Abstract
A general super-resolution (SR) reconstruction strategy is proposed to address the super-resolution reconstruction of temperature fields from low-resolution coarse temperature field data using convolutional neural networks. Two deep learning (DL) models were applied to augment the spatial resolution of temperature fields. One is the classical super-resolution convolutional neural network, and the other is the novel multiple path super-resolution convolutional neural network (MPSRC). Three paths with and without a pooling layer are designed in the MPSRC to fully capture spatial distribution features of temperature. Numerical simulations of combustion in a strut scramjet combustor at various Mach numbers are carried out to establish a dataset for network training and testing. The corresponding high-resolution temperature fields were successfully reconstructed with remarkable accuracy. The reconstruction performances of those models were comprehensively investigated and compared with the bicubic interpolation method. The results demonstrated that both DL methods can greatly improve the super-resolution reconstruction accuracy and the MPSRC can provide a better reconstruction result with a lower mean square error and a higher peak signal-to-noise ratio.
Highlights
To achieve hypersonic flight, the concept of a scramjet engine was proposed in the late 1940s.1 As a critical component in the scitation.org/journal/adv scramjet engine, the supersonic combustor has been one of the research hotspots in hypersonic propulsion,2,3 and the supersonic combustion technology has always been the key development challenge for hypersonic propulsion systems.4,5 In a supersonic combustor, the fuel resident time is of the order of microseconds, which makes it difficult to achieve an efficient combustion
All the learnable parameters θ have been determined, the SR model super-resolution convolutional neural network (SRCNN) and the multiple path super-resolution convolutional neural network (MPSRC) neural network are ready for use, and all the following deep learning (DL) SR results are derived from these models
A numerical simulation dataset with Mach numbers from 2.0 to 2.5 was used to train and test these models
Summary
The concept of a scramjet engine was proposed in the late 1940s.1 As a critical component in the scitation.org/journal/adv scramjet engine, the supersonic combustor has been one of the research hotspots in hypersonic propulsion, and the supersonic combustion technology has always been the key development challenge for hypersonic propulsion systems. In a supersonic combustor, the fuel resident time is of the order of microseconds, which makes it difficult to achieve an efficient combustion. As a critical component in the scitation.org/journal/adv scramjet engine, the supersonic combustor has been one of the research hotspots in hypersonic propulsion, and the supersonic combustion technology has always been the key development challenge for hypersonic propulsion systems.. The fuel resident time is of the order of microseconds, which makes it difficult to achieve an efficient combustion. Because of the high-speed flow and turbulent burning in supersonic combustion, the flow and combustion characteristics in a supersonic combustor are very complicated.. To realize rapid fuel/air mixing, reliable ignition, and stabilized combustion, a cavity and a strut are commonly introduced into a supersonic combustor, which undoubtedly increases the complexity of the flow and combustion characteristics.. Many detection methods have been developed to obtain the precise parameters and other details of flow and combustion in a supersonic combustor Precise parameters including temperature are crucial to the organization of heat release distribution and the avoidance of dangerous conditions. Many detection methods have been developed to obtain the precise parameters and other details of flow and combustion in a supersonic combustor
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