Subsonic In-Flight Temperature and Pressure Measurements Using a Scramjet Inlet Flow Sensor

Kurtz, Joe; Aizengendler, Mark; Krishna, Yedhu; Walsh, Paul; Brown, Melrose; O’Byrne, Sean

doi:10.2514/1.j054207

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…A prototype was successfully tested on the ground for signal fluctuations and signal-to-noise ratio under the pressure, temperature, and vibration conditions expected during a hypersonic flight test. The design is significantly simpler than previous designs presented in the literature and was the first TDLAS sensor design used in a scramjet inlet during a flight test [6,7].…”

Section: Discussionmentioning

confidence: 99%

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Rugged, Scramjet Inlet Temperature and Velocity Sensor: Design and Ground Test

et al. 2016

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The design and testing of a gas temperature and velocity scramjet engine inlet sensor based on tunable diode laser absorption spectroscopy of oxygen is discussed. The robustness and stability of the optical design necessitated by the dynamic vibrational and thermal environment were balanced against the need for compact size and very high signalto-noise ratio in a low-pressure, high-altitude flight regime. Test results demonstrated very good mechanical and thermal stability and an adequate signal-to-noise ratio for altitudes up to 30 km with predicted O 2 absorbance peaks greater than the signal standard deviation by a factor of more than 3. Nomenclature I, I 0 = signal intensity with and without sample gas (arbitrary units) T= I∕I 0 , sample gas transmittance

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Section: Discussionmentioning

confidence: 99%

“…This paper concerns the design, development, and ground testing of a diode-laser-based oxygen sensor for this inlet flow. Companion papers describe the flight test results [6,7].…”

Section: Introductionmentioning

confidence: 99%

Rugged, Scramjet Inlet Temperature and Velocity Sensor: Design and Ground Test

et al. 2016

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“…This method is an autoregressive data-driven model based on data flow and its prediction interval, which has the characteristics of fast execution, large amount of data, and no need to classify outliers in advance. The authors in [16] briefly introduces the technical basis of NN and BPNN (backpropagation neural network). The principle and method of sensor fault diagnosis based on NN observer are discussed.…”

Section: Related Workmentioning

confidence: 99%

An Abnormal Signal Processing Method for Sensors Using Convolution Neural Network

Zhang

Shen

2022

Mobile Information Systems

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The combination of sensor and neural network (NN) is an important development direction for intelligent sensors. This paper discusses the application of NN in sensor signal processing, introduces the basic principles, and illustrates the application method with examples. Based on the analysis of sensor’s abnormal signal processing, a diagnostic scheme based on CNN is proposed. At each current moment, NN is trained by the latest historical dataset of fixed length to complete the forecast of the next moment. The confidence interval is determined by the model’s residual of NN. Moreover, this paper proposes a signal noise reduction and compression method of multisensor system using the CNN. A multisensor sequence of noisy output signal and target’s true value are used as samples for network training, and the trained network is tested with test samples. The effectiveness of this method is verified by simulation of several typical function approximations. The results strongly suggest the advantages of CNN method for sensor abnormal signal processing and provide a solid foundation for the reliable use of sensors in such type of problems.

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