Predicting pressure coefficients of wing surface based on the transfer of spatial dependency.

Multi-conditional holographic pressure coefficients over a wing are crucial for wing design, and a wind tunnel test is an indispensable means to obtain this profile. However, it is resource-consuming to obtain wind tunnel data under different conditions and only a limited number of sensors can be placed on the wing model during one test, which results in sparse pressure coefficient data with distribution inconsistency across cross sections and conditions. Thus, how to obtain pressure coefficients of more cross sections or even the whole wing surface with multiple conditions from the distribution-inconsistent sensor data becomes a challenging problem. Therefore, a deep learning framework based on transfer learning is proposed in this paper, in which the spatial dependency captured by a long short-term memory model between the obtained multi-conditional sensor data is transferred to other cross sections with few-condition data on the wing. The results demonstrate that the proposed framework achieves high accuracy on the pressure coefficients prediction of distribution-inconsistent cross sections on wind tunnel test data, and thus improves data utilization and cuts costs by reducing wind tunnel tests under different design conditions. Our work proves the possibility of reconstructing the holographic flow field from sparse sensor data of wind tunnel tests and puts forward recommendations on the placement of sensors for achieving this goal. [ABSTRACT FROM AUTHOR]