![]() ![]() Yang C, Yang X, Xiao X (2016) Data-driven projection method in fluid simulation. ![]() Maulik R, Sharma H, Patel S, Lusch B, Jennings E (2019) Accelerating RANS turbulence modeling using potential flow and machine learning. Liu W, Fang J (2019) Iterative framework of machine-learning based turbulence modeling for Reynolds-averaged Navier–Stokes simulations. Zhu L, Zhang W, Kou J, Liu Y (2019) Machine learning methods for turbulence modeling in subsonic flows around airfoils. Ling J, Kurzawski A, Templeton J (2016) Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. In: 22nd AIAA computational fluid dynamics conference, p 2460 Zhang ZJ, Duraisamy K (2015) Machine learning methods for data-driven turbulence modeling. Milano M, Koumoutsakos P (2002) Neural network modeling for near wall turbulent flow. We achieve almost four orders of speed-up with a much cheaper computational resource.įielding JP (2017) Introduction to aircraft design, vol 11. Also, significant speed-up is achieved compared to time-consuming CFD simulations. Overall, our model achieves 88 \(\%\) accuracy for unseen airfoil shapes and shows promise to capture the overall flow pattern accurately. We also investigated the effect of the shock on the performance of our model. Performance analysis for airfoils with different thicknesses and cambers is conducted. Experiments are conducted with unseen airfoil shapes to evaluate the predictive capability of our model. For the better shape learning, a distance map is generated from airfoil shape and provided to the algorithm at data locations of the pressure points relative to the airfoil shape. Pressure data are calculated using CFD methods on high-quality structured computational grids. The calculated pressure field, which is the main parameter for lift and drag calculations, is fed to the neural network training algorithm. Reynolds averaged Navier Stokes-based CFD simulations are performed at a selected single Mach number and for an angle of attack condition. Network training and evaluation are performed from a set of computational fluid dynamics (CFD)-based solutions of the 2-D flow field around a group of known airfoils involving symmetrical, cambered, thick and thin airfoils. The developed tool is one of the early steps of a machine-learning-based aerodynamic performance prediction tool. In this study, we propose an encoder–decoder convolutional neural network-based approach for estimating the pressure field around an airfoil.
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