To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the ...To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the uncertainty analysis. The surrogate model is constructed by using the Latin Hypercube design and the Kriging model. The random parameters are used to account for the small manufacturing errors and the variations of operating conditions. Based on the surrogate model, an uncertainty analysis approach, called the Monte Carlo simulation, is used to compute the mean value and the variance of the predicated performance. The robust optimization for aerodynamic design is formulated, and solved by the genetic algorithm. And then, an airfoil optimization problem is used to test the proposed procedure. Results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties. And the design constraints are still satisfied under the uncertainties.展开更多
Model uncertainty directly affects the accuracy of robust flutter and limit-cycle-oscillation (LCO) analysis. Using a data-based method, the bounds of an uncertain block-oriented aeroelastic system with nonlinearity a...Model uncertainty directly affects the accuracy of robust flutter and limit-cycle-oscillation (LCO) analysis. Using a data-based method, the bounds of an uncertain block-oriented aeroelastic system with nonlinearity are obtained in the time domain. Then robust LCO analysis of the identified model set is performed. First, the proper orthonormal basis is constructed based on the on-line dynamic poles of the aeroelastic system. Accordingly, the identification problem of uncertain model is converted to a nonlinear optimization of the upper and lower bounds for uncertain parameters estimation. By replacing the identified memoryless nonlinear operators by its related sinusoidal-input describing function, the Linear Fractional Transformation (LFT) technique is applied to the modeling process. Finally, the structured singular value(μ) method is applied to robust LCO analysis. An example of a two-degree wing section is carded out to validate the framework above. Results indicate that the dynamic characteristics and model uncertainties of the aeroelastic system can be depicted by the identified uncertain model set. The robust LCO magnitude of pitch angle for the identified uncertain model is lower than that of the nominal model at the same velocity. This method can be applied to robust flutter and LCO prediction.展开更多
In recent decades, there have been a number of debates on climate warming and its driving forces. Based on an extensive literature review, we suggest that （1） climate warming occurs with great uncertainty in the mag...In recent decades, there have been a number of debates on climate warming and its driving forces. Based on an extensive literature review, we suggest that （1） climate warming occurs with great uncertainty in the magnitude of the temperature increase; （2） both human activities and natural forces contribute to climate change, but their relative contributions are difficult to quan- tify; and （3） the dominant role of the increase in the atmospheric concentration of greenhouse gases （including CO2） in the global warming claimed by the Intergovernrnental Panel on Climate Change （IPCC） is questioned by the scientific communities because of large uncertainties in the mechanisms of natural factors and anthropogenic activities and in the sources of the increased atmospheric CO2 concentration. More efforts should be made in order to clarify these uncertainties.展开更多
文摘To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the uncertainty analysis. The surrogate model is constructed by using the Latin Hypercube design and the Kriging model. The random parameters are used to account for the small manufacturing errors and the variations of operating conditions. Based on the surrogate model, an uncertainty analysis approach, called the Monte Carlo simulation, is used to compute the mean value and the variance of the predicated performance. The robust optimization for aerodynamic design is formulated, and solved by the genetic algorithm. And then, an airfoil optimization problem is used to test the proposed procedure. Results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties. And the design constraints are still satisfied under the uncertainties.
基金supported by the National Natural Science Foundation of China (Grant Nos. 90716006 and 10902006)Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20091102110015)the Innovation Foundation of BUAA for PhD Graduates
文摘Model uncertainty directly affects the accuracy of robust flutter and limit-cycle-oscillation (LCO) analysis. Using a data-based method, the bounds of an uncertain block-oriented aeroelastic system with nonlinearity are obtained in the time domain. Then robust LCO analysis of the identified model set is performed. First, the proper orthonormal basis is constructed based on the on-line dynamic poles of the aeroelastic system. Accordingly, the identification problem of uncertain model is converted to a nonlinear optimization of the upper and lower bounds for uncertain parameters estimation. By replacing the identified memoryless nonlinear operators by its related sinusoidal-input describing function, the Linear Fractional Transformation (LFT) technique is applied to the modeling process. Finally, the structured singular value(μ) method is applied to robust LCO analysis. An example of a two-degree wing section is carded out to validate the framework above. Results indicate that the dynamic characteristics and model uncertainties of the aeroelastic system can be depicted by the identified uncertain model set. The robust LCO magnitude of pitch angle for the identified uncertain model is lower than that of the nominal model at the same velocity. This method can be applied to robust flutter and LCO prediction.
基金supported by the Academic Division of the Chinese Academy of Sciencesthe National Natural Science Foundation of China (Grant No. 31021001)the National Basic Research Program of China (Grant No. 2010CB950600)
文摘In recent decades, there have been a number of debates on climate warming and its driving forces. Based on an extensive literature review, we suggest that （1） climate warming occurs with great uncertainty in the magnitude of the temperature increase; （2） both human activities and natural forces contribute to climate change, but their relative contributions are difficult to quan- tify; and （3） the dominant role of the increase in the atmospheric concentration of greenhouse gases （including CO2） in the global warming claimed by the Intergovernrnental Panel on Climate Change （IPCC） is questioned by the scientific communities because of large uncertainties in the mechanisms of natural factors and anthropogenic activities and in the sources of the increased atmospheric CO2 concentration. More efforts should be made in order to clarify these uncertainties.
