Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages ha...Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages have difficulties in predicting comprehensively Reynolds number effects on airfoils,matching and characteristics curves.This study proposes Re-correction models for loss,deviation angle and endwall blockage based on classical theories and cascade tests,and loss and deviation models show good agreement in test data of NACA65 and C4 cascades.Throughflow method considering Reynolds number effects is developed by integrating the correction models into a verified Streamline Curvature(SLC)tool.A three-stage axial compressor is investigated through SLC and CFD methods from design Reynolds number(Red=2106)to low Re=4104,and the numerical methods are validated with test data of characteristic curves and spanwise distributions at Red.With Re reduction,SLC method with correction models well predicts variation in overall performances compared with CFD calculations and Wassell's model.Streamwise and spanwise matching such as total pressure and loss distributions in SLC predictions are basically consistent with those in CFD results at near-stall points under design and low Reynolds numbers.SLC and CFD methods share similar detections of stall risks in the third stage(Stg3),and their analyses of diffusion processes deviate to some extent due to different predictions in separated endwall flow.The correction models can be adopted to consider Reynolds number effects in through-flow design and analysis of axial compressors.展开更多
In this study,the capability of deep neural network is verified in the aerodynamic design of supersonic through-flow fan blade profile.A highly flexible parameterization method was first proposed,followed by generatin...In this study,the capability of deep neural network is verified in the aerodynamic design of supersonic through-flow fan blade profile.A highly flexible parameterization method was first proposed,followed by generating a flow field data set using Latin Hypercube Sampling and batch processing.Through flexible deep neural network architectures,positive designs of prediction on performance,static pressure distribution and flow field from design variables,and the inverse design from static pressure distribution to blade profile,were well achieved.The effects of different deep neural network architectures on prediction performance are systematically compared,and the inverse design accuracy is quantitatively evaluated using representative error metrics.Moreover,this study provides comparisons of prediction accuracy among different architectures,sample sizes,physical complexities,and treatment approaches,with detailed assessments for each neural network.It was found that the deep neural network is powerful for establishing the physical relationship between input and output parameters in aerodynamic design,supporting flexible input-output combinations while ensuring a certain level of prediction accuracy.However,caution is advised during the later stage of design,as the prediction error is hard to be totally eliminated.Additionally,regions with drastic changes and boundaries of the design space tend to have larger prediction error.展开更多
基金supported by the National Science and Tech-nology Major Project of China(Nos.2017-II-0007-0021 and J2019-II-0017-0038)。
文摘Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages have difficulties in predicting comprehensively Reynolds number effects on airfoils,matching and characteristics curves.This study proposes Re-correction models for loss,deviation angle and endwall blockage based on classical theories and cascade tests,and loss and deviation models show good agreement in test data of NACA65 and C4 cascades.Throughflow method considering Reynolds number effects is developed by integrating the correction models into a verified Streamline Curvature(SLC)tool.A three-stage axial compressor is investigated through SLC and CFD methods from design Reynolds number(Red=2106)to low Re=4104,and the numerical methods are validated with test data of characteristic curves and spanwise distributions at Red.With Re reduction,SLC method with correction models well predicts variation in overall performances compared with CFD calculations and Wassell's model.Streamwise and spanwise matching such as total pressure and loss distributions in SLC predictions are basically consistent with those in CFD results at near-stall points under design and low Reynolds numbers.SLC and CFD methods share similar detections of stall risks in the third stage(Stg3),and their analyses of diffusion processes deviate to some extent due to different predictions in separated endwall flow.The correction models can be adopted to consider Reynolds number effects in through-flow design and analysis of axial compressors.
文摘In this study,the capability of deep neural network is verified in the aerodynamic design of supersonic through-flow fan blade profile.A highly flexible parameterization method was first proposed,followed by generating a flow field data set using Latin Hypercube Sampling and batch processing.Through flexible deep neural network architectures,positive designs of prediction on performance,static pressure distribution and flow field from design variables,and the inverse design from static pressure distribution to blade profile,were well achieved.The effects of different deep neural network architectures on prediction performance are systematically compared,and the inverse design accuracy is quantitatively evaluated using representative error metrics.Moreover,this study provides comparisons of prediction accuracy among different architectures,sample sizes,physical complexities,and treatment approaches,with detailed assessments for each neural network.It was found that the deep neural network is powerful for establishing the physical relationship between input and output parameters in aerodynamic design,supporting flexible input-output combinations while ensuring a certain level of prediction accuracy.However,caution is advised during the later stage of design,as the prediction error is hard to be totally eliminated.Additionally,regions with drastic changes and boundaries of the design space tend to have larger prediction error.
