Open AccessDrag Prediction of Nasa Common Research Model Under Stochastic Inflow Conditions
Author(s) -
Xiaojun Wu,
Jiangtao Chen,
Xiaoqian Hu,
Huan Li,
Chao Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1600/1/012058
Subject(s) - inflow , drag , drag coefficient , mach number , reynolds number , drag divergence mach number , mathematics , mechanics , parasitic drag , flow (mathematics) , variable (mathematics) , stochastic modelling , convergence (economics) , physics , statistics , mathematical analysis , turbulence , economics , economic growth
Numerical investigations of NASA Common Research Model under stochastic inflow conditions are analysed in this paper by Non-intrusive Polynomial Chaos (NIPC) method. Reynolds number, Mach number and temperature of inflow conditions are assumed to be independent stochastic variables. It is found that predicted drag mainly depends on Mach number of incoming flow. The contribution of Reynolds number and temperature to the variance of drag is negligible. The mean value of drag shows consistent convergence with grid refinement. The investigation of this paper quantitates the uncertainty induced by stochastic inflow conditions to drag prediction and recognizes the most significant input variable. This will help the validation of numerical methods with experiment.