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The flow and solidification characteristics of molten steel in thin slab continuous casting mold of medium Mn steel with section of 1 500 mm×90 mm were studied by numerical simulation. The melting and mixing behavior of reactive mold slag was analyzed. The results showed that in case of casting speed of 5 m/min and immersion depth of 300 mm， the temperature of the medium Mn steel fluctuated greatly around about a quarter of the width surface of the lower layer. Due to the slow heat transfer of the shell， the solidified shell of narrow face at the mold outlet was thin and its thickness was only 7.73 mm. The slag rim formed in the continuous casting process of medium Mn steel was slightly smaller in size and mainly consisted of the initial slag layer. The solid slag film was formed by mixing the initial layer and the reaction layer at different stages. The initial solidification of the shell was easy to be uneven due to the change of the slag ingredient in the upper part of the mold.

The examination of the lead–lag effect between different publication types, incorporating a temporal dimension, is very significant for assessing research. In this article, we introduce a novel framework to quantify the lead–lag effect between the research topics of conference papers and journal papers. We first identify research topics via the text‐embedding‐based topic modeling technique BERTopic, then extract the research topics of each time slice, construct and visualize the similarity matrix of topics to reveal the time‐lag direction and finally quantify the lead–lag effect by four proposed indicators, as well as by average influence topic similarity comparison maps. We conduct a detailed analysis of 19,166 bibliographic data for top conference papers and journal papers from 2015 to 2019 in the data mining field, calculate the similarity of topics obtained by BERTopic between each time slice divided by quarters. The results show that journal paper topics lag behind conference paper topics in the data mining field. The most significant lead–lag effect is 2.5 years, with approximately 33.45% of topics affected by this lag. The methodology presented here holds potential for broader application in the analysis of lead–lag effects across diverse research areas, offering valuable insights into the state of research development and informing policy decisions. This article is categorized under:Application Areas > Science and Technology [ABSTRACT FROM AUTHOR]

Dynamic modeling has beencapturing the attention for its fundamentality in precise locomotion analyses and control of underwater robots. In this article, we focused on the 3-D dynamic modeling and the experimental validation of multiple motion patterns of underwater robots in large-scale parameter space. A 3-D dynamic model of an active-tail-actuated robotic fish with a barycentre regulating mechanism was first built by combining Newton’s second law for linear motion and Euler’s equation for angular motion. Then, the model parameters were determined by 3-D computer-aided design software SolidWorks, HyperFlow-based computational fluid dynamics simulation, and grey-box model estimation method. Finally, both kinematic experiments with a prototype and numerical simulations were applied to validate the dynamic model’s accuracy mutually. Based on the dynamic model, multiple 3-D motions, including rectilinear motion, turning motion, surfacing motion, and spiral motion, were analyzed. The experimental and simulation results demonstrated the effectiveness of the proposed model in evaluating the trajectory, attitude, and motion parameters such as the velocity, turning radius, turning angular velocity of the robotic fish. [ABSTRACT FROM AUTHOR]

The self‐propelled fish maneuvering for avoiding obstacles under intelligent control is investigated by numerical simulation. The NACA0012 airfoil is adopted as the two‐dimensional fish model. To achieve autonomous cruising of the fish model in a complex environment with obstacles, a hydrodynamics/kinematics coupling simulation method is developed with artificial intelligence (AI) control based on deep reinforcement learning (DRL). The Navier–Stokes (NS) equations in the arbitrary Lagrangian–Eulerian (ALE) framework are solved by the dual‐time stepping approach, which is coupled with the kinematics equations in an implicit strong coupling way. Moreover, the moving mesh based on radial basis function and overset grid technology is taken to achieve a wide range of maneuvering. DRL is introduced into the coupling simulation platform for intelligent control of obstacle avoidance when the self‐propelled fish swimming. Three cases are tested to validate the novel approach, including the fish model maneuvering to avoid a single obstacle and double or multiple obstacles. The results indicate that the fish model can avoid obstacles in a complex environment under intelligent control. This work illustrates the possibility of producing navigation algorithms by DRL and brings potential applications of bionic robotic swarms in engineering. [ABSTRACT FROM AUTHOR]

In order to simulate the under control self-propelled swimming of bionic fishes, a coupling method of hydrodynamics/kinematics/motion-control is presented in this paper. The Navier-Stokes equations in the arbitrary Lagrangian-Eulerian framework are solved in parallel based on the computational domain decomposition to simulate the unsteady flow field efficiently. The flow dynamics is coupled with the fish dynamics in an implicit way by a dual-time stepping approach. In order to discretize the computational domain during a wide range maneuver, an overset grid approach with a parallel implicit hole-cutting technique is adopted and coupled with morphing hybrid grids around the undulation body. The motion control of the fish swimming is realized by a deep reinforcement learning algorithm, which makes the fish model choose proper undulation manner according to a specific purpose. By adding random disturbances in the training process of fish swimming along a straight line, a simplified two-dimensional fish model obtains the ability to swim along a specific trajectory. Then in subsequent tests, the two-dimensional fish model is able to swim along more complex curves with obstacles. Finally, the starting process of a three-dimensional tuna-like model is simulated preliminarily to validate the ability of the coupling method for three-dimensional complex configurations. The numerical results demonstrate that this study could be used to explore the swimming mechanism of fishes in complex environments and to guide how robotic fishes can be controlled to accomplish their tasks. [ABSTRACT FROM AUTHOR]