Reduced-Order Modeling of Two-Dimensional Supersonic Flows with Applications to Scramjet Inlets

被引:36
|
作者
Dalle, Derek J. [1 ]
Fotia, Matt L. [1 ]
Driscoll, James F. [1 ]
机构
[1] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA
关键词
HYPERSONIC VEHICLE;
D O I
10.2514/1.46521
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Control-oriented models of hypersonic vehicle propulsion systems require a reduced-order model of the scramjet inlet that is accurate to within 10% but requires less than a few seconds of computational time. To achieve this goal, a reduced-order model is presented, which predicts the solution of a steady two-dimensional supersonic flow through an inlet or around any other two-dimensional geometry. The model assumes that the flow is supersonic everywhere except in boundary layers and the regions near blunted leading edges. Expansion fans are modeled as a sequence of discrete waves instead of a continuous pressure change. Of critical importance to the model is the ability to predict the results of multiple wave interactions rapidly. The rounded detached shock near a blunt leading edge is discretized and replaced with three linear shocks. Boundary layers are approximated by displacing the flow by an empirical estimate of the displacement thickness. A scramjet inlet is considered as an example application. The predicted results are compared to two-dimensional computational fluid dynamics solutions and experimental results.
引用
收藏
页码:545 / 555
页数:11
相关论文
共 50 条
  • [31] Reduced-order modeling of unsteady flows without static correction requirement
    Behbahani-Nejad, M
    Haddadpour, H
    Esfahanian, V
    JOURNAL OF AIRCRAFT, 2005, 42 (04): : 882 - 886
  • [32] Reduced-Order Modeling of Low Mach Number Unsteady Microchannel Flows
    Issa, Leila
    Lakkis, Issam
    JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME, 2014, 136 (05):
  • [33] Reduced-Order Modeling of Steady and Unsteady Flows with Deep Neural Networks
    Barraza, Bryan
    Gross, Andreas
    AEROSPACE, 2024, 11 (07)
  • [34] Fast estimation of internal flowfields in scramjet intakes via reduced-order modeling and machine learning
    Brahmachary, Shuvayan
    Bhagyarajan, Ananthakrishnan
    Ogawa, Hideaki
    PHYSICS OF FLUIDS, 2021, 33 (10)
  • [35] Contemporary reduced-order modeling applications in nuclear science and engineering
    Alberti, Anthony L.
    Abdel-Khalik, Hany
    Haugh, Brandon
    Kramer, Boris
    Hu, Rui
    Palmer, Todd
    Transactions of the American Nuclear Society, 2020, 123 (01):
  • [36] Reduced-order modeling methods via bivariate discrete orthogonal polynomials for two-dimensional discrete state-delayed systems
    Zhao-Hong Wang
    Yao-Lin Jiang
    Kang-Li Xu
    Multidimensional Systems and Signal Processing, 2023, 34 : 227 - 248
  • [37] Reduced-order modeling methods via bivariate discrete orthogonal polynomials for two-dimensional discrete state-delayed systems
    Wang, Zhao-Hong
    Jiang, Yao-Lin
    Xu, Kang-Li
    MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING, 2023, 34 (01) : 227 - 248
  • [38] Reduced-order modeling of the two-dimensional Rayleigh-Benard convection flow through a non-intrusive operator inference
    Rocha, Pedro Roberto Barbosa
    Almeida, Joao Lucas de Sousa
    Gomes, Marcos Sebastiao de Paula
    Nogueira, Alberto Costa
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 126
  • [39] Reduced-order modeling of two-sided frictional interfaces
    Miller, Jason D.
    Quinn, D. Dane
    PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCE AND INFORMATION IN ENGINEERING CONFERENCE, VOL 1, PTS A-C, 2008, : 1619 - 1626
  • [40] On reduced-order modeling of gas-solid flows using deep learning
    Li, Shuo
    Duan, Guangtao
    Sakai, Mikio
    PHYSICS OF FLUIDS, 2024, 36 (03)