The prediction of deep-sea mining sediment plumes is essential for assessing and mitigating the environmental impacts on vulnerable deep-sea ecosystems. In this paper, the numerical simulation method is adopted to predict the sediment plume transportation. Fluid dynamics are governed by the incompressible Navier–Stokes equations, coupled with the Standard k–ε turbulence model to capture turbulent diffusion. The air–water free surface is tracked by a high-resolution Volume of Fluid (VOF) method. The pressure–velocity coupling utilizes the PISO algorithm. Sediment transport is governed by the advection–diffusion equation. The mathematical model is validated through experiments. There is a good consistency between the experiment results and the numerical results, which proves that the numerical method can be applied. The study calculates the diffusion range and characteristics of plumes under different free stream velocities, injection velocities and discharge densities. The results indicate that an increase in free stream velocity enhances the development of turbulence, but conversely restricts the expansion of the mixing zone between the plume and the ambient water. A greater injection velocity leads to a wider distribution range of the plume, while inhibiting the development of local turbulence. A higher plume discharge density results in a larger horizontal distribution range, while hindering the effective mixing between the plume and the ambient water body.

To address the urgent need for high-fidelity motion data for validating navigation algorithms for Unmanned Underwater Vehicles (UUVs), this paper proposes a data generation method based on a parametric motion model. First, based on the principles of rigid body dynamics and fluid mechanics, a decoupled six-degrees-of-freedom (6-DOF) Linear and Angular Acceleration Vector (LAAV) model is constructed, establishing a dynamic mapping relationship between the rudder angle and speed setting commands and motion acceleration. Second, a segmentation–identification framework is proposed for three-dimensional trajectory segmentation, integrating Gaussian Process Regression and Ordering Points To Identify the Clustering Structure (GPR-OPTICS), along with a Dynamic Immune Genetic Algorithm (DIGA). This framework utilizes real vessel data to achieve motion segment clustering and parameter identification, completing the construction of the LAAV model. On this basis, by introducing sensor error models, highly credible Inertial Measurement Unit (IMU) data are generated, and a complete attitude, velocity, and position (AVP) motion sequence is obtained through an inertial navigation solution. Experiments demonstrate that the AVP data generated by our method achieve over 88% reliability compared with the real vessel dataset. Furthermore, the proposed method outperforms the PSINS toolbox in both the reliability and accuracy of all motion parameters. These results validate the effectiveness and superiority of our proposed method, which provides a high-fidelity data benchmark for research on underwater navigation algorithms.