Fish adaption behaviors in complex environments are of great importance in improving the performance of underwater vehicles. This work presents a numerical study of the adaption behaviors of self-propelled fish in complex environments by developing a numerical framework of deep learning and immersed boundary–lattice Boltzmann method (IB–LBM). In this framework, the fish swimming in a viscous incompressible flow is simulated with an IB–LBM which is validated by conducting two benchmark problems including a uniform flow over a stationary cylinder and a self-propelled anguilliform swimming in a quiescent flow. Furthermore, a deep recurrent Q-network (DRQN) is incorporated with the IB–LBM to train the fish model to adapt its motion to optimally achieve a specific task, such as prey capture, rheotaxis and Kármán gaiting. Compared to existing learning models for fish, this work incorporates the fish position, velocity and acceleration into the state space in the DRQN; and it considers the amplitude and frequency action spaces as well as the historical effects. This framework makes use of the high computational efficiency of the IB–LBM which is of crucial importance for the effective coupling with learning algorithms. Applications of the proposed numerical framework in point-to-point swimming in quiescent flow and position holding both in a uniform stream and a Kármán vortex street demonstrate the strategies used to adapt to different situations.
Recently, robots have assisted and contributed to the biomedical field. Scaling down the size of robots to micro/nanoscale can increase the accuracy of targeted medications and decrease the danger of invasive operations in human surgery. Inspired by the motion pattern and collective behaviors of the tiny biological motors in nature, various kinds of sophisticated and programmable microrobots are fabricated with the ability for cargo delivery, bio-imaging, precise operation, etc. In this review, four types of propulsion—magnetically, acoustically, chemically/optically and hybrid driven—and their corresponding features have been outlined and categorized. In particular, the locomotion of these micro/nanorobots, as well as the requirement of biocompatibility, transportation efficiency, and controllable motion for applications in the complex human body environment should be considered. We discuss applications of different propulsion mechanisms in the biomedical field, list their individual benefits, and suggest their potential growth paths.
This work presents a numerical study of the collective motion of two freely-swimming swimmers by a hybrid method of the deep reinforcement learning method (DRL) and the immersed boundary-lattice Boltzmann method (IB-LBM). An active control policy is developed by training a fish-like swimmer to swim at an average speed of 0.4 L/T and an average orientation angle of 0∘. After training, the swimmer is able to restore the desired swimming speed and orientation from moderate external perturbation. Then the control policy is adopted by two identical swimmers in the collective swimming. Stable side-by-side, in-line and staggered formations are achieved according to the initial positions. The stable side-by-side swimming area of the follower is concentrated to a small area left or right to the leader with an average distance of 1.35 L. The stable in-line area is concentrated to a small area about 0.25 L behind the leader. A detailed analysis shows that both the active control and passive self-organization play an important role in the emergence of the stable schooling formations, while the active control works for maintaining the speed and orientation in case the swimmers collide or depart from each other and the passive self-organization works for emerging a stable schooling configuration. The result supports the Lighthill conjecture and also highlights the importance of the active control.
Carbon dioxide (CO2) valorization to light olefins via sustainable energy input poses great industrial significance for the synthesis of key chemical feedstocks and reduces emission of this potent greenhouse gas. Solar energy, harnessed using light‐capturing catalytic materials, can negate external heat requirements for the energy‐intensive reaction. Presently, photothermal CO2‐Fischer–Tropsch synthesis (FTS)‐dedicated studies remain limited and are focused on the nonselective synthesis of C2+ hydrocarbons. A possible extension in catalyst design may be leveraged upon re‐examination of the better‐established thermal CO2‐FTS in conjunction with studies on photothermal FTS. To this end, herein, a narrative on the prominent chemical mechanisms and existing strategies for Fe‐based catalyst design within thermal CO2‐FTS as a foundation is established. Then, with the intent of regulating product selectivity, a gap in the adaptation of encapsulated structures involving zeolitic frameworks for CO2‐FTS is discussed. Next, current photothermal studies on C2+ hydrocarbon synthesis via FTS, CO2‐FTS, and relevant thermal‐assisted photocatalytic systems involving CO2 conversion are examined. Finally, the possible applications of structures encapsulated by porous media for boosting light utilization for photothermal CO2‐FTS are considered. Overall, the potential for the uptake of strategies aimed at producing multifunctional, light‐responsive future catalysts suitable for CO2‐FTS is explored.
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