The design objectives of modern aircraft engines include high load capacity,efficiency,and stability.With increasing loads,the phenomenon of corner separation in compressors intensifies,affecting engine performance an...The design objectives of modern aircraft engines include high load capacity,efficiency,and stability.With increasing loads,the phenomenon of corner separation in compressors intensifies,affecting engine performance and stability.Therefore,the adoption of appropriate flow control technology holds significant academic and engineering significance.This study employs the Reynolds-averaged Navier-Stokes(RANS)method to investigate the effects and mechanisms of active/passive Co-flow Jet(CFJ)control,implemented by introducing full-height and partial height jet slots between the suction surface and end wall of a compressor cascade.The results indicate that passive CFJ control significantly reduces the impact of corner separation at small incidence,with partial-height control further enhancing the effectiveness.The introduction of active CFJ enables separation control at large incidence,improving blade performance under different operating conditions.Active control achieves this by reducing the scale of corner separation vortices,effectively reducing the size of the separation region and enhancing blade performance.展开更多
Predicting the external flow field with limited data or limited measurements has attracted long-time interests of researchers in many industrial applications.Physics informed neural network(PINN)provides a seamless fr...Predicting the external flow field with limited data or limited measurements has attracted long-time interests of researchers in many industrial applications.Physics informed neural network(PINN)provides a seamless framework for combining the measured data with the deep neural network,making the neural network capable of executing certain physical constraints.Unlike the data-driven model to learn the end-to-end mapping between the sensor data and high-dimensional flow field,PINN need no prior high-dimensional field as the training dataset and can construct the mapping from sensor data to high dimensional flow field directly.However,the extrapolation of the flow field in the temporal direction is limited due to the lack of training data.Therefore,we apply the long short-term memory(LSTM)network and physics-informed neural network(PINN)to predict the flow field and hydrodynamic force in the future temporal domain with limited data measured in the spatial domain.The physical constraints(conservation laws of fluid flow,e.g.,Navier-Stokes equations)are embedded into the loss function to enforce the trained neural network to capture some latent physical relation between the output fluid parameters and input tempo-spatial parameters.The sparsely measured points in this work are obtained from computational fluid dynamics(CFD)solver based on the local radial basis function(RBF)method.Different numbers of spatial measured points(4–35)downstream the cylinder are trained with/without the prior knowledge of Reynolds number to validate the availability and accuracy of the proposed approach.More practical applications of flow field prediction can compute the drag and lift force along with the cylinder,while different geometry shapes are taken into account.By comparing the flow field reconstruction and force prediction with CFD results,the proposed approach produces a comparable level of accuracy while significantly fewer data in the spatial domain is needed.The numerical results demonstrate that the proposed approach with a specific deep neural network configuration is of great potential for emerging cases where the measured data are often limited.展开更多
基金National Science&Technology Major Project(Grant No.2017-II-0004-0016)National Nature Science Foundation of China(Grant No.52176044)。
文摘The design objectives of modern aircraft engines include high load capacity,efficiency,and stability.With increasing loads,the phenomenon of corner separation in compressors intensifies,affecting engine performance and stability.Therefore,the adoption of appropriate flow control technology holds significant academic and engineering significance.This study employs the Reynolds-averaged Navier-Stokes(RANS)method to investigate the effects and mechanisms of active/passive Co-flow Jet(CFJ)control,implemented by introducing full-height and partial height jet slots between the suction surface and end wall of a compressor cascade.The results indicate that passive CFJ control significantly reduces the impact of corner separation at small incidence,with partial-height control further enhancing the effectiveness.The introduction of active CFJ enables separation control at large incidence,improving blade performance under different operating conditions.Active control achieves this by reducing the scale of corner separation vortices,effectively reducing the size of the separation region and enhancing blade performance.
基金supported by the National Natural Science Foundation of China(Grant Nos.52206053,52130603)。
文摘Predicting the external flow field with limited data or limited measurements has attracted long-time interests of researchers in many industrial applications.Physics informed neural network(PINN)provides a seamless framework for combining the measured data with the deep neural network,making the neural network capable of executing certain physical constraints.Unlike the data-driven model to learn the end-to-end mapping between the sensor data and high-dimensional flow field,PINN need no prior high-dimensional field as the training dataset and can construct the mapping from sensor data to high dimensional flow field directly.However,the extrapolation of the flow field in the temporal direction is limited due to the lack of training data.Therefore,we apply the long short-term memory(LSTM)network and physics-informed neural network(PINN)to predict the flow field and hydrodynamic force in the future temporal domain with limited data measured in the spatial domain.The physical constraints(conservation laws of fluid flow,e.g.,Navier-Stokes equations)are embedded into the loss function to enforce the trained neural network to capture some latent physical relation between the output fluid parameters and input tempo-spatial parameters.The sparsely measured points in this work are obtained from computational fluid dynamics(CFD)solver based on the local radial basis function(RBF)method.Different numbers of spatial measured points(4–35)downstream the cylinder are trained with/without the prior knowledge of Reynolds number to validate the availability and accuracy of the proposed approach.More practical applications of flow field prediction can compute the drag and lift force along with the cylinder,while different geometry shapes are taken into account.By comparing the flow field reconstruction and force prediction with CFD results,the proposed approach produces a comparable level of accuracy while significantly fewer data in the spatial domain is needed.The numerical results demonstrate that the proposed approach with a specific deep neural network configuration is of great potential for emerging cases where the measured data are often limited.
