Evaluating the role of draghead positioning in dredging performance using 3D CFD, mud sampling, and rheometry

• High-fidelity CFD simulations developed to optimize dredging operations’ efficiency. • Dual Herschel-Bulkley model accurately captures mud flow from Harwich Harbour samples. • Rheological model validated in OpenFOAM using experimental rheometry data. • CFD simulations assess dredging strategies, providing information on the rate of mass removal. This paper presents a framework for the modelling of dredging operations, by developing high-fidelity CFD simulations and recognising the highly complex rheology of mud. The goal is to understand the impact of draghead position on the dredging efficiency. The mud sampling procedure is described, followed by a detailed account of the rheometry experiments, rheological modelling, and statistical data reduction methods. In this work mud samples are obtained from 5 different locations for up to 4 different depths in Harwich Harbour in the United Kingdom. Various analyses are performed including analysis of constituents (sand, silt and organic matter) and bulk density. Rheometry tests are performed and after examining the existing empirical models a dual Bingham and Herschel-Bulkley model was chosen which fits the data well and accurately captures the observed mud’s flow behaviour. In addition, the dual Bingham and Herschel-Bulkley model is implemented in the OpenFOAM open-source CFD framework and thoroughly validated by simulating the rheometer cell and comparing the calculated torque directly with the experimental data. Then, large-scale CFD simulations are performed to investigate the flux of different mud layers as a function of the draghead operating depth, employing the dual Bingham and Herschel-Bulkley model. The assessment of various dredging strategies based on draghead depth is presented through analysis of CFD mud layer suction flux data for a stationary draghead. Based on these results, recommendations are made regarding the optimal operating depth relative to different mud layers, considering both economic and environmental factors, including fuel consumption.