In many practical fluid dynamics experiments, measuring variables such as
velocity and pressure is possible only at a limited number of sensor locations,
or for a few two-dimensional planes in the flow. However, knowledge of the full
fields is necessary to understand the dynamics of many flows. Deep learning
reconstruction of full flow fields from sparse measurements has recently
garnered significant research interest, as a way of overcoming this limitation.
This task is referred to as the flow reconstruction (FR) task. In the present
study, we propose a convolutional autoencoder based neural network model,
dubbed FR3D, which enables FR to be carried out for three-dimensional flows
around extruded 3D objects with arbitrary cross-sections. An innovative mapping
approach, whereby multiple fluid domains are mapped to an annulus, enables FR3D
to generalize its performance to objects not encountered during training. We
conclusively demonstrate this generalization capability using a dataset
composed of 80 training and 20 testing geometries, all randomly generated. We
show that the FR3D model reconstructs pressure and velocity components with a
few percentage points of error. Additionally, using these predictions, we
accurately estimate the Q-criterion fields as well lift and drag forces on the
geometries.

提出了基于卷积自编码器的神经网络模型 FR3D，用于流体动力学中三维流场的重构，可推广到马鞍形的平面之外，能够以很少的误差重构出压力和速度组成，且能准确估计 Q-criterion 场以及提取几何物体的升力和阻力。

基于共形映射卷积自编码器的任意钝体非定常流三维重构和力估计

FR3D: Three-dimensional Flow Reconstruction and Force Estimation for Unsteady Flows Around Arbitrary Bluff Bodies via Conformal Mapping Aided Convolutional Autoencoders