Data assimilation method and application of shear stress transport turbulence model for complex separation of internal shock boundary layer flow

The uncertain factors in wind tunnel experiments, especially the interference effects, will cause the flow around the test model to differ from that under the free flow condition. The wind tunnel wall interference effects are challenging to be eliminated, especially in transonic flow regions where classical linear correction methods are ineffective. To address this issue, an efficient wind tunnel wall interference correction framework based on data assimilation is proposed for steady and shock buffet flows around the airfoils in the transonic region in this work. The ensemble Kalman filter (EnKF) is used to find the combination of inflow conditions, which minimizes the differences between the pressure coefficients measured in the wind tunnel experiment and those estimated by computational fluid dynamics (CFD). The polynomial chaos expansion method is used to propagate uncertainty in the forecast step of the EnKF in order to improve efficiency. Typical wind tunnel wall interference correction problems are studied, including two steady cases: the transonic flow around the Royal Aircraft Establishment 2822 (RAE2822) and the National Aerospace Laboratory 7301 (NLR7301) airfoils, and one unsteady case: the transonic shock buffet on the National Aeronautics and Space Administration supercritical (phase 2)-0714 (NASA SC(2)-0714) airfoil. It is demonstrated that with the corrected Mach number and angle of attack, the estimated CFD results are in better agreement with the measured experimental data than traditional linear methods and the computational efficiency is improved compared to the original EnKF.

Wind tunnel wall interference correction for transonic airfoils with data-reduced ensemble Kalman filter

Chen,

Wang,

2024

Advancing neural network-based data assimilation for large-scale spatiotemporal systems with sparse observations

Cai,

Fang,

Wang

2024

Data assimilation (DA) is a powerful technique for improving the forecast accuracy of dynamic systems by optimally integrating model forecasts with observations. Traditional DA approaches, however, encounter significant challenges when applied to complex, large-scale, highly nonlinear systems with sparse and noisy observations. To overcome these challenges, this study presents a new Neural Network-based Data Assimilation (DANet) model, specifically employing a Convolutional Long Short-Term Memory architecture. By leveraging the strengths of neural networks, DANet establishes the relationship among model forecasts, observations, and ground truth, facilitating efficient DA in large-scale spatiotemporal forecasting with sparse observations. The effectiveness of the DANet model is demonstrated through an initial case study of wind-driven oceanic flow forecasting, as described by a Quasi-Geostrophic (QG) model. Compared to the traditional Ensemble Kalman Filter (EnKF), DANet exhibits superior performance in cases involving both structured and unstructured sparse observations. This is evidenced by reduced Root Mean Square Errors (RMSEs) and improved correlation coefficients (R) and Structural Similarity Index. Moreover, DANet is seamlessly integrated with the QG model to operationally forecast vorticity and stream function in the long term, further confirming the accuracy and reliability of the DANet model. DANet achieves operational forecasting 60 times faster than EnKF, underscoring its efficiency and potential in DA advancement.

Multi-objective optimization of high Mach waverider based on small-sample surrogate model

Ma,

Jiang,

Guo

et al. 2024

Advancements have been achieved in the optimization of waverider designs with the aid of machine learning to expedite the design process. However, these approaches are hampered by the need for extensive sample sizes and susceptibility to becoming ensnared in local optima. This study undertakes a parametric design based on the wedge-derived, power-law-shaped waverider, increasing configuration diversity and creating a dataset with limited samples by calculating waverider geometry and aerodynamic parameters. At a Mach number of 10, a multi-objective optimization design is implemented using the Young's double-slit experiment-least squares support vector regression (YDSE-LSSVR) surrogate model in conjunction with improved congestion distance multi-objective particle swarm optimization algorithm, focusing on maximizing the lift-to-drag ratio and volumetric efficiency as much as possible. The results indicated that, under conditions of limited samples, the YDSE-LSSVR model outperforms standard models such as support vector regression, LSSVR, Kriging, and Polynomial Chaos Expansions-Kriging regarding prediction accuracy. The Pareto solutions for both concave and convex waveriders, obtained through multi-objective optimization, improve the lift-to-drag ratio by 17.36% and 21.70%, respectively, and increase the volumetric efficiency by 88.89% and 105.56%, in comparison to baseline configurations. In addition, the research examines the impact of various design parameters on the Pareto solutions. Finally, the study applies the K-means method to conduct a cluster analysis of the Pareto solutions, generating three-dimensional waverider configurations based on distinguished solutions from different clusters.