Abstract
The pursuit of high-resolution flow fields is meaningful for the development of hypersonic technology. Flow field super-resolution (SR) based on deep learning is a novel and effective method to provide HR flow fields in a scramjet isolator. Single-path and multiple-path network models based on convolutional neural networks (CNNs) have been developed to augment the spatial resolution of the experimental supersonic flow field. The single-path model uses a simple convolutional layer and fully connected layer serial architecture, and the multiple-path model increases the branch path by adding pooling layers to achieve a fusion structure architecture. Ground experiments of flow in a supersonic isolator at various working conditions are conducted to establish an experimental dataset. The trained single-path and multiple-path CNNs are compared with the traditional interpolation method on the flow field SR reconstruction accuracy. The results demonstrated that single-path CNNs have certain learning ability, but the SR accuracy is not satisfactory; multiple-path CNNs significantly improve the accuracy of flow field SR, and the multiple-path CNN with one branch path achieves the best SR performance.
Highlights
In the late 1940s, the concept of scramjet was proposed, which made hypersonic flight in the atmosphere possible.1,2 As an important component of the scramjet, the supersonic isolator is always a hot issue in hypersonic propulsion research,3,4 and supersonic flow control technology is always the focus of the hypersonic propulsion systems.5 Without rotating parts, the compression is completely scitation.org/journal/adv controlled by the shock waves in the supersonic scramjet inlet and isolator
In the study by Dong et al.,33 a simple convolutional neural networks (CNNs) is trained to regress the mapping between low-resolution data and high-resolution data
The fusion architecture can scitation.org/journal/adv improve the expressive ability of the CNN and make it have a stronger learning ability,35 which should help improve the accuracy of flow field super-resolution
Summary
In the late 1940s, the concept of scramjet was proposed, which made hypersonic flight in the atmosphere possible. As an important component of the scramjet, the supersonic isolator is always a hot issue in hypersonic propulsion research, and supersonic flow control technology is always the focus of the hypersonic propulsion systems. Without rotating parts, the compression is completely. There are numerous research studies on the rapid acquisition technology of flow field data, and how to obtain high-density flow data with less time and computational resources has become a new research hotspot in fluid mechanics.. There are numerous research studies on the rapid acquisition technology of flow field data, and how to obtain high-density flow data with less time and computational resources has become a new research hotspot in fluid mechanics.25–27 From another point of view, deep learning (DL)-based flow field super-resolution (SR) technology for reconstructing high-resolution flow fields from lowresolution (LR) flow fields provides promising data mining tools for high-density and high-precision acquisition of complex flow fields. The SR reconstruction performance of those models is deeply analyzed
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