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Summary A high‐order curvilinear hybrid mesh generation technique is developed for high‐order numerical method (eg, discontinuous Galerkin method) applications to improve the accuracy for problems with curve boundary. The grid generation technique is based on an improved radius basic function (RBF) approach by which the straight‐edge mesh is converted into high‐order curve mesh. Firstly, an initial straight‐edge mesh is prepared by traditional grid generation software. Then, high‐order interpolation points are inserted into the mesh entities such as edges, faces, and cells according to the final demand of mesh order. To preserve the original geometry, the inserted points on solid wall are then projected onto the CAD model using an open source tool “Open Cascade.” Finally, other inserted points in the field near the solid wall are moved to appropriate positions by the improved RBF approach to avoid tangled cells. If we use the original RBF approach, then the inserted points on the edge and face entities normal to the solid boundary in the region of boundary layer will move to improper positions. To overcome this problem, a weighting based on the local grid aspect ratio between normal direction and tangential direction is introduced into the baseline RBF approach. Three typical configurations are tested to validate the mesh generator. Meanwhile, a third‐order solution of subsonic flow over an analytical 3D body of revolution in the second International Workshop on High‐Order CFD Methods is supplied by a discontinuous Galerkin solver. These numerical tests demonstrate the potential capability of present technique for high‐order simulations of complex geometries.

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. K E Y W O R D Sdeep reinforcement learning, intelligent control, moving overlapping grid, multi-discipline coupling simulation method, obstacle avoidance, self-propelled fish swimming