In the past couple of years, the interest of the fluid mechanics community for deep
reinforcement learning techniques has increased at fast pace, leading to a growing …

The swimming of aquatic animals and flying of insects and birds have fascinated physicists
and biologists for more than a century. In this regard, great efforts have been made to …

Reinforcement learning is applied to the development of control strategies in order to reduce
skin friction drag in a fully developed turbulent channel flow at a low Reynolds number …

Conventional approaches to studying fish kinematics pose a great challenge for the real‐
time monitoring of fish motion kinematics. Here, a multifunctional fish‐wearable data …

This work presents the wall-modeled large eddy simulation (WMLES) in the immersed
boundary-lattice Boltzmann method (IB-LBM). Here, the wall model with both diffusive-and …

Model-based reinforcement learning (MBRL) is believed to have much higher sample
efficiency compared with model-free algorithms by learning a predictive model of the …

This paper presents a study on streamline penetration, velocity error, and consequences of
a fluid–structure interaction (FSI) solver based on the feedback immersed boundary method …

The lattice Boltzmann method (LBM) has emerged as a prominent technique for solving fluid
dynamics problems due to its algorithmic potential for computational scalability. We …

Fluid-structure interaction (FSI) is a very common physical phenomenon which extensively
exists in nature, human daily life and many engineering applications. The lattice Boltzmann …

The bionic propulsion has garnered increasing attention in both scientific and industrial
communities of underwater bionic vehicles on account of its higher efficiency, less noise …