A point cloud deep neural network metamodel method for aerodynamic prediction

Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamodel method is proposed in this paper.The 3D geometric data,corresponding to the object boundaries,are chosen as point clouds and a deep learning neural net-work metamodel fed by the point clouds is further established based on the PointNet architecture.This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities.With the proposed aerodynamic metamodel approach,the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain,which can maintain the boundary smoothness and allow the network to detect small changes between geometries.Moreover,the point clouds are easily accessible from 3D sensors.The point cloud deep learning neural network,which employs re-sampling technique,the spatial transformer network and the fully connected layer,is developed to predict the aerodynamic char-acteristics of 3D geometry.The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing.The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.

PREDICTION OF THE AERODYNAMIC PERFORMANCE OF HORIZONTAL AXIS WIND TURBINES IN CONDITION OF UNIFORM WIND

The classical momentum-blade element theory is improved by using the empirical formula while part of rotor blades enters into the turbulent wake state, and the performance of a horizontal-axis wind turbine (HAWT) at all speed ratios can be predicted. By using an improved version of the so-called secant method, the convergent solutions of the system of two-dimensional equations concerning the induced velocity factors a and a' are guaranteed. Besides, a solving method of multiple solutions for a and a' is proposed and discussed. The method provided in this paper can be used for computing the aerodynamic performance of HAWTs both ofrlow solidity and of high solidity. The calculated results coincide well with the experimental data.

Fast prediction of aerodynamic noise induced by the flow around a cylinder based on deep neural network

Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics.The conventional prediction methods based on numerical simulation often demand huge computational resources,which are difficult to balance between accuracy and efficiency.Here,we present a data-driven deep neural network(DNN)method to realize fast aerodynamic noise prediction while maintaining accuracy.The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions.Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory,a dataset composed of 1216samples is established.With reference to the deep learning method,a DNN framework is proposed to map the relationship between spatial coordinates,inlet velocity and overall sound pressure level.The root-mean-square-errors of prediction are below 0.82 dB in the test dataset,and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation.This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.