In order to improve the real-time performance of aero-engine component-level models, an automatic fast positioning interpolation method is proposed. Based on the maximum parameter slope, this method can automatically determine the interpolation cut in point, change the disadvantage of low efficiency of traditional sequential interpolation from the starting point, effectively reduce the interpolation interval, thus greatly improving the efficiency of interpolation. The method is applied to the calculation of gas thermodynamic parameters and the interpolation of the characteristic of rotating parts ,so as to ameliorate the real-time performance of the single-stage flow path calculation of the component-level model. Simulation results show that, compared with the traditional method, the method proposed in this paper improves the fan characteristic calculation efficiency by 47.5%, reduces the time of single complete flow calculation by 74.3% when the dynamic and steady-state accuracy changes are less than 0.4%, which greatly improves the real-time performance of the component-level model.
In order to improve the real-time performance of aero-engine component-level models, an automatic fast positioning interpolation method is proposed. Based on the maximum parameter slope, this method can automatically determine the interpolation cut in point, change the disadvantage of low efficiency of traditional sequential interpolation from the starting point, effectively reduce the interpolation interval, thus greatly improving the efficiency of interpolation. The method is applied to the calculation of gas thermodynamic parameters and the interpolation of the characteristic of rotating parts ,so as to ameliorate the real-time performance of the single-stage flow path calculation of the component-level model. Simulation results show that, compared with the traditional method, the method proposed in this paper improves the fan characteristic calculation efficiency by 47.5%, reduces the time of single complete flow calculation by 74.3% when the dynamic and steady-state accuracy changes are less than 0.4%, which greatly improves the real-time performance of the component-level model.
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