This paper proposes a classification of the concurrent sources of Cutter Suction Dredger (CSD) spillage as well as a pseudo-analytical model for a-priori computation of spillage rates due to high rotational velocity-induced flow. As of yet, in literature, no analytical models exist that describe spillage due to centrifugal advection. Based on work performed by Miedema (2017) and Nieuwboer (2018), a preliminarymodel is set up that includes most relevant cutting variables, or a simplification thereof. However, in this preliminary model, the axial pump effect described in Den Burger is not explicitly accounted for and the pressures exerted on the cutterhead contour are heavily simplified. An adaptation of a governing dimensionless velocity ratio proposed by Steinbusch et al. (1999) and Dekker et al. (2003) is used for model calibration using experimental data for sand from Miltenburg (1983) and rock from Den Burger (2003). Model parameters were identified for which sand spillage can be estimated within a 5 percentage point bandwidth of the experimental data. Moreover, the shape of the model plot appears to resemble that of the data for sand accurately, i.e. the model behaves as expected. The preliminary model is not capable of accurately estimating rock spillage rates over a wide range of mixture velocities. This inaccuracy may be ascribed to the concurrence of other spillage sources. The preliminary proposed model may not entirely capture the centrifugal effect of the cutterhead for larger grain sizes. Recommendations are given for further research.@en

Spillage is a problem for many dredging projects that make use of a Cutter Suction Dredge (CSD). In addition to higher energy consumption for delivering the targeted depth, spillage can lead to a variety of environmental issues. Equally important is the operator's uncertainty as to how much spillage can be expected when drafting a tender. For this reason, this paper presents a tool to predict spillage rates when cutting sand or rock named the Sand-Rock Cutting Spillage Model. The tool focusses on centrifugal advection and rapid redeposition of sediment, the most significant spillage mechanisms. Centrifugal advection is defined as spillage due to the centrifugal forces acting on disintegrated soil inside the cutter as described in the Sand Cutting Spillage Model of Werkhoven et al. (2018). Rapid redeposition is induced by low mixture velocities that cause suspended particles to settle back onto the bank before reaching the suction mouth. This paper derives an improved mathematical foundation for the Sand Cutting Spillage Model and describes five model mechanisms that more accurately capture a number of observed dynamics. First, to more accurately predict cutter head flows, the internal and external cutter head densities are included. Second, the addition of axial flow in the cutter is investigated. Third, a formulation for the effects of rapid redeposition is incorporated. Fourth, an enhanced representation of the geometry of the cutter allows for a more accurate spillage prediction for the cutting of sand. The tool results compare well with measurements of Miltenburg (1983). For the cutting of rock, Den Burger (2003) observed that the downscaled rock material in his experiment easily settled at the cutter bottom. To this end, the fifth contribution of the Sand-Rock Cutting Spillage Model is the introduction of a concentration difference for rapid redeposition flow in comparison to other flow terms. The five mechanisms underlying the Sand-Rock Cutting Spillage Model bring the model in good agreement with experimental data for rock spillage rates by Den Burger (2003).@en

Many models for slurry transport exist. Usually the models describe one of the 5 main flow regimes: fixed or stationary bed transport; sliding bed transport; heterogeneous transport; homogeneous transport and sliding flow transport. The transition between these flow regimes is seldom described, as well as the difference between spatial and delivered concentration and the behavior of graded sands or gravels. A framework is described connecting the different flow regimes. Additional models for; the limit deposit velocity, the holdup function, the bed height, the concentration distribution and graded sands and gravels, complete the framework. The framework is based on constant spatial volumetric concentration curves for uniform sands and gravels. The models for the flow regimes and the limit deposit velocity are based on energy considerations. By means of a holdup function, the constant spatial volumetric concentration curves can be transformed into constant delivered volumetric concentration curves. Curves for graded sands and gravels can be constructed by splitting the particle size distribution (PSD) into fractions and use the superposition principle on the resulting curves of each fraction. The framework is validated with many experiments from literature.@en

Production Engineers are asked to provide estimated productions and operational guidance for dredging plant that works in a rapidly changing and dynamic environment. These engineers use models to apply scientific principles and engineering practices. The models must have three properties; they must be tractable, discernable and informative. Tractability means the ability to quickly setup and run the model, as the engineer has to quickly evaluate multiple scenarios. Discernable models use parameters and inputs likely to be known to the engineers and eschew parameters that are unknowable or unimportant. By informative, we mean that the output of the model must provide a direct answer to the specific engineering question, provide other outputs that can be used as operational guidance, and to check and calibrate the model. To satisfy these requirements the models are generally analytical, based on fundamental or empirical equations that reflect the underlying physics. The paper contrasts such engineering models to those used in design engineering and to scientific models used for research purposes.@en

In dredging, sand and gravel are often transported through pipelines mixed with water. In order to determine the pumping power required, but also predict whether there will be a bed or not, the resistance (hydraulic gradient) and the Limit Deposit Velocity (LDV) have to be determined. The transport process is always an interaction between the pump and the pipeline resistance of the mixture transported. An important parameter is the LDV, the line speed above which there is no fixed or sliding bed. There are many models in literature to determine the resistance, expressed as the hydraulic gradient, and the LDV, but which models are best suited for the case considered. For the pipeline resistance models, the following categories can be distinguished: 2LM or 3LM sliding bed model, concentration distribution based models, semi-empirical heterogeneous flow regime models and homogeneous flow regime models. For the LDV about 25 models are compared, where most models are semi-empirical. The paper will give an overview of the most used models and show methods to compare these models and make a choice of the best suited model, the Delft Head Loss & Limit Deposit Velocity (DHLLDV) Framework.@en

The DHLLDV model consists of a framework to determine the resulting hydraulic gradient curve for spatial volumetric concentrations and uniform sands, consisting of parts of five flow regimes: fixed or stationary bed transport, sliding bed transport, heterogeneous transport, homogeneous transport and sliding flow transport. Each flow regime has its own physical and mathematical model. Once the resulting hydraulic gradient curve and the limit deposit velocity are determined, a holdup function enables the user to construct the delivered volumetric concentration curve and the bed height. The authors have developed an open source software library, written in the Python programming language, that implements the model. The paper will provide an overview of the software, how to download and test it, and how to incorporate it into a variety of types of software. Developing engineering software presents a number of challenges: tracking and managing the development process, ensuring the quality and correctness of the resulting software, and incorporating the software into existing engineering processes. The software is hosted in a Git software repository, to enable a transparent and traceable development process. @en