Abstract
Accurate aerodynamic distribution perception and real-time flight state evaluation are crucial for flight safety,e.g.,stall detection.However,the observations are usually sparse due to limitations in sensor mounting space and cost,and a re-construction technology is urgently required.Herein,a machine learning-assisted assimilation method based on sparse ob-servations has been proposed.Different from the traditional reconstruction methods focusing on boundary condition correction,the proposed method formulates the flow field pressure distribution as a linear superposition of flow field modes,thereby forming a real-time reconstruction pattern that combines offline modal extraction using computational fluid dynamics(CFD)with real-time determination of modal weights using a neural network.In this study,CFD simulations were conducted under 800 different operating conditions for common modal extraction and model training.The weights of these modes were determined online based on merely five observations for reconstructing the full pressure field.A pressure reconstruction with a relative error of 6.1％and a mean square error of 0.003 was achieved within the prescribed condition range.The computational cost was just 2 ms for each reconstruction run,significantly faster than the 20 min required by the classical reconstruction ensemble transform Kalman filter.It also showed that the method maintains almost the same accuracy amidst 1.5％measurement noise.As practical examples,shock waves and the change of lift coefficient were analyzed using the proposed method,providing remarkable evidence for the capability of the method in supporting stall detection.These validate the method's effectiveness and explore its potential in real-time and accurate monitoring of an aircraft.
基金项目
National Key R&D Program of China(2021YFB3200700)
National Science Foundation of China(52175510)
National Science Foundation of China(51925503)
National Science Foundation of China(52188102)
Hubei Provincial Natural Science Foundation of China(2023AFA085)
NETL
NSTL
万方数据