A Robust Aerodynamic Optimal Design Method for Airfoil Considering Geometric Uncertainty

Abstract

The invention provides an airfoil robust aerodynamic optimization design method considering geometric uncertain factors, including: deterministically optimizing the reference airfoil to obtain a deterministic optimized airfoil; selecting a robust optimization design space; using a sample set to establish a Kriging agent Model group; use the robust point addition method to find the robust optimal point on the Kriging proxy model group and improve the precision sample points used to improve the accuracy of the Kriging proxy model group; update the sample set; if the termination condition is not met, use the updated sample set to rebuild The Kriging agent model group is optimized and solved until the robust optimization termination condition is met. The robust optimized airfoil designed by the invention has good aerodynamic performance, is insensitive to geometrical uncertain factors such as machining errors, has high robustness, and has stronger engineering practicability. The invention guarantees optimal quality while reducing computational cost.

Description

technical field [0001] The invention belongs to the technical field of airfoil design, and in particular relates to an airfoil robust aerodynamic optimization design method considering geometric uncertain factors. Background technique [0002] The existing traditional aerodynamic optimization design method generally only optimizes the design for some ideal states of the aircraft during use, and does not fully consider the various uncertain factors faced in the actual situation. It is a deterministic optimal design method. . In fact, in the manufacturing stage of the aircraft, there will be geometric uncertainties such as machining errors, which lead to slight fluctuations in the actual geometric shape of the airfoil. These objectively existing geometric uncertainties are not only difficult to eliminate, but also lead to insufficient robustness of the optimal design of the airfoil during use, making it difficult to exert the ideal optimal aerodynamic performance, and even ac...

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Problems solved by technology

With the increase of the number of uncertain variables, the number of calls to CFD analysis for uncertainty quantification increases sharply, and the resulting large-scale calculation will lead to a significant decrease in the efficiency of optimal design.

In the presence of geometric uncertainties, in order to accurately describe the small fluctuations of the airfoil shape, there are many uncertain variables, so these traditional methods are difficult to apply to fine design problems with many uncertain variables (more than 15)

[0004] In order to reduce the calculation amount of CFD high-confidence analysis, some methods use the prediction value of proxy model instead of CFD calculation in uncertainty quantification, but due to the lack of corresponding proxy model update mechanism, the accuracy of prediction value is difficult to guarantee, and it is also not applicable Due to the refined design with many uncertain variables

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