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Supercritical airfoil6/25/2023 ![]() To achieve reduced drag in the transonic phase, Whitcomb realized that the wing's pressure distribution must be modified to delay and weaken the shock wave created on the upper surface where the high-velocity flow decelerated to subsonic. Casting about for other research, he returned to the question of transonic drag, especially on wings. He built proposed models, but by 1962 he abandoned the project because of the intractable drag problem. In 1958 Whitcomb was named head of Langley's transonic aerodynamics branch, and he began working on a possible SST design. For his insight, Whitcomb won the Collier Trophy in 1954. This was rectified by re-sculpting the fuselage. Its impact on aircraft design was immediate: the prototype Convair YF-102, for example, was found not to be capable of exceeding the speed of sound in level flight. This became known as the area rule, which allowed a significant reduction in the drag felt by airplanes near the speed of sound. ![]() Since the wings could not be dispensed with in the actual case, the alternate to removing the "bulge" would be to decrease the fuselage's cross-section near the wings. After considering the sudden drag increase which a wing-fuselage combination experiences at somewhere around 500 mph (800 km/h), Whitcomb concluded that "the disturbances and shock waves are simply a function of the longitudinal variation of the cross-sectional area" – that is, the effect of the wings could be visualized as equivalent to a fuselage with a sort of midriff bulge whose frontal area was the same as that of the wings. Especially, our method obtains accurate prediction results over the shock area, indicating its superiority in conducting turbulent flow under high Reynolds number.After World War II, NACA research began to focus on near-sonic and low-supersonic airflow. The statistical results indicate the accurate and generalized performance of the proposed method in reconstructing and predicting flow fields around a supercritical airfoil. The proposed VAE network achieves compression of high-dimensional flow field data into ten representative features. Eventually, a composite network is adopted to connect the MLP and the decoder of VAE for predicting the flow fields given the airfoil. Afterward, the extracted features are incorporated into the dataset, followed by the mapping from the airfoil shapes to features via a multilayer perception (MLP) model. Specifically, the principal component analysis technique is adopted to realize feature reduction, aiming to obtain the optimal dimension of features in VAE. To begin with, a variational autoencoder (VAE) network is designed to extract representative features of the flow fields. In this study, a systematic method based on generative deep learning is developed to extract features for depicting the flow fields and predict the steady flow fields around supercritical airfoils. Effective access to obtain the complex flow fields around an airfoil is crucial in improving the quality of supercritical wings.
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