In this paper,a coupled CFD-CSD method based on N-S equations is described for static aeroelastic correction and jig-shape design of large airliners.The wing structural flexibility matrix is analyzed by a finite eleme...In this paper,a coupled CFD-CSD method based on N-S equations is described for static aeroelastic correction and jig-shape design of large airliners.The wing structural flexibility matrix is analyzed by a finite element method with a double-beam model.The viscous multi-block structured grid is used in aerodynamic calculations.Flexibility matrix interpolation is fulfilled by use of a surface spline method.The load distributions on wing surface are evaluated by solving N-S equations with a parallel algorithm.A flexibility approach is employed to calculate the structural deformations.By successive iterations between steady aerodynamic forces and structural deformations,a coupled CFD-CSD method is achieved for the static aeroelastic correction and jig-shape design of a large airliner.The present method is applied to the static aeroelastic analysis and jig-shape design for a typical large airliner with engine nacelle and winglet.The numerical results indicate that calculations of static aeroelastic correction should employ tightly coupled CFD-CSD iterations,and that on a given cruise shape only one round of iterative design is needed to obtain the jig-shape meeting design requirements.展开更多
Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation(ENSO),the current models are insufficient to simulate diverse characteristics of the ENSO,which depends on the calen...Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation(ENSO),the current models are insufficient to simulate diverse characteristics of the ENSO,which depends on the calendar season.Consequently,a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times,thereby leading to arbitrary fluctuations in the predicted time series.To overcome this problem and account for ENSO seasonality,we developed an all-season convolutional neural network(A_CNN)model.The correlation skill of the ENSO index was particularly improved for forecasts of the boreal spring,which is the most challenging season to predict.Moreover,activation map values indicated a clear time evolution with increasing forecast lead time.The study findings reveal the comprehensive role of various climate precursors of ENSO events that act differently over time,thus indicating the potential of the A_CNN model as a diagnostic tool.展开更多
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘In this paper,a coupled CFD-CSD method based on N-S equations is described for static aeroelastic correction and jig-shape design of large airliners.The wing structural flexibility matrix is analyzed by a finite element method with a double-beam model.The viscous multi-block structured grid is used in aerodynamic calculations.Flexibility matrix interpolation is fulfilled by use of a surface spline method.The load distributions on wing surface are evaluated by solving N-S equations with a parallel algorithm.A flexibility approach is employed to calculate the structural deformations.By successive iterations between steady aerodynamic forces and structural deformations,a coupled CFD-CSD method is achieved for the static aeroelastic correction and jig-shape design of a large airliner.The present method is applied to the static aeroelastic analysis and jig-shape design for a typical large airliner with engine nacelle and winglet.The numerical results indicate that calculations of static aeroelastic correction should employ tightly coupled CFD-CSD iterations,and that on a given cruise shape only one round of iterative design is needed to obtain the jig-shape meeting design requirements.
基金This work was supported by the National Research Foundation of Korea(NRF)(NRF-2020R1A2C2101025).
文摘Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation(ENSO),the current models are insufficient to simulate diverse characteristics of the ENSO,which depends on the calendar season.Consequently,a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times,thereby leading to arbitrary fluctuations in the predicted time series.To overcome this problem and account for ENSO seasonality,we developed an all-season convolutional neural network(A_CNN)model.The correlation skill of the ENSO index was particularly improved for forecasts of the boreal spring,which is the most challenging season to predict.Moreover,activation map values indicated a clear time evolution with increasing forecast lead time.The study findings reveal the comprehensive role of various climate precursors of ENSO events that act differently over time,thus indicating the potential of the A_CNN model as a diagnostic tool.
