XU Jia, CAI Jinsheng, LIU Qiuhong. Numerical investigation of jet interactions and optimization design of drag reduction for the afterbody of jet aircraft[J]. ACTA AERODYNAMICA SINICA, 2014, 32(1): 38-44. DOI: 10.7638/kqdlxxb-2012.0061
Citation: XU Jia, CAI Jinsheng, LIU Qiuhong. Numerical investigation of jet interactions and optimization design of drag reduction for the afterbody of jet aircraft[J]. ACTA AERODYNAMICA SINICA, 2014, 32(1): 38-44. DOI: 10.7638/kqdlxxb-2012.0061

Numerical investigation of jet interactions and optimization design of drag reduction for the afterbody of jet aircraft

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  • Received Date: February 20, 2014
  • Revised Date: February 20, 2014
  • Available Online: January 07, 2021
  • The purpose of this paper is to study transonic flow around the axisymmetric afterbody/nozzle of a jet aircraft at zero incidences, and optimization design for the afterbody. The afterbody/nozzle flow is simulated and analyzed by numerical solutions of 2D Reynoldaveraged NavierStokes equations、k-ω-SST turbulence model and species transport equations. The interaction between jet flow and external flow, and influence of afterbody drag between mixture gas and ideal gas, and coupled flowfield of a vehicle at different pressure ratio(NPR) of jet air are investigated. The optimization design of drag reduction for the afterbody shape is carried out by the classical steepestdescent method, and a new algorithm, based on curvature computation of shape, is proposed to improve the optimization efficiency. The number and location of design points are calculated for optimized acceleration by this algorithm. The numerical results show the numerical method can capture the wave structures of jet flow fields, and afterbody drag using ideal gas is higher than that of mixture gas, and the increase of jet pressure ratio brings the reduction of afterbody drag coefficient. Optimization results show that optimized shape brings 13% afterbody drag reduction, compared with original shape. The results also show optimization time reduces of 40%, since the algorithm is combined with steepestdescent method.
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