A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network

In a myriad of engineering situations, we often hope to establish a model which can acquire load conditions around structures through flow features detection. A data-driven method is developed to predict the pressure on a cylinder from velocity distributions in its wake flow. The proposed deep learning neural network is constituted with convolutional layers and fully-connected layers: The convolutional layers can process the velocity information by features extraction, which are gathered by the fully-connected layers to obtain the pressure coefficients. By comparing the output data of the typical network with Computational Fluid Dynamics (CFD) results as reference values, it suggests that the present convolutional neural network (CNN) is able to predict the pressure coefficient in the vicinity of the trained Reynolds numbers with various inlet flow profiles and achieves a high overall precision. Moreover, a transfer learning approach is adopted to preserve the feature detection ability by keeping the parameters in the convolutional layers unchanged while shifting parameters in the fully-connected layers. Further results show that this transfer learning network has nearly the same precision while significantly lower cost. The active prospects of convolutional neural network in fluid mechanics have also been demonstrated, which can inspire more kinds of loads prediction in the future

A semi-implicit discrepancy model of Reynolds stress in a higher-order tensor basis framework for Reynolds-averaged Navier-Stokes simulations

With the rapid development of artificial intelligence, machine learning algorithms are becoming more widely applied in the modification of turbulence models. In this paper, with the aim of improving the prediction accuracy of the Reynolds-averaged Navier-Stokes (RANS) model, a semi-implicit treatment of Reynolds stress anisotropy discrepancy model is developed using a higher-order tensor basis. A deep neural network is constructed and trained based on this discrepancy model. The trained model parameters are embedded in a computational fluid dynamics solver to modify the original RANS model. Modification computations are performed for two cases: one interpolation and one extrapolation of different Reynolds numbers. For these two cases, the ability of the modified model to capture anisotropic features has been improved. Moreover, when compared with the mean velocity of large eddy simulations (LES), the root mean square error of the modified model is significantly lower than the original RANS model. Meanwhile, the modified model can better simulate flow field separation and fluctuation in the shear layer and has better prediction accuracy for the reattachment point and the mean velocity profile compared with the original RANS model. In addition, the modified model also improves the prediction accuracy for the mean pressure coefficient and mean friction coefficient of the underlying wall surface. The previously trained model is also directly performed for the modification computation of the two massive separation periodic hill flows. It is shown that the results simulated by the modified model and LES approach are more consistent in both trend and magnitude than the original RANS model and LES approach