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.
翻译:在许多实际流体动态实验中,测量速度和压力等变量只能在数量有限的传感器位置或流动中的几个二维平面上进行。然而,了解整个领域是了解许多流动动态所必需的。从稀少的测量中深度学习重建完整的流动场最近引起了重要的研究兴趣,以克服这一限制。这项任务被称为流量重建任务。在本研究中,我们提议了一个基于神经网络模型,称为FR3D, 使FR能够用于在3D天体外挤出三维流,并带有任意截面。一个创新的绘图方法,即将多个流体区域绘制成一个废墟,使FR3D能够将其性能概括到培训期间未遇到的物体。我们用一套由80项培训和20项测试随机生成的地理模型来充分证明这种总体化能力。我们显示FR3D模型将压力和速度组件重新组合成几个百分点误差。此外,我们利用这些预测,精确地估算了地球升动力和升降场。