Here we introduce AMGNET, a multi-scale graph neural network model based on Encoder-Process-Decoder structure for flow field prediction. Our model employs message passing of graph neural networks at different mesh graph scales. Our method has significantly lower prediction errors than the GCN ...
Given a design geometry space and a range of flow condition of interest, if we have enough data, it would be easy to predict flow field of an airfoil under a particular flow condition by building a prediction model as the method does in Ref. 10. However, collecting large-scale flow field...
It differs from other data-driven flowfield generators that it introducephysics knowledgeto enhance the predicting accuracy and generaliztion capability. It has been applied to the prediction of airfoil,wingand single expansion ramp nozzle (SERN). ...
Flow field prediction using deep learning is a promising method to provide a rich source of information for isolator operating state detection. A data-driven model is proposed for the prediction of the flow field in an isolator by fusion convolutional neural networks using measurements of the ...
(self, flow, mask): """ Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """ N, _, H, W = flow.shape mask = mask.view(N, 1, 9, 8, 8, H, W) mask = torch.softmax(mask, dim=2) up_flow = F.unfold(8 * flow, [3,3], padding=1) ...
At the same time, new arriving data are processed in real-time, and a more effective prediction system is explored in the field of dynamic training40, to further optimize the design of the traffic flow prediction model and improve its generalization ability....
The very first step in the simulation of ice accretion on a wind turbine blade is the accurate prediction of the flow field around it and the performance o... E Sagol,M Reggio,A Ilinca - 《Isrn Mechanical Engineering》 被引量: 13发表: 2012年 Numerical Simulation of Wind Turbine Performance...
Flow Prediction Problem MULTITASK DEEP LEARNING MDL框架,由三个组件组成,分别用于数据转换、节点流建模和边缘流建模 我们首先将地图上沿时间方向的轨迹(或行程)数据转换为两种类型的流 :i)节点流为张量时间有序序列(Step (1a)); ii)边流为图的时间有序序列(转移矩阵) (步骤(2a)),将其转化为张量序列(步骤(...
The purpose is to set-up a reliable methodology for the prediction of complex particle-laden two-phase flows at high mass loadings. The objectives are two-fold. On the one hand the suitability of the entire method to tackle practically relevant turbulent flows should be proven. On the other ...
Artificial neural network (ANN), inspired by the biological mechanism of nervous system of human beings [34], [35], [36], [37], has become one of the most classical and successful data-driven methods in the flow prediction field. In the ANN model, the neurons receive input information an...