Choubey G, Devarajan Y, Huang W, Mehar K, Tiwari M, Pandey KM (2019) Recent advances in cavity-based scramjet engine – a brief review. Int J Hydrogen Energy 44(26):13895–13909
Choubey G, Pandey KM (2016) Effect of variation of angle of attack on the performance of two-strut scramjet combustor. Int J Hydrogen Energy 41(26):11455–11470
Choubey G, Devarajan Y, Huang W, Yan L, Babazadeh H, Pandey KM (2020) Hydrogen fuel in scramjet engines – A brief review. Int J Hydrogen Energy 45(33):16799–16815
Choubey G, Yadav PM, Devarajan Y, Huang W (2021) Numerical investigation on mixing improvement mechanism of transverse injection based scramjet combustor. Acta Astronaut 188:426–437
Li Z, Moradi R, Marashi SM, Babazadeh H, Choubey G (2020) Influence of backward-facing step on the mixing efficiency of multi microjets at supersonic flow. Acta Astronaut 175:37–44
Liu X, Moradi R, Manh TD, Choubey G, Li Z, Bach QV (2020) Computational study of the multi hydrogen jets in presence of the upstream step in a Ma=4 supersonic flow. Int J Hydrogen Energy 45(55):31118–31129
Pratt DT, Heiser WH (1993) Isolator-combustor interaction in a dual-mode scramjet engine. AIAA paper 1993–0358
Li N, Chang JT, Xu KJ, Yu DR, Bao W, Song YP (2017) Prediction dynamic model of shock train with complex background waves. Phys Fluids 29(11):116103
Ren H, Yuan H, Zhang J, Zhang B (2019) Experimental and numerical investigation of isolator in three-dimensional inward turning inlet. Aerosp Sci Technol 95:105435
Thillai N, Thakur A, Srikrishnateja K, Dharani J (2021) Analysis of flow-field in a dual mode ramjet combustor with boundary layer bleed in isolator. Propuls Power Res 10(1):37–47
Hutzel JR, Decker DD, Donbar JM (2011) Scramjet isolator shock-train leading-edge location modeling. AIAA Paper 2011–2223
Xing F, Ruan C, Huang Y, Fang XY, Yao YF (2017) Numerical investigation on shock train control and applications in a scramjet engine. Aerosp Sci Technol 60:162–171
Curran ET (2001) Scramjet engines: the first forty years. J Propuls Power 17(6):1138–1148
Matsuo K, Miyazato Y, Kim HD (1999) Shock Train and Pseudo Shock Phenomena in Internal Gas Flows. Prog Aerosp Sci 35(1):33–100
Lewis M (2010) X-51 scrams into the future. Aerosp Am 48(9):26–31
Mutzman R, Murphy S (2011) X-51 development: a chief engineer’s perspective. AIAA Paper 2011–0846
Chang JT, Li N, Xu KJ, Bao W, Yu DR (2017) Recent research progress on unstart mechanism, detection and control of hypersonic inlet. Prog Aerosp Sci 89:1–22
Hutzel JR, Decker DD, Cobb RG, King PI, Veth MJ, Donbar JM (2011) Scramjet isolator shock train location techniques. AIAA Paper 2011–402
Hutzel JR (2011) Scramjet isolator modeling and control. Dissertation, Air Force Institute of Technology
Abedi M, Askari R, Sepahi-Younsi J, Soltani MR (2020) Axisymmetric and three-dimensional flow simulation of a mixed compression supersonic air inlet. Propuls Power Res 9(1):51–61
Khani M, Esmaeelzade G (2017) Three-dimensional simulation of a novel rotary-piston engine in the motoring mode. Propuls Power Res 6(3):195–205
Bottarelli M, Zannoni G, Bortoloni M, Allen R, Cherry N (2017) CFD analysis and experimental comparison of novel roof tile shapes. Propuls Power Res 6(2):134–139
Ayed AH, Kusterer K, Funke HHW, Keinz J, Bohn D (2017) CFD based exploration of the dry-low-NOx hydrogen micromix combustion technology at increased energy densities. Propuls Power Res 6(1):15–24
Alam MMA, Setoguchi T, Matsuo S, Kim HD (2016) Nozzle geometry variations on the discharge coefficient. Propuls Power Res 5(1):22–33
Shih TIP, Ramachandran SG, Chyu MK (2013) Time-accurate CFD conjugate analysis of transient measurements of the heat-transfer coefficient in a channel with pin fins. Propuls Power Res 2(1):10–19
Abu-Farah L, Haidn OJ, Kau HP (2014) Numerical simulations of single and multi-staged injection of H2 in a supersonic scramjet combustor. Propuls Power Res 3(4):175–186
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Proceedings of the 25th international conference on neural information processing systems, vol 1.
Curran Associates Inc., New York, p 1097–1105
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the 28th IEEE conference on computer vision and pattern recognition, Boston, 7-12 June 2015
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y (eds) Proceedings of the 3rd international conference on learning representations, San Diego, 7-9 May 2015
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, Las Vegas, 27-30 June 2016
Hinton G, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97
Chen C, Seff A, Kornhauser A, Xiao J (2015) DeepDriving: learning affordance for direct perception in autonomous driving. In: Proceedings of the 2015 IEEE international conference on computer vision, Santiago, 7-13 December 2015
Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd annual meeting of the association for computational linguistics: system demonstrations, Baltimore, 23-24 June 2014
Li D, Qiu L, Tao K, Zhu J (2020) Artificial intelligence aided design of film cooling scheme on turbine guide vane. Propuls Power Res 9(4):344–354
Guo X, Li W, Iorio F (2016) Convolutional neural networks for steady flow approximation. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, August 2016. Association for Computing Machinery, New York, p 481–490
Ling J, Kurzawski A, Templeton J (2016) Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. J Fluid Mech 807:155–166
Lee S, You D (2017) Prediction of laminar vortex shedding over a cylinder using deep learning. arXiv:1712.07854v1
Liu Y, Wang Y, Deng L, Wang F, Liu F, Lu Y, Li S (2019) A novel in situ compression method for CFD data based on generative adversarial network. J Vis 22(1):95–108
Liu Y, Lu Y, Wang Y, Sun D, Deng L, Wang F, Lei Y (2019) A CNN-based shock detection method in flow visualization. Comput Fluids 184:1–9
Kong C, Chang JT, Li YF, Li N (2020) Flowfield reconstruction and shock train leading edge detection in scramjet isolators. AIAA J 58(9):4068–4080
Chen H, Guo MM, Tian Y, Le JL, Zhang H, Zhong FY (2022) Intelligent reconstruction of the flow field in a supersonic combustor based on deep learning. Phys Fluids 34(3):035128
Guo MM, Chen ED, Tian Y, Chen H, Le JL, Zhang H, Zhong FY (2022) Super-resolution reconstruction of flow field of hydrogen-fueled scramjet under self-ignition conditions. Phys Fluids 34(6):065111
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