Physics-based energy estimation during the loading phase of a TSHD

Abstract

The dredging industry is a contributor to the greenhouse gas (GHG) emissions due to the energy-intensive process of dredging and transporting sediment. This industry has to reduce the GHG emissions due to future possible restricting regulations on GHG emissions. The dredging industry is shifting towards more environmentally friendly fuels, but designing new vessels which can run on these fuels will be a time consuming transformation. Though, the demand of dredging activities is growing, think of construction of offshore wind farms and creating flood defences against sea level rise. To minimize the costs due to the high GHG footprint in the short term, Van Oord should be able to estimate the energy consumption (GHG emissions) based on different dredging strategies.

Van Oord uses a model to estimate energy consumption that accurately describes the majority of dredging projects, based on the power installed, dredging time, and a power coefficient term. This power coefficient term is based on empirical data and may result in a less accurate estimation of more complex (new) projects such as offshore wind farms and cable dredging, which are the new opportunities arising for dredging companies. To better estimate energy consumption during these complex dredging operations, a physics-based and semi-empirical method is developed that can more accurately estimate energy consumption based on certain dredging strategies. Previous research already looked at the sailing phases of the dredging cycle, therefore the main objective of this thesis is:

To quantify the energy consumption of different TSHD dredging strategies based on physics for the loading phase.

To achieve this research objective, first a literature study is conducted to list the different physics-based methods available to estimate the energy consumption. The energy consumption of a TSHD is divided into five main energy consumers (propulsion system, dredge pumps, jet pumps, bow thrusters and board net) in which the energy of the individual components is described by a power and a time term. The time component is represented by the duration of the loading cycle, which in this study is limited to the filling process of the hopper until overflow. The power term of the jet- and dredge pumps have been estimated by the (adjusted) in-house physics-based methods of Van Oord. The board net and the bow thrusters have been estimated by data analysis of a case study. The goal of this research is to develop a method to estimate the required propulsion power and a tool to bring all methods together to simulate the total energy consumption during the loading phase based on different dredging strategies.

A model is developed to estimate the required propulsion power during loading. The model development is divided into three phases; The first phase describes the development of a semi-empirical physics based model. In this, the various resistance forces acting on the vessel, suction pipe, and draghead are calculated based on stationary parameters such as vessel dimensions, and project parameters. The project parameters, including water depth, trailing speed, and visor angle, are extracted by filtering the actual data from the case study. In this way the estimation model is set equal to a reference case. Finally, the output of the estimation model is calibrated by comparison with the actual data of the case study. This calibration phase is an iterative process in which the accuracy of the model is described and increased.

The model shows that it is possible to calculate the required propulsion power based on operational parameters, such as trailing speed. Furthermore, the model provides insight into the amount of resistance on the three components (draghead, suction pipe and vessel). The power curve follows a quadratic pattern for increasing trailing speed, which appears to be a reasonable estimate. When comparing the model to the actual data it can be seen that the model underestimates at lower trailing speed and over estimates at higher trailing speed. By finding correlations between visor angle and trailing speed the model is calibrated and seems to better fit the dataset, however the slope of the curve still has a large deviation compared to the regression line based on the actual dataset. Therefore, the model needs to be further developed before it can be included as an addition to the existing models within Van Oord. The static friction term is not included, which could compensate the underestimation of the model and the high cutting forces are the potential reason for the overestimation at high Van Oord Marine Ingenuity iii Delft University of Technology trailing speed. For now, the current developed model falls within the reference dataset for the trailing speed between 1.6 [kn] and 1.9 [kn], thus making it a reasonable first estimate of the required propulsion power.

The OpenCLSim python package, which can be used to simulate discrete events, is used to simulate the dredging processes of the loading phase. A plugin for this python package is developed to calculate the total energy consumption during these processes. The propulsion model is integrated (with the set limitations) in this plugin together with the four other power estimation methods (dredge pumps, jet pumps, bow thrusters and board net). The simulation tool runs based on four input characteristics: vessel parameters (TSHD), site characteristics, dredging strategy, and data describing the bow thrusters and board net. The output of the simulation includes required power, duration, and consumed energy. Additionally, the simulation estimates the fuel usage,
emissions, and project costs associated with energy consumption.

The main ability of the simulation tool is that it can visualize the energy consumption (and emissions) based on dredging strategies and location. This enables the prediction and potential reduction of emissions in sensitive areas, such as fine dust emissions near cities. By using this simulation tool, a dredging plan can be created based on dredging strategies (trailing speed) to reduce emissions in these sensitive areas. To better demonstrate the other capabilities of the developed plugin within the OpenCLSim Python package, the simulation tool is applied to an imaginary project called Barachi. This project has strict regulations that prohibit overflow and apply emission taxes. Two dredging strategies are compared based on the limitations of the developed propulsion model: trailing speeds of 1.6 [kn] and 1.9 [kn]. The results show that, first of all, the project duration will decrease with an increasing trail speed. Secondly, the increase in power has a greater effect on energy consumption than the decrease in project duration, meaning that energy consumption will increase. Since fuel use and corresponding emissions are linked to energy consumption, they will also increase. However, the cost analysis provides a different view. In this case, because the ship’s operating costs are governing, faster trailing is the most cost-efficient, even if an emission tax of 200 euros/ton CO2e is applied.

To conclude, by creating a model to estimate the propulsion power and by integrating this model in a simulation tool, it is possible to quantify the energy consumption of different TSHD dredging strategies based on physics for the loading phase. Subsequently, the simulation also provides insight into the fuel usage, carbon emissions, and costs of the project. Further development and validation of the propulsion model is needed to give a more accurate estimation of the required power. Firstly, the static friction component should be included in the estimation model which could potentially solve the underestimation at lower trailing speed. Secondly, a maximum trail speed limit should be set based on the available propulsion power with respect to the total power distribution of the vessel. This limit should be included in the simulation tool, since it is linked to the other power consumers and the operational parameters. Thirdly, the propulsion model can only be used in saturated sand projects. Other cutting models based on cutting of silt, clay and rock should be added in the propulsion model to be able to apply the model in these soil conditions. Finally, the use of the tool is limited to dredging projects where overflow is not allowed. The suction production and the settling process within the hopper should be added to expand the capabilities of the tool to projects where overflow is possible.