted the challenges that come with engine power prediction in inland waterways. Firstly, the fact that the speed over ground is used has a bigger influence in this case, as the ship will always sail with or against the current. Secondly, the influence of the blockage factor can
not be ignored without having higher losses. In addition to these two shortcomings, the model was trained on a diesel-electric ship. This ship has a much more constant power profile than ships with a more classical propulsion run on fossil fuels. This likely made the model more robust to changes. The combination of these three aspects caused less accurate predictions. Although there was more data available, errors were higher than for the case study. The best performing models showed a +3.41% error on total energy use and an MAE of 205.65, or about 12.8% of the maximum engine power.

This research has contributed new insights to the field of maritime emission modeling. The potential of machine learning models has been shown in comparison to an existing semi-empirical model. In addition, this model has now been validated using measurement data for a seagoing container vessel. Finally, shortcomings of the method using machine learning were exposed and a solution is proposed for a more complete, generalizable model.

The key parameters governing collision energy calculation are the velocity of the vessel, its mass, and the angle of approach. The conventional method outlined in the Recommendations of the Committee for Waterfront Structures Harbours and Waterways (EAU, 2012), which was specifically developed for berthing ships, relies on an overly simplistic model that fails to capture the dynamics of ship­to­guiding structure collisions. Nevertheless, in the Netherlands, the Guidelines for Civil Engineering Structures (Richtlijnen Ontwerp Kunstwerken, ROK) suggest that the EAU 2012 method be applied unmodified for guiding structures. This report introduces a methodological approach aimed at refining the calculation of collision energy by addressing specific deficiencies in the existing framework, particularly regarding its application to guiding structures.

The kinetic energy approach used in the EAU 2012 method requires, as inputs, the mass of the ship, its velocity vector normal to the structure, a virtual mass coefficient accounting for water mass moving with the ship, and an eccentricity coefficient accounting for energy loss due to rotation when the ship does not approach the structure parallel.

The literature review identifies critical gaps in the EAU 2012 approach to calculating collision energy for guiding structures. An unsuitable application of the eccentricity coefficient and assumptions about the velocity vector direction, which do not align with actual conditions in ship­to­guiding structure colli­ sions, are discovered. Moreover, the review reveals that the framework does not differentiate between berthing and guiding approaches, resulting in a one­size­fits­all approach that fails to account for the unique characteristics and requirements of guiding structures. The framework only partially includes the ship’s angle of approach to the structure in the kinetic energy calculation. Although the recommen­ dation is to calculate the kinetic energy using the normal ship speed to the structure—thus indirectly accounting for the approach angle—when it comes to the eccentricity coefficient, which relies on ap­ proach geometry, the recommendation is to consider the total velocity vector. This practice results in exaggerated collision energy when a ship maintains forward speed near guiding structures while passing through the lock.

The theoretical refinement proposed in this thesis focuses on the scenario where the ship approaches a guiding structure with purely longitudinal speed. Specifically, a new definition for the angle variable in the eccentricity coefficient expression is proposed, which directly reflects the effect of the approach angle of the ship linked to the contributing velocity vector and not only laterally through the magnitude of the contributing vector. This proposed change is validated through comparative analysis.

The comparative analysis is performed using historical data cases from model research published by Ri­ jkswaterstaat (Ministry of Infrastructure and Water Management). The data consist of experiments with ship approaches to structures that have characteristics fitting guiding structure approaches. The ships are of lengths commonly found in lock passages, maintain forward speed, and approach structures similar to those at lock entrances. The historical data provide the maximum force exerted on the tested structure and the resulting deflection. This information is used to derive the energy absorbed by the structure. The published reports valuable to this research resulted in the Virtual Water Mass method. This method is reproduced to provide benchmark values for the kinetic energy absorbed, which are compared to both the EAU 2012 collision energy calculation and the proposed refined calculation for the historical data cases.

The findings of the comparative analysis suggest that the proposed refined calculation provides results closer to the benchmark values than the EAU 2012 calculation. However, a consistent improvement was not discovered. Although the refined calculation is significantly closer to the benchmark value in every case compared to the EAU 2012 calculation, this improvement cannot be quantified as a universal percentage, as it varied for each case. Despite this, the comparative analysis still supports the main objective of demonstrating the exaggeration in the EAU 2012 collision energy calculation.

A case study, as part of this research, explores the potential effect of the refined calculation on an actual structure design. In the case study, the design vessel, velocity, and angle of approach are sourced from the Waterway Guidelines (2011, 2020) and ROK 2.0 (all documents published by Rijkswaterstaat). After calculating the impact energy using the two methods compared in this research, the design of the mono­ pile follows the Blum method. The structure is modeled as a series of piles connected by a main girder, with the girder having the same diameter as the piles to ensure uniformity and strength.

The case of the Volkerak locks is chosen, and calculations using both the standard EAU 2012 method and the proposed refined one are executed. Two designs are produced and compared. With the refined calculation, a design with a smaller pile diameter of 1,620 mm and a longer in­between pile distance of 6.5 m is achieved, compared to the EAU 2012 design, where the pile diameter is 2,020 mm and the distance between piles is 3 m. These results demonstrate the potential for material savings if the refined calculation is adopted.

The study concludes that a refined approach is indeed necessary and that focus should be placed on specific definitions and descriptions in the guidelines. Ambiguity in such definitions leaves decision­ making subject to individual interpretations, creating inconsistency among the designs of structures with the same purpose. Although this inconsistency may not be inherently dangerous, as it usually results in an overly conservative design, it contributes to excess material usage and resource waste.

This study explores the decisive aspects during an evaluation for the viability of an existing innovative interorganisational partnership. This research focuses on six interorganisational partnerships within the maritime sector, using semi-structured interviews conducted with eleven participants from two perspectives, the strategic and the operational. The analysis found three aspects to be decisive during an evaluation, which are the task-related, the partner-related and the environment-related aspect. Among these three aspects, ten categories are found. This research provides academic contributions and practical recommendations for organisations and individuals involved in an innovative interorganisational partnership. Among these practical recommendations, the trigger for an evaluation, the criteria for an evaluation, and the involved individuals who should execute the evaluation are provided.

The expansion of offshore wind farms in the North Sea has raised serious concerns about maritime safety, in particular the increased risk of collisions—or 'allisions'—where a ship adrift hits a stationary structure, such as a wind turbine. For Rijkswaterstaat, the executive agency of the Ministry of Infrastructure and Water Management, managing this expansion requires developing a comprehensive understanding of the integral image of allision risks of drifting ships in the Dutch North Sea region. This understanding must be valid, reliable, and insightful, and include factors such as dynamic weather conditions and shipping activity. Unfortunately, current state-of-the-art methods for estimating the probability of allision rely heavily on assumptions, leading to significant variations in risk estimates, as highlighted by Ellis et al. (2008). These methods usually lack a North Sea-wide perspective and instead focus on risks specific to individual turbines.
This study proposes an approach based on trajectory prediction using the OpenDrift model, which is simple and accessible for simulating ship drift trajectories based on wind, wave, and current data. A method is developed to estimate the probability of allision by combining the probability of a ship drifting into a wind farm, analysed through a ship's potential trajectories, with ship density data for specific locations. This method facilitates the calculation of allision risks under the influence of different environmental conditions and can be applied to multiple wind farms, providing a detailed assessment of the origins of potential threats and available response times.
Overall, the assumptions in state-of-the-art methods might not sufficiently capture the risks associated with drifting ships. In addition, the established method broadens the ability to improve monitoring accuracy and optimise the positioning of ERTVs, even for specific environmental conditions. As a next step, this method should be extended to estimate actual risk levels by considering the potential consequences of an allision. With this approach, decision-making regarding allision threats can be contextualised alongside other maritime safety risks, facilitating the development of a robust risk mitigation strategy that enables Rijkswaterstaat to responsibly exploit the abundant potential the North Sea has to offer.

Maintenance dredging plays a crucial role in maintaining navigable waterways. While traditional methods using Trailing Suction Hopper Dredgers (TSHDs) are well-established, Water Injection Dredgers (WIDs) are gaining popularity due to their energy efficiency. WIDs utilize hydrodynamic forces to mobilize sediment, eliminating the need for physical transport, thereby reducing energy consumption. However, a comprehensive comparison between WID and TSHD-based dredging methods has been lacking.

This research aimed to address this gap by developing a model to simulate WID operations, estimate production rates, and assess energy consumption. The model was integrated into the discrete-event simulation package OpenCLSim, enabling the comparison of different dredging strategies.

The model incorporates various factors, including sediment characteristics, hydrodynamic conditions, and equipment specifications. Production rates were estimated using empirical, theoretical, and data-driven approaches. The energy footprint was calculated by considering the power consumption of key components such as engines, jet pumps, and thrusters. A case study was conducted to validate the model and compare WID and TSHD performance. Results demonstrated that WIDs were significantly more energy-efficient per cubic meter of dredged material compared to TSHDs. However, the energy footprint is significantly effected by the operational circumstances.

The model provides valuable insights for dredging contractors and port authorities. By simulating various scenarios, stakeholders can optimize dredging strategies, minimizing environmental impact and maximizing efficiency. Future research should focus on refining the model's accuracy, expanding its applicability to diverse scenarios, and exploring the potential for optimizing dredging operations based on energy consumption. This research contributes to a better understanding of sustainable dredging practices. By promoting the adoption of energy-efficient technologies like WIDs, the dredging industry can play a vital role in mitigating its environmental impact and contributing to a more sustainable future.

Maintaining the channels and infrastructure for large commercial ports is costly and complex. Port authorities traditionally rely on bathymetry surveys to guide their dredging operations, which limits their ability to be proactive. This research aims to develop machine learning (ML) methods to predict sedimentation rates (SR) by analysing patterns in hydrological and meteorological (hydro-meteo) conditions and dredging data. By investigating the possibility of SR prediction models, this research can contribute to efficient maintenance operations.

The study focuses on the Botlek, a harbour in the Port of Rotterdam situated in the Rhine-Meuse estuary. Three data types are integrated to capture the dynamic interplay of saline and riverine factors within the estuary. The data consists of Multibeam bathymetry surveys, hydro-meteo variables (such as salinity, river discharge, and tidal variation), and dredging logs. The surveys provide the net sediment accumulation, which is assumed to result from a specific period of hydro-meteo conditions and dredging. Due to the limited availability of surveys, the number of features had to be managed. To avoid having more features than samples, the hydro-meteo variables are aggregated into daily, weekly, and
monthly means.

The ML algorithms evaluated in this research are Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR). The feature importance scores from the RFR and the accuracy on small datasets of the SVR were decisive in this selection. All algorithms were tested and refined over multiple development phases to determine their predictive accuracy and robustness across different data configurations. It was found that the ML algorithms can reasonably predict SR. In addition to dredging data, incorporating hydro-meteo variables enhanced the predictive accuracy and consistency. Specifically, a feature set of dredging volumes, salinity, discharge, and tidal variation improved model performance. Training the model on dredging data only resulted in an inferior performance. The monthly aggregation of these hydro-meteo data was the most beneficial configuration. Among the tested algorithms, SVR consistently outperformed both LR and RFR, with its best configuration achieving a mean R2 score of 0.69 over four different dataset splits.

The ML models do not have to be able to predict sediment volumes with a small confidence interval to be practical for the port authorities. Instead, the models should be able to predict trends in sediment accumulation and provide actionable insights to enable proactive dredging. The research shows promising results, as most predictions fall within the acceptable range. However, predictive accuracy strongly varied across the different dredging areas in Botlek. Further development is required to provide real-time, reliable SR predictions before the models can be integrated into the maintenance operations of ports.

The goal of achieving net-zero emissions by 2050 requires innovative ways to reduce energy consumption in all sectors. This thesis presents an in-depth analysis on the effect of coastal currents on energy consumption of sailing dredging vessels and the potential to make use of these currents to minimize energy consumption in dredging projects. To model the highly dynamic and time-dependent currents of the North Sea, an analytical solution for the tidal-induced currents and residual density-driven currents was used to create a synthetic 3D flow field, respectively derived by Prandle and Heaps. To quantify the energy consumption of dredging vessels sailing through a 3D flow field, a python-based model was developed. This model utilizes the Holtrop and Mennen method to calculate the sailing speed corresponding to a desired engine power, with a modification to account for current-induced drag resistance on the rudder caused by currents perpendicular to the sailing direction. The model was validated with sensory vessel data of Van Oord containing information of a dredging project in the North Sea, in combination with measured data for the currents at the location of the project. The validated model was used to explore a dredging strategy for a sand nourishment project that minimizes energy consumption by waiting for favourable marine currents before sailing. A sand nourishment project was chosen because the primary cross-shore movement of the vessel interacts with the bi-weekly occurrence of alternating cross-shore currents. Two cases were simulated, a hypothetical case in which the vessel does not consume energy when waiting, and a realistic case in which the vessel's energy consumption continues due to utilities such a lighting and heating of on-board accommodations. The hypothetical case showed a reduction of energy consumption of 3\% when sailing with the cross-shore currents as opposed to neglecting these currents, which is at the lower boundary of the energy reduction due to voyage optimization predicted by IMO (1\% - 10\%). However, the energy reduction was outweighed by energy consumption during waiting in the realistic case, making this strategy unsuitable for conventional dredging vessels. The model developed in this thesis can be of significant value for both dredging companies as well as researchers. These stakeholders can use the model to plan and optimize dredging activities, reduce environmental impact, and identify areas for innovation and improvement in the dredging industry.

In the global energy transition where the shift from fossil fuels to renewable energy is needed, wind energy is a growing industry. In the offshore wind industry, Service Operation Vessels (SOVs) are needed to operate and maintain wind farms. To reduce the environmental footprint of SOVs at offshore wind farms, the possibility of battery-electric sailing with plug-in charging at offshore wind farms is investigated. Battery-electric sailing has been applied to many vessels, however, it is mostly applied to ferries which operate with a fixed daily schedule. An SOV has a variable operational schedule and therefore the energy use calculation is more complex than for a fixed operational schedule.

At the root of calculating the daily energy use are the daily schedules of the SOV. The daily schedules are determined by the maintenance strategy applied by the contractor, and the optimisation of the vehicle routing problems. However, in the concept design phase, this method is too detailed to get a useful and trustworthy result. A discrete event simulation is suggested to create daily operation schedules of the SOV where the power and duration are still calculated for, but the too-detailed routing information is left behind. The daily schedules are structured as a vehicle routing problem with delivery and pick-up with full-day and half-day tasks. They are influenced by wind farm characteristics, vessel characteristics, and operational characteristics.

With the information that is known about the daily schedules and the input parameters, a Monte Carlo simulation is deemed the most appropriate. This method takes into account the likelihood of input variables, can deal with these uncertainties, and still give a useful, trustworthy, result. The model creates many daily schedules of which the resulting maximum energy use, by the law of large numbers, converges to the expected value. For the idle time, a charging module is implemented to make the choice between standby and charging.

A case study is done to look at the influence on the results of design choices and boundary settings. The inputs that are varied during the case study are: with or without charging, the number of charging points, the layout of the offshore wind farm, the workday length, the task distribution and the charging power. The daily energy use results for a 12-hour workday vary from a bit over 4000 kWh with not that many tasks and six charging points, to over 10000 kWh for many tasks and only one charging point. Individual inputs can make a difference from 500 to 3000 kWh. The case study gives a good insight into how much influence different parameters have on daily energy use and shows that offshore charging during the day can drastically reduce the maximum battery energy needed during the day.

The shipping industry is responsible for almost 3% of the world's greenhouse gas emissions and is looking for possibilities to reduce their share. The demand for lowering the emissions of the maritime industry creates the need for insight in the emissions. Currently a generic method to specify emissions in space and time, as a function of vessel and waterway properties is not available.
Although most of the emissions of vessels take place at sea, the most noticeable part takes place in ports since they are located close to urbanized areas. Therefore, this research focuses specifically on emissions in ports. The study investigates the impact of sea-going vessels. By gaining insight in the emissions of sea-going vessels, the big polluters can be tackled. Due to the scope of the research, not all vessel types and emission types are taken into account. Ten vessel types are selected and only CO2, NOx, SOx and PM10 emissions are estimated. These are the most relevant polluters in the shipping industry since they cause health and environmental related issues, both locally as more widely.
To provide insight in the emissions, the objective of this study is to develop a generalized method to calculate and map the emission of a single vessel in space and time in ports based on reliable data. To reduce these emissions, emission reduction strategies have to be drawn up, targeting the largest emission sources and the most crucial locations. Therefore, the developed method must not only quantify the emission sources, but must also provide insight in emission patterns in ports and indicate emission hotspots.
A bottom-up method is developed to estimate the emission of a single vessel in space and time. To derive the emission rate of a vessel, the fuel and energy consumption of the vessel is multiplied by a vessel-specific emission factor. The CO2 and SOx emissions follow a fuel-based approach, in which the emissions produced are directly proportional to the fuel consumption and therefore depend on the engine load. The energy-based approach is used for estimating NOx and PM10 emissions, which cannot be directly related to the fuel consumption but depend on engine characteristics. The fuel consumption is determined by multiplying the energy consumption of the engine with a fuel consumption factor specific to each vessel, meaning the fuel consumption is related to the energy consumption.
The amount of energy the main engines of a vessel consume, is the energy needed to overcome the resistance a ship experiences from sailing through the water and is therefore related to the vessel's speed. This speed is derived from AIS data. AIS data represents real-life vessel tracking data and is gathered automatically, which makes it a reliable and realistic data source. It also provides the ability to make the emission estimations time dependent. Due to the global coverage, AIS data is a good data source to develop a generic method applicable to ports all around the world.

However, in ports the vessel's speed alone is not a good indicator of the energy consumption, since this neglects the amount of energy the auxiliary engines consume. Their energy consumption is dependent on how much energy the electrical systems of a vessel require at that moment, which can be high when a vessel is for example at berth or manoeuvring. The energy consumption is therefore related to the operations a vessel performs. This leads to an approach which takes into account the vessel's resistance and operational modes.
Four operational modes are important to distinguish in ports: 'sailing', 'manoeuvring', 'anchoring' and 'berthing'. According to the operational mode, the main engine power is either estimated with the resistance calculation from Holtrop and Mennen when the vessel is sailing or manoeuvring, or assumed zero when laying still at anchor or berth. According to the operational mode, the auxiliary engine power can be derived from values of the IMO fourth GHG research. Depending on the emission type and vessel characteristics, the emission factor is determined.
The algorithm to determine the emission of a single vessel in space and time is implemented in a model. The model's input consists of AIS data providing the speed and position of the vessel, a vessel database containing vessel characteristics based on information from the Sea-web Ships database, and a FISgraph of the port network. This graph contains the fairway characteristics needed to calculate the resistance. The calculated emissions will also be displayed on the FIS graph for a detailed insight in the emission distribution in space.
The model provides an insight in the emissions patterns in a port. The fairway sections which are subjected to high emissions can be identified immediately and so the emission hotspots are determined. The source of the emissions can be identified by down-drilling of the emissions. The model can drill down to vessel types, operational modes and all the way down to a single vessel in space and time.
The model is illustrated by means of two case studies concerning the Port of Rotterdam and the Port of Constanța. These case studies indicate that the port basins hosting the largest vessels have the highest estimated emissions. These basins are indicated as an emission hotspot by the model when the emissions are projected on the FIS graph. In ports generally, high emissions are observed at places with a high traffic intensity, such as the port entrance. Junctions of fairway sections or port basin entrances also show locally higher emissions. The rise in emissions, is probably due to the fact that vessels are slowing down when approaching a junction. This increases the emission rate of a vessel due to more inefficient engine use, but also since they spend a larger amount of time at this fairway section.
By indicating the location and source of the emission hotspots, targeted emission reduction measures can be taken. Three of these measures are demonstrated on one of the case studies. The first strategy concerns installing shore power. The model is able to simulate vessels connected to shore power, by setting their emission rate at berth to zero. This simulation is compared to the original situation without using shore power. Out of the evaluated reduction measures, this seems the best strategy to reduce emissions since it shows the largest reduction of the total emissions. Besides that, it is an effective measure especially for ports, since the largest emissions reduction takes place at berth. The second evaluated strategy shows also good results and is about switching from a normal tugboat fleet to a zero-emission tugboat fleet. The model simulates this switch by eliminating all the tugboats from the fleet since their emissions will be zero. This new case is compared to the original situation with normal tugboats. The third option to reduce emissions is applying ECA limits which do not seem to have a lot of effect on reducing emissions except for the SOx emissions. This is derived from comparing the original situation without ECA limits, to a new case study where a situation with ECA limits is simulated. This means that the fuel types of the vessels are changed from the most economical fuel type to the lightest fuel type on board and that the sulphur content in the fuel is altered.
However, the model has some limitations. The method does not take into account the effect of currents or variations in time of the water depth. The accuracy of the resistance calculation can be improved by adding these. Furthermore, the emission pattern of tugboats needs further examination as it could not be demonstrated that the split of emissions into operational modes is correctly. The research has also shown that the method to estimate the energy consumption is not suitable for tankers at berth, since their energy consumption pattern is different. The quantity of emissions in the case study of the Port of Rotterdam is far from the expected amount of emissions. The quantification of emissions is assumed to be unreliable and further research should focus on validating these results.
Concluding, the developed model makes use of AIS data, local waterway properties, empirical emission factors and operational modes. This data is used in a physics-based method to estimate the resistance and the energy consumption. If this information is available, all this combined makes the approach in principle applicable to any port. The developed model provides an insight in the emission distribution patterns and provides the ability for down-drilling to find the source of the emission hotspots. A targeted emission reduction strategy can be proposed as a result of this and the model has the ability to evaluate specific emission reduction strategies. The strategies can be simulated with the developed model and the effect of these measures can be quantified by comparing the emission reduction strategy to a situation without these measures.

Implementing renewable energy generation and more sustainable consumption behaviour in the future inland shipping industry is necessary. Electrification of the inland shipping fleet leads to an increase in electrical energy demand on the consumption side. The generation of decentralized photovoltaic (PV) energy systems on board of general cargo vessels can be one solution to these challenges. To estimate the potential of these decentralized energy systems, accurate simulation models are needed. The objective of this research is to determine the photovoltaic potential of the Dutch general cargo inland shipping fleet.
A method is developed to simulate the energy yield of moving general cargo vessels. The estimated energy yield is based on hourly power calculations. A difference is made between container and bulk vessels. This simulation model consists of a skyline, a vessel, an irradiance, a PV module temperature, and an energy model. In the skyline model, skyline profiles for 3036 waterway points are generated using LIDAR AHN3 height data. The waterways skylines are corrected for every vessel individually. In the vessel model, AIS (automatic identification system) data is used to simulate the sailing behaviour of 2746 vessels. In the irradiance model, the diffused, direct and ground reflection irradiance received by the PV panel is simulated. The diffused irradiance is calculated according to the Perez model. The PV module temperature is estimated according to the fluid-dynamic model. The additional air caused by the forward movement of the vessel and the water temperature is taken into account. Finally, the PV energy yield is calculated by the received irradiance, the suitable PV surface and the corrected PV module efficiency.
An experiment is performed to validate the developed model, where a PV panel is installed on the vessel Harmonie. Equipment is installed to monitor Harmonie during one docking week and two sailing weeks. A co-variance linear regression model describes the relationship between the measured and the estimated PV power. According to the co-variance linear regression model, the P-value for the simulated PV power is below 0.05 and, therefore statistically significant. The outcome of the linear regression model is overestimated by 4 % with a 95 % confidence interval between 0.87 and 1.05.
740,958 PV panels can be installed on the general cargo fleet. Together, these panels have an installed peak power of 267 [MW] and an annual estimated PV potential of 230 [GWh]. The annual PV energy per unit area of a container vessel is 171 [kWh/m2 ] and for a bulk vessel 168 [kWh/m2 ]. The annual PV energy per installed power for a container vessel is 864 [Wh/Wp] and for a bulk vessel 852 [Wh/Wp]. When the outliers are removed from the annual specific power [Wh/Wp] dataset, the complete fleet can be modelled by a Weibull distribution. A t locationscale distribution is suggested when the outliers are not removed from the dataset. The average annual energy demand of a container vessel is 1440 [MWh], 7.17 % of this demand can be supplied by the PV panels installed on the vessel. Bulk vessels have an energy demand of 1350 [MWh] on average, of which the installed PV panels can cover 5.82 %.

In the port of Ostend a discharge sluice is going to be constructed as part of an enforced dike ring. The proposed location of this discharge sluice is close to an already existing marina, the Royal Yachtclub Oostende (RYCO), and hindrance is expected regarding the outflow of the sluice in marina direction. When designing the sluice, an optimal balance has to be found between acceptable flow velocities in the downstream area and the capacity of the discharge sluice. It is therefore very important to be able to determine resulting flow patterns.

Formulas and rules of thumb found in literature are not sufficient to determine the resulting flow pattern of this system due to the complex geometry, including a pile row for flow velocity reduction. Other studies have shown that numerical models could simulate flow patterns of discharge sluices with much detail. However, a lot of detail in the results also requires much computational time. An example of a detailed numerical software program that is able to simulate the complete three-dimensional flow field is COMSOL Multiphysics 5.6 (COMSOL). Although it provides the most detail, simulating the flow in the entire area of interest (including the RYCO) for a complete tidal cycle in COMSOL would take too much computational time.

The objective of the present study is therefore to investigate the possibilities of determining the flow pattern downstream of a discharge sluice using a numerical method that requires less computational time but has sufficient accuracy to determine the potential impact of a discharge sluice on nautical activities.

In the present study, two options are considered to determine the flow field downstream of a discharge sluice. Method COMSOL-D3D is a coupled numerical method of a COMSOL and a coarser Delft3D-FLOW 4 (D3D) model. The other option, method D3D, uses only a D3D model and the sluice outflow is schematized by means of the general discharge relation.

As validation of the results is not possible due to a lack of measurement data, the methods are applied to a simplified case. The flow pattern resulting from each method is compared to the results obtained with a so-called baseline method. This method consists of modelling the entire domain with only a detailed numerical model, COMSOL. This is possible since, for validation purpose, the domain of the simplified case is relatively small and only stationary conditions are considered.

In conclusion, there is a lot of potential in the use of both methods in predicting the flow pattern downstream of a discharge sluice. They produce for the simplified case flow patterns similar to those obtained with the detailed method. Moreover, both methods require relatively little computational time compared to a full 3D simulation, method D3D requires the least amount. However, there are a number of conditions for the application of both methods.

The methods cannot be applied in the direct vicinity of the discharge sluice where the flow is highly three-dimensional. If one is interested in the flow in the first meters after the outflow opening or around the pile row, for example for designing the bottom protection, the two considered options are not sufficiently accurate. The flow in this area is too complex to simulate in a D3D model. In this case it is recommended to model the situation completely in COMSOL or a model similar to COMSOL. Furthermore, method D3D can only be applied if the sluice system is simple enough to correctly determine the discharge coefficient analytically/empirically and to simulate the effect of the pile row with a simplification in D3D. It is possible to accurately determine the effect of the pile row on the flow in this study with a schematized porous plate in D3D. Further research must show whether this applies to all types of pile rows.

For method COMSOL-D3D it is important that a correct coupling is made between both models. Here it is important to gradually impose the flow rates in the D3D model. Furthermore, the coupling should be made before the predicted point at which the jets starts deflecting towards the side but downstream of the area at which three-dimensional flows caused by the pile row are present.

It is important to note that due to a lack of validation data there is an uncertainty in the results following from the model approaches. Further research and the use of validation data must show how accurate the results of the considered methods are.

In this research method D3D is applied to the Ostend case. It becomes clear that flow rates exceed predetermined limits for safe operation in the marina. This applies to the entire marina and a large part of the time that the discharge sluice is discharging in marina direction. Measures will therefore have to be taken to prevent this. It is recommended to use method COMSOL-D3D to investigate the optimization between flow velocities in the marina and the discharge capacity. This is due to the fact that the design of the discharge sluice is expected to become much more complex and as a result the discharge coefficient is no longer easy to determine using formulas from literature.

A ship lock scheduling model called LOSCO was developed in order to decrease passage times at locks with two parallel chambers. Three locks were chosen to model, as they are some of the busiest locks of the Netherlands. The Krammersluizen, the Sluizen Hansweert and the Kreekraksluizen. Passage times are not collected at the locks, therefore the model was compared to SIVAK. SIVAK is the standard model for research on locks at Rijkswaterstaat. To schedule vessels in locks three types of choices can be made. Every vessel needs to be assigned to a chamber, the initiation time of locking should be decided and the order of sailing in a chamber should bedefined. By optimising these choices, passage times can be decreased. Optimising is however not straightforward, as the problem is an instance of the job shop scheduling problem. No exact algorithm has been found to solve these problems in a practical amount of time. Therefore the challange is to find a balance in solution quality and the speed of the model when creating a lock scheduling model. In this thesis, four ideas to improve the lock scheduling model by Verstichel were researched. The first idea is to change the resolution of the timesteps. The gain in performance was however not found to weight up against the loss in solution quality for resolution to be useful. The second idea is to drop the first come, first serve constraint that Verstichel created. This idea was also not found to be effective. The third idea is to divide the scheduling problem up into chunks. This is called cut separation. Chunks of around 25 vessels were found to be effective. The fourth and last idea is that of a maximum waiting time. The maximum waiting time makes the performance of the model better and also makes the scheduling model fairer. Data from the year 2016 was used to schedule all lockings. The LOSCO model is effective for reducing the average assage time per vessel with about 3.9 ± 0.12 SE minutes for the Krammersluizen and 2.0 ± 0.14 SE minutes for the Sluizen Hansweert compared to the SIVAK model. At the Kreekraksluizen, the model could not find a solution, as the Kreekraksluizen are a lot busier than the other locks that were tested. At the Kreekraksluizen on average every 7.8 minutes a vessel arrives, whereas at the Krammersluizen and the Sluizen Hansweert respectively every 14.0 and 13.0 minutes a vessel arrives throughout the year. The LOSCO model is only better than SIVAK if the lock is relatively quiet. At the busiest time of the day, typically in the afternoon, SIVAK performs better. The models perform equal at inter arrival times of around 8
to 10 minutes. Optimisation on economical value of the vessels was found to be less effective than optimisation on time. Optimisation on time was also found to be fairer. The LOSCO model is a step ahead towards a practical lock scheduling model. In order to achieve a fully practical model, some simplifications need to be expanded. It is recommended to first improve the model before it is applied in practice, as the model is able to outperform SIVAK in some cases, but not in the busiest cases. After this some extra features can be implemented, such as the model dealing with vessel delays and locks with 3 chambers.

The seasonal Chindwin river (Myanmar) forms the central artery for transportation in the region. However, the dynamic river behaviour, archaic boat equipment and limited monitoring, yearly results in large numbers of grounding ships, causing injuries and economic losses. A pilot project involving CoVadem technology was initiated in the beginning of 2019 to benefit safer navigation on the Chindwin. CoVadem technology can chart the most up to date water depths by collecting under keel clearance measurements from the commercial fleet. The pilot project aims to increase safety on the Chindwin through sharing information about safe routes and forecasted water depths with captains sailing the river.

The added value of using CoVadem technology, however, is vulnerable to the number of participating vessels. A combination of morphological changes and the typical spatial spread of CoVadem data limits the extent of the navigation channel that can be derived from the data. With only a small part of the navigation channel known it is unclear where passing of other vessels is possible, where speeds should be adjusted due to e.g., bottlenecks and where shorter routes are present.

The objective of this research is to provide more information about the navigable area around CoVadem data in order to assist captains with navigation. During this research, two physics-based models have been developed, able to carry out this task. The ‘soil-model’ combines CoVadem data with assumptions about the maximum slope in the bed.
The ‘axi-symmetric model’ utilises CoVadem data, the axi-symmetric solution and assumptions about sand dunes and river banks in a model to estimate a navigable area.

Two ‘reliability indicators’ have been developed that can indicate areas of the river where the output of the axi-symmetric model is reliable in its estimate. One indicator is related to the channel stability, it is calculated from many years of satellite imagery. The other indicator is related to channel curvature.

To measure the performance of the models and the reliability indicators, two dimensionless performance indicators have been developed: the safety score and the channel coverage score. The models and the reliability indicators were consecutively tested and evaluated for four study cases located along the Chindwin river.

The results are promising. It follows from this research that navigation channel estimates around scarce CoVadem ship track data can substantially benefit from application of a physics-based model. The soil-model is very robust, but has limited added value for navigation. The axi-symmetric model increases the navigable width estimate around a single CoVadem trackline significantly O(100 m). The performance indicators can improve the reliability of the axi-symmetric model significantly.

This research combines Big Data, physics-based models and remote sensing in a not early demonstrated way: with models tailored for navigable area estimates and with measured data as the starting point. The correlation between axi-symmetric model reliability and remote sensing/curvature is, moreover, something that has not been demonstrated before. Finally, the two developed performance indicators show great promise for the evaluation of navigable area estimates. As such, this research adds to current advancements in (open-access) cross-platform data accumulation and utilisation.

Summary
In the twentieth century a substantial number of navigation locks were build in the Netherlands. The navigation locks built in that era mostly had a life span of 80 to 100 years. This means that in the coming years most of these locks will be in need of either renovation or replacement. Rijkswaterstaat, the administrator of most of the locks in the Netherlands, is likely to put out tenders for the renovation and replacement of these locks. Companies can subsequently submit a proposal for design and/or construction. This proposal includes a preliminary design of the lock. During this phase of the design multiple possible alternatives will be reviewed to check which of those satisfy the design requirements and how they will be implemented. The time available to create a proposal is limited and the preliminary design phase is time consuming due to the numerous design alternatives available. Therefore, several alternatives are often not elaborated, which already leads to converging in the design process in an early phase. When the time needed to create a design of the alternatives can be reduced, the converging can be postponed to the detailed design and more attention can be paid to out-of-the-box thinking during preliminary design. This time saving can be accomplished by the use of automation and parametric design. With parametric design all the possible alternatives can be calculated in a given range of parameters. After comparison of these generated alternatives a well-educated choice can be made for the direction in preliminary design. This research aims to optimize preliminary lock design in the tendering phase by creating a design-tool that automatically generates all plausible alternatives based on the Design Criteria and boundary conditions of the client.
Eventually, the complete preliminary lock design can be accomplished with a parametric design tool. However, an advantage of a design tool is that it can be composed of different building blocks which all design a specific part of the navigation lock. The basis of the parametric design tool should be a relevant part of the lock when looking at the total costs of this part, the design costs, the variety of options and the relation with other parts. The lock chamber is chosen as a starting point for the design tool. The lock chamber, like the lock head and the mechanics, has high investment costs and a high percentage of design costs. The lock chamber however, is in contrast to the other parts of the lock, not heavily dependent on other components. This makes it an interesting component to make a parametric design tool out of.
The parametric model, that has been developed as a part of this thesis, includes the commonly used chamber types for inland navigation locks. These common types are retaining wall structures with an under water concrete floor including sheet pile, combi and diaphragm walls, and a U-basin structure, which can be constructed in an open building pit or in a temporary retaining wall building pit.
When the design tool is finished, research is conducted on the behaviour of the alternatives in different circumstances. The effects of the main design criteria are examined. These include the length of the chamber, width, depth, water drop and local soil profile. These values are mostly depended on the vessels that need to be accommodated and the local circumstances. Next to that, the impact of different construction methods, the use of materials and the variation in unit prices are elaborated.
In this thesis the second Juliana lock near Gouda is used as reference case. For this case it has been found that a sheet pile wall structure is economically most attractive, followed by the U-basin with an open building pit, a diaphragm wall structure and least feasible a U-basin structure with a retaining wall building pit. It is concluded that the length of the lock does hardly impact the design of the alternatives. The width of the chamber does influence the dimensions of the structure. The floor has to be stronger for larger widths and, because of this, the diaphragm wall structure will become more attractive compared to the U-basin structures. The depth and water drop mainly impacts the dimensions of the walls. This, combined with the increased soil handling with open building pits, leads to decreasing interest in U-basin structures. When looking at the construction methods and materials, it can be noted that the length of the tension piles is especially important for the pile plan and the floor. The concrete strength for the U-basins is of importance, while the under water concrete strength and the casting method hardly influence the total costs.
When looking at environmental impact, the alternative with the least material use is most attractive, the sheet pile wall structure followed, by the diaphragm wall structure. The type of material does heavily influence the environmental impact. The U-basin structures have more environmental impact due to the construction of a larger temporary building pit. The environmental impact consists of three main components, the used materials, transport of these materials and the processes for the construction of the lock. For the U-basin structures the last two components have a large impact, while for the retaining wall structures the materials used have a large footprint.
The proposed CO2 taxes of € 0,05 an €0,20 per kg CO2 equivalent will lead to an average cost increase of respectively 4,5% and 16.5%. The impact per alternative differs, but does not influence the order of economical feasibility. As from a CO2 tax of € 0,68 per kg CO2 equivalent the diaphragm wall structure will become financially more attractive compared to the U-basin structure with open building pit.
Despite the calculated strengths and costs, each structure has its own advantages, disadvantages and limitations in construction, final use and maintenance. Special attention should be paid to these properties, since certain construction methods may be excluded by design requirements and uncertainties can lead to high risk. Further research could focus on steel sheet or combi piles constructed in bentonite excavations, resolving the limitations of vibrations during sheet pile construction and the uncertainties of diaphragm walls.

The main objective and secondary objective, which corresponds to the in-depth research are as follows:

(1) To develop guidelines for engineers to include salt marshes in hydraulic infrastructure projects, subject to the Building with Nature philosophy.
(2) To investigate the effect of grazing at salt marshes on the coastal protection property.

This research concerns the development of guidelines for engineers to include salt marshes in coastal protection projects, subject to the Building with Nature philosophy. The results include important design aspects for engineers and an analysis of trade-offs between biodiversity and the coastal protection property of salt marshes.

This research is based on a literature study about the natural and artificial development of salt marsh. Thereafter, the effect of grazing on wave attenuation is modelled by the numerical model SWAN (Simulating WAves Nearshore). The effect of vegetation and the bathymetry is analysed. The vegetation data results from a grazing experiment at the salt marsh at Noord Friesland Buitendijks, in the north of the Netherlands. The results from the literature study and the in-depth research are translated to important design aspects within the engineering design guidelines for salt marsh development.

The main conclusion of the in-depth research is that the effect of vegetation on wave attenuation is small compared to the effect of wave breaking and bottom friction. Moreover, the variations in wave attenuation between the different grazing strategies is also small. However, grazing could be used as an ecosystem management strategy, as from literature it follows that grazing positively affects biodiversity. Although the effect of biotic factors on wave energy dissipation is small, the presence of a salt marsh indirectly affects wave attenuation. The salt marsh vegetation enhances sediment accumulation, which leads to a higher bottom profile elevation compared to the bare foreshore. This should be substantiated by future research.