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As a hydraulic lifting pipeline structure widely used in deep-sea oil, gas transportation, and sediment dredging projects, the pipeline configuration is related to the improvement of transportation efficiency and pipeline safety. Particularly, the bending section consumes the most energy and withstands severe erosion. Understanding and predicting the conveying characteristics of two-phase flow in bends is therefore crucial. In this study, CFD-DEM (computational fluid dynamics-discrete element method) simulation method is employed to calculate various cases, considering five parameters: pipeline bending radius and angle, conveying velocity, particle diameter, and concentration, to explore the influence of these parameters on pressure drop and erosion rate of pipeline and result in a data set of hundreds of cases. Based on this data set, seven machine learning models are trained to predict pressure drop and erosion rate, respectively. To enhance model accuracy, the stacking method in ensemble learning is employed to combine multiple models with good performance. Additionally, the Optuna and SHAP (SHapley Additive exPlanation) methods are utilized to optimize hyperparameters and explain the degree to which parameters impact the predictions. The result demonstrates that pressure drop is almost unaffected by bending radius, while erosion rate initially decreases and then increases with bending angle, and both increase with other parameters. Among the evaluated models, artificial neural network, XGBoost, and random forest all demonstrate high prediction accuracy. The stacking model further improves the accuracy, with mean absolute error improving by 21.7% and 32.2%, and the SHAP method demonstrated good interpretability, which is basically consistent with CFD-DEM results. [ABSTRACT FROM AUTHOR]

While it is challenging for a traditional propulsor to achieve a wide range of force profile manipulation and propulsion efficiency, nature provides a solution for a flapping foil such as that found in birds and turtles. In this paper, we introduce a deep reinforcement learning (DRL) algorithm with great potential for solving nonlinear systems during the simulation to achieve a self-learning posture adjustment for a flapping foil to effectively improve its thrust performance. With DRL, a brute-force search is first carried out to provide intuition about the optimal trajectories of the foil and also a database for the following case studies. We implement an episodic training strategy for intelligent agent learning using the DRL algorithm. To address a slow data generation issue in the computational fluid dynamics simulation, we introduce a multi-environment technique to accelerate data exchange between the environment and the agent. This method is capable of adaptively and automatically performing an optimal foil path planning to generate the maximum thrust under various scenarios and can even outperform the optimal cases designed by users. Numerical results demonstrate how the proposed DRL is powerful to achieve optimization and has great potential to solve a more complex problem in the field of fluid mechanics beyond human predictability. [ABSTRACT FROM AUTHOR]

Flow pattern and hydrodynamic characteristics of coarse particles in deep-sea hydraulic lifting pipes are simulated using a numerical approach developed by combining the computational fluid dynamics method with a discrete element method in the Euler–Lagrange framework. This paper examines the effects of feed concentration, two-phase flow initial mixture velocity, and particle gradation on the dynamic characteristics of particles and flow pattern in the pipe by validating the rationality of numerical simulation. The results demonstrate that particles are distributed from the pipe center to the pipe wall, while the lift force causes more particles to be distributed in the pipe center. Moreover, greater inertia makes large particles more concentrated in the center. Particle-induced turbulence modifies the lift force and causes particles to move from the pipe center to its wall. Due to the increasing trend of lift force, particles gather toward the center of the pipe at an increasing pace as the initial velocity increases. The greater the feed concentration, the more particles disperse toward the pipe wall due to the violent momentum exchange caused by the high concentration and the significantly altered lift force caused by the high turbulent kinetic energy resulting from a high concentration. From the particle gradation 1:1:1 to 1:6:1, the pressure drop decreases gradually as the reduction in small particles decreases the number of particles near the wall, and the frictional energy loss between the particles and the pipe wall decreases. [ABSTRACT FROM AUTHOR]