Fluid dynamics simulation is often repeated under varying conditions.This leads to a generation of large amounts of results,which are difficult to compare.To compare results under different conditions,it is effective ...Fluid dynamics simulation is often repeated under varying conditions.This leads to a generation of large amounts of results,which are difficult to compare.To compare results under different conditions,it is effective to overlap the streamlines generated from each condition in a single three-dimensional space.Streamline is a curved line,which represents a wind flow.This paper presents a technique to automatically select and visualize important streamlines that are suitable for the comparison of the simulation results.Additionally,we present an implementation to observe the flow fields in virtual reality spaces.展开更多
In nature,with the help of lateral lines,fish is capable of sensing the state of the flow field and recognizing the surrounding near-fleld hydrodynamic environment in the condition of weak light or even complete darkn...In nature,with the help of lateral lines,fish is capable of sensing the state of the flow field and recognizing the surrounding near-fleld hydrodynamic environment in the condition of weak light or even complete darkness.In order to study the application of lateral lines,an improved pressure distribution model was proposed in this paper,and the pressure distributions of the lateral line carrier under different working conditions were obtained using hydrodynamic simulations.Subsequently,a visualized pressure difference matrix was constructed to identify the flow fields under different working conditions.The role of the lateral lines was investigated from a visual image perspective.Instinct features of different flow velocities,flow angles and obstacle offset distances were mapped into the pressure difference matrix.Lastly,a four-layer Convolutional Neural Network(CNN)model was built as a recognition tool to evaluate the effectiveness of the pressure difference matrix method.The recognition results demonstrate that the CNN can identify the flow field state with 2 s earlier than the current time.Hence,the proposed method provides a new way to identify flow field information in engineering applications.展开更多
文摘Fluid dynamics simulation is often repeated under varying conditions.This leads to a generation of large amounts of results,which are difficult to compare.To compare results under different conditions,it is effective to overlap the streamlines generated from each condition in a single three-dimensional space.Streamline is a curved line,which represents a wind flow.This paper presents a technique to automatically select and visualize important streamlines that are suitable for the comparison of the simulation results.Additionally,we present an implementation to observe the flow fields in virtual reality spaces.
基金This research was supported by the National Science Foundation of China(No.61540010)Shandong Natural Science Foundation(No.ZR201709240210).
文摘In nature,with the help of lateral lines,fish is capable of sensing the state of the flow field and recognizing the surrounding near-fleld hydrodynamic environment in the condition of weak light or even complete darkness.In order to study the application of lateral lines,an improved pressure distribution model was proposed in this paper,and the pressure distributions of the lateral line carrier under different working conditions were obtained using hydrodynamic simulations.Subsequently,a visualized pressure difference matrix was constructed to identify the flow fields under different working conditions.The role of the lateral lines was investigated from a visual image perspective.Instinct features of different flow velocities,flow angles and obstacle offset distances were mapped into the pressure difference matrix.Lastly,a four-layer Convolutional Neural Network(CNN)model was built as a recognition tool to evaluate the effectiveness of the pressure difference matrix method.The recognition results demonstrate that the CNN can identify the flow field state with 2 s earlier than the current time.Hence,the proposed method provides a new way to identify flow field information in engineering applications.
