The runaway oscillation process of the pump-turbine in a high head pumped-storage power plant is usually unstable. The root cause of its instability is still unclear. In this paper, its internal mechanism and the improvement method were studied in depth. First, the flow characteristics in a model pump-turbine during the runaway process at four guide-vane openings (GVOs) were investigated by 3D transient numerical simulations. Then, the energy dissipation characteristics of different types of backflow vortex structures (BFVSs) occurring at the runner inlet and their impacts on the runaway stability were investigated by the entropy production theory. The results show that the location change of BFVSs between the hub side and the mid-span of the runner inlet around the no-load point leads to the sharp change in the energy dissipation rate, which makes the slope of dynamic trajectory positive and the runaway oscillation self-excited. If the occurrence of BFVSs at the hub side is suspended, the runaway process will be damped. Finally, the pump-turbine runner was improved to obtain a wider stable operating range.

The autonomous swimming of fish in a complex flow environment is a nonlinear and intricate system, which is the focus and challenge in various fields. This study proposed a novel simulation framework for artificial intelligence fish. It employed a high-precision immersed boundary-lattice Boltzmann coupling scheme to simulate the interactions between fish and flow in real time, and utilized the soft actor-critic (SAC) deep reinforcement learning algorithm for fish brain decision-making module, which was further divided into a vision-based directional navigation and a lateral line-based flow perception modules, each matched with its corresponding macro-action space. The flow features were extracted using a deep neural network based on a multi-classification algorithm from the data perceived by the lateral line and were linked to the fish actions. The predation swimming and the various Kármán gait swimming were explored in terms of training, simulation, and generalization. Numerical results demonstrated significant advantages in the convergence speed and training efficiency of the SAC algorithm. Owing to the closed-loop “perceive-feedback-memory” mode, intelligent fish can respond in real-time to changes in flow fields based on reward-driven requirements and experience, and the accumulated experience can be directly utilized in other flow fields, and its adaptability, model training efficiency, and generalization were substantially improved.

Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels.

The transient behaviour of the pump-turbine in load rejection is very complex, and slight changes in runner blade shape may have great influence on it. To understand the underlying flow mechanisms, the load rejection processes of two model pump-turbines with the same specific-speed but different in the runner blade inlet shape were investigated by a three-dimensional numerical simulation method. The results show that the lean angle of the runner blade leading edge impacts the evolution of backflows at the runner inlet. The backflows first emerge at the shroud side for the runner with a negative lean angle, and the backflows first emerge at the hub side for the runner with no lean angle. However, the backflows at the shroud side make more imbalanced flows in the runner passages and higher increase of pressure fluctuations in the vaneless space. As a consequence, greater radial force fluctuations on the runner blades are formed.

The reef ecosystem plays a vital role as a habitat for fish species with limited swimming capabilities, serving not only as a sanctuary and food source but also influencing their behavioral tendencies. Understanding the intricate mechanism through which fish adeptly navigate the moving targets within reef environments within complex water flow, all while evading obstacles and maintaining stable postures, has remained a challenging and prominent subject in the realms of fish behavior, ecology, and biomimetics alike. An integrated simulation framework is used to investigate fish predation problems within intricate environments, combining deep reinforcement learning algorithms (DRL) with high-precision fluid-structure interaction numerical methods-immersed boundary lattice Boltzmann method (lB-LBM). The Soft Actor-Critic (SAC) algorithm is used to improve the intelligent fish’s capacity for random exploration, tackling the multi-objective sparse reward challenge inherent in real-world scenarios. Additionally, a reward shaping method tailored to its action purposes has been developed, capable of capturing outcomes and trend characteristics effectively. The convergence and robustness advantages of the method elucidated in this paper are showcased through two case studies: one addressing fish capturing randomly moving targets in hydrostatic flow field, and the other focusing on fish counter-current foraging in reef environments to capture drifting food. A comprehensive analysis was conducted of the influence and significance of various reward types on the decision-making processes of intelligent fish within intricate environments.

Three-Dimensional Simulation of Balloon Dynamics by the Immersed Boundary Method Coupled to the Multiple-Relaxation-Time Lattice Boltzmann Method - Volume 17 Issue 5 - Jiayang Wu, Yongguang Cheng, Chunze Zhang, Wei Diao